Gyre Research publications
How markets and the economy move together: which relations changed, which lead, and which broke from their own history.
Each issue can be read online or downloaded as a PDF; a corrected issue keeps every earlier version.
394 documents on quantitative finance, risk, machine learning and applied mathematics, each with a short summary written by Gyre Research. Browse by subject, or search the collection above. Every paper our own research cites is here; where its licence does not let us host the PDF, the entry carries our summary and a Publisher link to the publisher or DOI page instead.
Donna Bartlett, Willem-Jan Cosemans, Matt Saunders, Till Contzen, Klaus Gresbrand, Maria-Alexandra Papoutsi, Pietro Boccaccini, Peggy Anstett, Bruce Braude, Richard Reeve-Young · Report
The book explores the legal challenges and considerations surrounding the use of generative AI technologies. It examines issues such as intellectual property rights, data privacy, liability, and regulatory frameworks. The authors provide insights into how these technologies impact various industries and offer guidance on navigating the evolving legal landscape to ensure compliance and mitigate risks.
AI · Machine Learning · Math
Nichola Lacey · Book
"Python By Example" by Nichola Lacey provides a practical approach to learning Python programming through hands-on examples. It guides readers from basic concepts to more advanced topics, using real-world scenarios to illustrate how Python can be applied effectively. The book is designed to help beginners understand and implement Python code by working through a series of projects and exercises.
Python · Data Visualization · Machine Learning
Chloe Annable · Book
"Python Mastery" by Chloe Annable is a comprehensive guide designed to elevate readers from beginner to expert in Python programming. It covers fundamental concepts, advanced techniques, and practical applications, providing hands-on exercises and real-world examples to ensure mastery of the language.
Python · Math · Data Visualization
Arun Arunisto · Notes
"Object Oriented Programming" by Arun Arunisto provides a comprehensive overview of the principles and practices of object-oriented programming. It covers key concepts such as classes, objects, inheritance, polymorphism, and encapsulation, offering practical examples and exercises to help readers understand and apply these concepts in real-world programming scenarios. The book is designed for both beginners and experienced programmers looking to deepen their understanding of object-oriented design and implementation.
Computer Science · Math · Software Engineering
McKinsey & Company · Report
The book "What is Fintech?" by McKinsey & Company explores the rapidly evolving financial technology sector, examining its impact on traditional banking and financial services. It delves into key innovations such as digital payments, blockchain, and AI-driven financial solutions, highlighting how these technologies are reshaping consumer experiences and business models. The book also discusses regulatory challenges and the future landscape of fintech, providing insights into how companies can adapt and thrive in this dynamic environment.
Corporate Finance · Finance · Quant Finance
Ken Abbott · Book
The book "Lecture 7: Value At Risk (VAR) Models" by Ken Abbott provides an overview of Value at Risk (VAR) models, which are used to assess the potential loss in value of a portfolio over a defined period for a given confidence interval. It covers the theoretical foundations, methodologies, and practical applications of VAR, including historical simulation, variance-covariance, and Monte Carlo simulation approaches. The lecture also discusses the limitations and challenges of VAR models in risk management.
Risk Management · Quant Finance · Finance
Thomson Reuters Institute · Report
The book "The 2023 State of Corporate ESG" by Thomson Reuters Institute provides an analysis of the current trends, challenges, and opportunities in corporate Environmental, Social, and Governance (ESG) practices. It examines how companies are integrating ESG factors into their strategies, the impact of regulatory changes, and the role of stakeholders in driving ESG initiatives. The book also highlights case studies and best practices from leading organizations to offer insights into effective ESG implementation.
Corporate Finance · Risk Management · Finance
Andriy Burkov · Book
"The Hundred-Page Machine Learning Book" by Andriy Burkov provides a concise overview of machine learning concepts, techniques, and algorithms. It covers fundamental topics such as supervised and unsupervised learning, model evaluation, and neural networks, offering practical insights and examples to help readers understand and apply machine learning in real-world scenarios.
Machine Learning · Math · Computer Science
Flavio Copes · Book
"The Python Handbook" by Flavio Copes is a comprehensive guide for beginners and experienced programmers alike, covering Python programming fundamentals, data structures, functions, modules, and libraries. It provides practical examples and exercises to help readers understand and apply Python concepts effectively.
Python · Computer Science · Math
Allen B. Downey · Book
"Think Stats" by Allen B. Downey is a practical guide to exploring and analyzing real-world data using Python. It introduces statistical concepts and techniques through hands-on exercises, focusing on understanding data distributions, probability, and statistical inference. The book emphasizes computational thinking and encourages readers to apply statistical methods to solve problems and make data-driven decisions.
Statistics · Python · Data Visualization
Dylan Penny · Book
"Python Programming" by Dylan Penny is a comprehensive guide that introduces readers to the fundamentals of Python, covering essential concepts such as data types, control structures, functions, and modules. The book progresses to more advanced topics like object-oriented programming, file handling, and libraries, providing practical examples and exercises to reinforce learning. It is designed for both beginners and those looking to enhance their Python skills for real-world applications.
Python · Computer Science · Math
Hayden Van Der Post · Book
"Mastering Pandas: Advanced Pandas For Finance" by Hayden Van Der Post delves into sophisticated techniques for using the Pandas library in financial data analysis. It covers advanced data manipulation, time series analysis, and financial modeling, providing practical examples and strategies to enhance data-driven decision-making in finance.
Python · Quant Finance · Finance
Carlos Andre Reis Pinheiro & Mike Patetta · Book
"Introduction to Statistical and Machine Learning Methods for Data Science" by Carlos Andre Reis Pinheiro & Mike Patetta provides a comprehensive guide to the foundational techniques used in modern data science. The book bridges the gap between traditional statistical approaches and contemporary machine learning methods, offering readers a well-rounded understanding of data-driven decision-making. Key Topics Covered: Fundamentals of Statistical Methods: Covers core concepts such as probability distributions, hypothesis testing, regression analysis, and statistical inference. Machine Learning Techniques: Introduces supervised and unsupervised learning algorithms, including decision trees, random forests, support vector machines (SVMs), and neural networks. Feature Engineering & Data Preprocessing: Explores methods to clean, transform, and optimize data for machine learning models. Model Evaluation & Validation: Discusses key metrics like accuracy, precision, recall, and ROC curves to assess model performance. Real-World Applications: Demonstrates how statistical and machine learning methods are applied in finance, healthcare, and business analytics. This book is designed for data scientists, analysts, and professionals looking to enhance their knowledge of both classical statistical methods and modern machine learning techniques. With practical examples and code implementations, it serves as an essential resource for anyone aiming to leverage data science in their field.
Machine Learning · Statistics · Math
Marc Peter Deisenroth, A. Aldo Faisal & Cheng Soon Ong · Book
"Mathematics for Machine Learning" is a comprehensive guide that provides the mathematical foundation necessary for understanding and developing machine learning models. The book is designed for students, engineers, and researchers who want to strengthen their mathematical skills to effectively engage with modern machine learning techniques. Key Topics Covered: Linear Algebra: Covers essential concepts such as vectors, matrices, eigenvalues, and singular value decomposition (SVD), which are crucial for algorithms like PCA and deep learning. Analytical Geometry: Explores coordinate transformations, basis changes, and geometric interpretations of machine learning problems. Matrix Decompositions: Discusses LU, QR, and Cholesky decompositions and their applications in optimization and numerical computations. Vector Calculus: Introduces gradients, Hessians, and Jacobians, which are fundamental for optimization and backpropagation in neural networks. Probability and Statistics: Provides an overview of probability distributions, Bayes’ theorem, and information theory concepts used in probabilistic machine learning models. Optimization Techniques: Explains gradient descent, convex optimization, and constrained optimization, which are essential for training machine learning models. The book balances theory with practical applications, making complex mathematical concepts more accessible through intuitive explanations and visualizations. By focusing on the mathematical principles behind machine learning, it equips readers with the tools needed to develop, analyze, and optimize machine learning algorithms effectively.
Math · Machine Learning · Quant Finance
ALGORITHMIC EXECUTION LLC. · Guide
"MATH-TWS" is a technical guide that introduces a package designed to integrate Mathematica with Interactive Brokers Trader Workstation (TWS), enabling traders and quantitative analysts to develop and execute trading strategies within Mathematica’s powerful computational environment. The book provides insights into the seamless connection between symbolic computation, real-time market data, and algorithmic trading execution. Key Topics Covered: Introduction to Mathematica and Interactive Brokers (IB) API: Explains the fundamentals of using Mathematica for financial modeling and how it can be integrated with IB’s TWS platform. Setting Up MATH-TWS: Guides users through the installation and configuration process to establish a real-time connection between Mathematica and IB’s trading system. Market Data Retrieval: Demonstrates how to fetch live market data, historical data, and financial indicators directly within Mathematica for analysis. Algorithmic Trading Execution: Covers order placement, trade execution, and automated strategy implementation using Mathematica’s computational tools. Risk Management & Portfolio Optimization: Introduces methods to manage trading risk, optimize portfolios, and analyze performance metrics using statistical and machine learning techniques. Customizing & Extending MATH-TWS: Discusses how users can modify and extend the package to fit their specific trading needs. This book is an essential resource for quantitative traders, algorithmic developers, and financial engineers who want to leverage Mathematica’s computational power alongside Interactive Brokers’ API for enhanced trading capabilities. It provides practical coding examples, real-world applications, and a step-by-step approach to building automated trading systems efficiently.
Math · Quant Finance · Data Visualization
Bogdan Bilaus & Luis Garcia-Feijòo · Book
"Middle East Capital Markets: Challenges and Opportunities" provides an in-depth analysis of the evolving financial markets in the Middle East, focusing on the economic, regulatory, and structural factors shaping investment opportunities in the region. The book examines key trends, challenges, and growth prospects in both equity and fixed-income markets, offering valuable insights for investors, policymakers, and financial professionals. Key Topics Covered: Historical Development of Capital Markets in the Middle East: Overview of the region’s financial systems, including the rise of stock exchanges, sovereign wealth funds, and key financial institutions. Regulatory & Governance Challenges: Analysis of market regulations, corporate governance practices, and compliance requirements that impact investor confidence. Equity & Debt Markets: Exploration of stock exchanges, bond markets, and alternative investment vehicles such as sukuk (Islamic bonds). Foreign Investment & Market Accessibility: Discussion on foreign direct investment (FDI), capital flow restrictions, and the role of financial liberalization in attracting global investors. Geopolitical & Economic Risks: Examination of oil price fluctuations, political instability, and macroeconomic factors affecting capital market growth. Future Opportunities & Financial Innovation: Insights into fintech adoption, ESG (Environmental, Social, and Governance) investing, and the potential for sustainable economic expansion in the region. This book serves as an essential guide for investors, financial analysts, and policymakers seeking to navigate the complexities of Middle Eastern capital markets. By addressing both risks and opportunities, it provides a comprehensive framework for understanding and capitalizing on the region’s financial landscape.
Finance · Corporate Finance · Quant Finance
Prashanta Kumar Patra · Book
"Lecture Notes on Artificial Intelligence" is a structured and concise guide designed to introduce the fundamental concepts of Artificial Intelligence (AI). The book is tailored for students, researchers, and professionals looking to understand the theoretical foundations and practical applications of AI. It provides a systematic overview of AI methodologies, techniques, and algorithms, making it an essential resource for academic learning and real-world problem-solving. Key Topics Covered: Introduction to Artificial Intelligence: Definition, history, and evolution of AI, along with its impact on various industries. Problem Solving & Search Algorithms: Covers uninformed search (BFS, DFS), heuristic search (A), and optimization techniques*. Knowledge Representation & Reasoning: Explores logical reasoning, semantic networks, ontologies, and rule-based systems. Machine Learning Fundamentals: Introduces supervised, unsupervised, and reinforcement learning, along with key algorithms like decision trees and neural networks. Natural Language Processing (NLP): Discusses text processing, sentiment analysis, and AI-driven language models. Expert Systems & Fuzzy Logic: Explains the design and implementation of expert systems and how fuzzy logic enhances decision-making. Neural Networks & Deep Learning: Covers perceptrons, backpropagation, convolutional neural networks (CNNs), and recurrent neural networks (RNNs). AI Ethics & Future Trends: Discusses ethical considerations, AI governance, and the future impact of AI on society. With clear explanations, mathematical foundations, and practical examples, this book serves as a valuable resource for understanding AI principles and preparing for advanced studies or careers in artificial intelligence.
AI · Machine Learning · Computer Science
Pat Hagan & Graeme West · Report
"Interpolation Methods for Curve Construction" is a specialized mathematical and financial guide that explores various interpolation techniques used in curve construction. The book is particularly relevant for quantitative analysts, financial engineers, and mathematicians working in areas such as yield curve modeling, interest rate derivatives, and risk management. It provides a deep understanding of interpolation methods, their properties, and their impact on numerical stability and financial applications. Key Topics Covered: Fundamentals of Interpolation: Introduction to interpolation concepts, including polynomial, piecewise, and spline interpolation. Linear and Polynomial Interpolation: Discusses basic techniques such as linear interpolation and Lagrange polynomials, with applications in finance. Spline Interpolation Methods: Covers cubic splines, B-splines, and natural splines, explaining how they ensure smooth and stable curve construction. Monotonicity and Shape-Preserving Interpolation: Examines methods that prevent oscillations and ensure realistic financial curve modeling. Hermite and Rational Interpolation: Explores advanced techniques that improve curve smoothness and numerical stability. Applications in Finance: Discusses how interpolation methods are used for yield curves, discount factors, option pricing, and bootstrapping interest rate curves. Error Analysis and Stability Considerations: Evaluates the numerical accuracy, computational efficiency, and robustness of different interpolation techniques. This book serves as an essential reference for professionals and researchers who need to construct smooth, reliable curves for financial modeling. With a balance of theory, practical implementation, and real-world financial applications, it provides valuable insights into interpolation techniques critical for modern quantitative finance.
Quant Finance · Math · Finance
Alfonso Ricciardelli, Philip Clements, Trevor Castledine, Kathryn Saklatvala,Thibault Sandret, Stephan Connelly, David Preston, Nils Hertzner, Nikita Saygakov, Dave Skirzenski, Adil Hasan, Nick Cleary, Zack Ellison, Mike Dowdall · Book
"An Introduction to Alternative Credit" is a comprehensive guide to the growing field of alternative credit investments, a crucial segment of modern financial markets. The book explores various forms of non-traditional lending, including private debt, structured credit, and direct lending, providing valuable insights for institutional investors, asset managers, and financial professionals. Key Topics Covered: Understanding Alternative Credit: An overview of alternative credit markets, their evolution, and their role in the financial system. Private Debt & Direct Lending: Examines the rise of private credit as an alternative to traditional bank loans, focusing on middle-market lending, mezzanine debt, and unitranche financing. Structured Credit & Securitization: Explains structured financial instruments such as collateralized loan obligations (CLOs), asset-backed securities (ABS), and mortgage-backed securities (MBS). Distressed Debt & Special Situations: Covers strategies for investing in distressed companies, non-performing loans (NPLs), and turnaround opportunities. Risk & Return Characteristics: Analyzes the risk-reward trade-offs in alternative credit, comparing it to traditional fixed-income investments. Market Trends & Regulatory Landscape: Discusses how regulatory changes, macroeconomic factors, and interest rate environments affect alternative credit strategies. Institutional Investor Perspectives: Provides insights into how pension funds, insurance companies, and endowments integrate alternative credit into their portfolios. Future of Alternative Credit: Examines emerging trends, including fintech-driven lending, ESG (Environmental, Social, and Governance) considerations, and global market expansion. This book serves as an essential introduction to the alternative credit landscape, offering a blend of theory, market analysis, and practical applications. It is a valuable resource for those looking to understand the role of private and structured credit in modern investment portfolios.
Finance · Corporate Finance · Quant Finance
Andrew Lesniewski · Slides
"Interest Rate and Credit Models" is a comprehensive guide to the mathematical and financial theories behind interest rate modeling and credit risk assessment. The book delves into the stochastic processes, pricing methodologies, and risk management techniques used in modern fixed-income and credit markets. It is aimed at quantitative analysts, risk managers, financial engineers, and academics looking for a deep understanding of how interest rate and credit models function in practice. Key Topics Covered: Fundamentals of Interest Rate Models: Introduction to yield curves, discount factors, and the evolution of interest rate dynamics. Short Rate Models: Covers Vasicek, Cox-Ingersoll-Ross (CIR), and Hull-White models, explaining their applications in bond pricing and risk management. Market Models & Libor-Based Approaches: Discusses Libor Market Models (LMM), forward rate agreements, and swap pricing. Affine Term Structure Models: Explores the Heath-Jarrow-Morton (HJM) framework and its role in modeling yield curve movements. Credit Risk & Default Models: Examines structural and reduced-form models for credit spreads, default probabilities, and counterparty risk. Credit Derivatives & Securitization: Discusses credit default swaps (CDS), collateralized debt obligations (CDOs), and risk transfer mechanisms. Calibration & Numerical Methods: Covers Monte Carlo simulations, finite difference methods, and optimization techniques for model calibration. Risk Management & Practical Applications: Provides real-world insights into stress testing, risk-neutral pricing, and hedging strategies. This book serves as an advanced reference for professionals and researchers seeking a rigorous yet practical exploration of interest rate and credit risk modeling, offering a blend of mathematical depth and financial intuition.
Quant Finance · Finance · Risk Management
Ray Perrault & Jack Clark · Book
The "Artificial Intelligence Index Report 2024" is the seventh edition of an annual comprehensive analysis that tracks, collates, distills, and visualizes data related to artificial intelligence (AI). Produced by the Stanford Institute for Human-Centered Artificial Intelligence (HAI), this report aims to provide unbiased, rigorously vetted, and broadly sourced data to inform policymakers, researchers, executives, journalists, and the general public about the complex field of AI. Key Highlights: Expansion of Scope: This edition broadens its coverage to include essential trends such as technical advancements in AI, public perceptions of the technology, and the geopolitical dynamics surrounding its development. Original Data and New Analyses: Featuring more original data than previous editions, the report introduces new estimates on AI training costs, detailed analyses of the responsible AI landscape, and an entirely new chapter dedicated to AI's impact on science and medicine. Generative AI Investment Surge: Despite a decline in overall AI private investment in 2023, funding for generative AI surged, nearly octupling from 2022 to reach $25.2 billion. Major players in the generative AI space, including OpenAI, Anthropic, Hugging Face, and Inflection, reported substantial fundraising rounds. U.S. Leadership in AI Investment: The United States saw AI investments reach $67.2 billion in 2023, nearly 8.7 times more than China, the next highest country. Impact on Science and Medicine: An entirely new chapter is dedicated to AI's impact on science and medicine, reflecting the growing influence of AI technologies in these critical fields. This report serves as an essential resource for understanding the current state and future trajectory of AI, offering a blend of data-driven insights and comprehensive analyses across various dimensions of artificial intelligence.
AI · Machine Learning · Statistics
Scott Chacon · Book
"Pro Git" is the definitive guide to Git, the powerful and widely used version control system. Written by Scott Chacon, one of the early contributors to Git, the book provides a comprehensive, hands-on approach to learning Git, from basic concepts to advanced workflows. It is an essential resource for developers, software engineers, and teams looking to improve their version control practices. Key Topics Covered: 1. Introduction to Git: The history of Git and why it was created. Understanding distributed version control and how Git differs from other systems. 2. Git Basics: Installing and setting up Git on different platforms. Creating repositories, cloning, committing, and working with branches. Basic Git commands like git add, git commit, git status, and git log. 3. Branching and Merging: The power of branching in Git and best practices for feature branches. Merging strategies: fast-forward merges, three-way merges, and conflict resolution. 4. Working with Remote Repositories: Setting up and using GitHub, GitLab, and Bitbucket for collaboration. Pushing, pulling, and synchronizing changes with git push and git pull. 5. Git Advanced Features: Rebasing vs. merging: when to use each approach. Interactive rebasing (git rebase -i) for cleaning up commit history. Stashing changes with git stash for temporary work storage. Using git bisect for debugging with binary search. 6. Git Internals: Understanding how Git stores data using objects, trees, and commits. Exploring the .git directory structure. How Git compresses and optimizes repository storage. 7. Collaborative Workflows: Best practices for working in a team using Git. Forking, pull requests, and code reviews. Using Git in continuous integration (CI) and DevOps pipelines. 8. Customizing Git & Automation: Writing custom Git hooks for automating workflows. Configuring .gitconfig to personalize Git behavior. Integrating Git with other tools and platforms. With clear explanations, real-world examples, and hands-on exercises, "Pro Git" is the ultimate guide to mastering Git for version control and software development.
Software Engineering · Computer Science · Math
Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, Jonathan Taylor · Book
"An Introduction to Statistical Learning with Applications in Python" is an essential guide to statistical and machine learning methods, providing a clear and accessible introduction to the field. Designed for students, researchers, and practitioners, this book offers a balance between theory and practical implementation, focusing on Python-based applications for data analysis and predictive modeling. Key Highlights: Comprehensive Coverage of Statistical Learning: The book explains core machine learning principles, including supervised and unsupervised learning, model evaluation, and statistical inference. Regression and Classification Techniques: Topics include linear regression, logistic regression, decision trees, and support vector machines (SVMs), providing practical guidance on applying these models effectively. Resampling Methods and Model Selection: Introduces cross-validation and bootstrap techniques, helping readers understand how to improve model performance and avoid overfitting. Tree-Based and Ensemble Methods: Covers random forests, gradient boosting, and bagging, which are widely used in modern machine learning applications. Unsupervised Learning Techniques: Introduces principal component analysis (PCA), k-means clustering, and hierarchical clustering, essential for working with high-dimensional data. Python-Based Implementation: The book provides hands-on coding examples using scikit-learn, NumPy, pandas, and Matplotlib, enabling readers to apply theoretical concepts in real-world scenarios. This book serves as a valuable resource for those looking to build a strong foundation in statistical learning, blending intuitive explanations, practical examples, and Python-based applications to make complex concepts accessible to a broad audience.
Python · Machine Learning · Statistics
Code Quickly · Book
"Learn SQL Quickly: A Beginner’s Guide to Learning SQL, Even If You’re New to Databases" is a comprehensive introduction to SQL designed for readers without prior coding experience. Authored by Code Quickly, the book aims to demystify SQL and equip readers with practical skills for managing and manipulating databases. Key Features: Clear and Concise Explanations: The book offers straightforward examples to help readers grasp essential SQL concepts, ensuring a solid foundational understanding. Foundational Knowledge: It provides an in-depth breakdown of what SQL is, making complex topics accessible to beginners. Database Management Setup: Readers are guided through setting up their database management systems, emphasizing a methodical approach to ensure a strong grasp of the basics. Efficient Data Handling: The book teaches how to control data movement effectively, optimizing workflows and minimizing potential issues. Real-World Applications: Incorporating practical examples, the book demonstrates how SQL skills can be applied in real-life scenarios, enhancing learning and retention. Focus on MySQL: It discusses the advantages of using MySQL over other database systems and provides guidance on getting started with it. In today's data-driven world, proficiency in SQL is increasingly valuable. "Learn SQL Quickly" serves as a practical resource for those eager to acquire this in-demand skill efficiently.
SQL · Data Visualization · Machine Learning
D Armstrong · Book
"Learn SQL Fast: SQL Made Simple! A Beginner's Guide to SQL, with Practical Exercises for Microsoft SQL Server" by D. Armstrong is a comprehensive resource designed to take readers from beginner to proficient in SQL. The book provides a step-by-step approach to learning SQL, focusing on practical application and hands-on exercises. Key Features: Guided Setup: The book begins by assisting readers in acquiring and setting up free SQL Server software and a sample database directly from Microsoft, ensuring a solid foundation for practice. Incremental Learning: SQL concepts are introduced in manageable segments, with each new topic accompanied by clear explanations and examples. This methodical approach facilitates a deeper understanding of each concept before progressing. Hands-On Exercises: At each stage, the book provides practice exercises, allowing readers to apply what they've learned and reinforce their skills through practical application. Progressive Complexity: As the course advances, examples and exercises become more complex, demonstrating how to combine basic concepts to write advanced queries. This progression ensures that readers build upon their knowledge systematically. This book is tailored for individuals who aim to learn SQL efficiently and effectively, providing both the practical skills and the underlying principles necessary for proficient database management.
SQL · Machine Learning · Data Visualization
SigTech · Slides
The "Hedge Fund Research Report 2021" by SigTech offers comprehensive insights into the evolving landscape of the hedge fund industry, focusing on quantitative strategies and asset allocation trends. The report is based on a survey of over 100 leading hedge fund managers, collectively overseeing assets under management (AUM) totaling approximately $231 billion across European, Asian, and North American markets. Key Findings: Increased Allocation to Quant Strategies: A significant 80% of hedge fund managers anticipate that institutional investors will boost their allocations to quantitative strategies within the next twelve months. Favorable Economic Conditions: Approximately 73% of respondents believe that the current economic and fiscal environment is conducive to the success of quantitative strategies. Growth in Quant Hedge Funds: An overwhelming 86% of managers expect an increase in the number of quantitative hedge funds over the next five years, indicating a strong trend toward systematic investment approaches. Data and Technology as Alpha Drivers: A substantial 95% of surveyed managers assert that access to high-quality data and cutting-edge technology is crucial for generating alpha, underscoring the industry's shift toward data-driven decision-making. Rise in Digital Asset Trading: About 85% of hedge fund managers anticipate an increase in trading activities involving digital assets, reflecting the growing acceptance and integration of cryptocurrencies and related instruments into investment portfolios. This report highlights a clear industry trend toward the adoption and expansion of quantitative investment strategies, driven by advancements in data accessibility and technological innovation. Hedge fund managers are optimistic about the future, recognizing the importance of leveraging these tools to enhance investment performance and meet the evolving demands of institutional investors.
Quant Finance · Finance · Risk Management
Arthur L. Zwickel & Alicia M. Harrison. · Report
"SEC Reporting Obligations Under Section 13 and Section 16 of the Exchange Act" by Arthur L. Zwickel and Alicia M. Harrison provides a comprehensive overview of the reporting requirements mandated by the Securities Exchange Act of 1934. This legal update is essential for individuals and entities involved in owning, managing, or trading publicly traded or exchange-listed equity securities. Key Highlights: Section 13 Reporting Requirements: Beneficial Ownership Reporting: Entities or individuals that directly or indirectly own more than 5% of a class of voting equity securities registered under Section 12 of the Exchange Act are required to file reports on Schedule 13D or Schedule 13G. Institutional Investment Managers: Managers exercising investment discretion over accounts holding equity securities with an aggregate fair market value of $100 million or more must file quarterly reports on Form 13F. Large Trader Reporting: Persons or entities that trade significant amounts of NMS securities are required to file Form 13H to provide identifying information to the SEC. Section 16 Reporting Requirements: Insider Reporting: Directors, officers, and beneficial owners of more than 10% of any class of equity security registered under Section 12 must file reports disclosing their ownership and transactions. Forms Required: Form 3: Initial statement of beneficial ownership, filed within 10 days of becoming an insider. Form 4: Reports changes in ownership, filed within two business days following the transaction. Form 5: Annual statement of beneficial ownership, filed to report transactions not previously reported. This update serves as a vital resource for understanding the complexities of SEC reporting obligations, ensuring compliance, and avoiding potential penalties associated with non-compliance.
Corporate Finance · Finance · Risk Management
Felix Zumstein · Book
"Python for Excel" by Felix Zumstein is a comprehensive guide that demonstrates how to integrate Python into Excel workflows, enhancing data analysis and automation capabilities for experienced Excel users. As the creator of xlwings, a popular open-source package for automating Excel with Python, Zumstein provides practical insights and tools to bridge the gap between these two platforms. Key Features: Introduction to Python in Excel: The book begins by introducing Python as a powerful tool for Excel users, emphasizing its advantages over traditional VBA scripting. It guides readers through setting up modern development environments, including Jupyter notebooks and Visual Studio Code, to streamline their workflow. Data Analysis with pandas: Readers learn to utilize the pandas library to acquire, clean, and analyze data, effectively replacing typical Excel calculations with more robust Python operations. This section covers data manipulation techniques essential for efficient analysis. Automation of Excel Tasks: The book provides strategies to automate repetitive tasks such as consolidating workbooks and generating reports. By leveraging Python scripts, users can significantly reduce manual effort and increase productivity. Building Interactive Tools with xlwings: Zumstein delves into using xlwings to create interactive Excel tools that harness Python as a calculation engine. This enables the development of sophisticated applications within the familiar Excel interface. Database Integration and Web Data Retrieval: The guide covers connecting Excel to external databases and importing data from the web using Python, expanding the scope of data sources available for analysis. Unified Tool for Automation and Analysis: By combining Python with Excel, users can replace multiple tools such as VBA, Power Query, and Power Pivot, creating a cohesive and versatile environment for data tasks. For hands-on practice, the author provides a companion repository containing all relevant Jupyter notebooks and code samples, facilitating practical application of the concepts discussed.
Python · Data Visualization · SQL
Jaime Buelta · Book
"Python Automation Cookbook" by Jaime Buelta is a practical guide that demonstrates how to automate various tasks using Python, aiming to enhance efficiency and productivity. The book employs a problem-solution approach, providing recipes to automate repetitive tasks across different domains. Key Features: Web Scraping and Data Extraction: Learn techniques to scrape websites, detect changes, and extract valuable information for analysis. File and Data Management: Discover methods to search, process, and aggregate raw data files into structured formats like spreadsheets. Report Generation: Explore ways to extract data from Excel spreadsheets and generate comprehensive reports with graphs using libraries such as Matplotlib. Marketing Automation: Understand how to automatically generate marketing campaigns and communicate with recipients over different channels. Debugging Techniques: Gain insights into identifying and implementing precise solutions to common automation challenges. The second edition of the book includes additional chapters focusing on automated code testing, machine learning projects, and handling unstructured data, reflecting Python's growth in data science and AI automation. This cookbook is suitable for developers, data enthusiasts, or anyone interested in automating monotonous manual tasks related to business processes such as finance, sales, and HR. It serves both as a step-by-step guide for beginners and a reference for experienced Python users seeking to streamline their workflows.
Python · Machine Learning · Data Visualization
Zed A. Shaw · Book
"Learn More Python 3 the Hard Way" by Zed A. Shaw is a continuation of his instructional series, designed for individuals who have a foundational understanding of Python and wish to deepen their programming skills. This book emphasizes hands-on learning through a series of 52 meticulously crafted exercises, each aimed at building practical capabilities in Python programming. Key Features: Project-Based Learning: The book adopts a project-centric approach, guiding readers through exercises that involve analyzing problems, designing solutions, and implementing them in Python. This methodology fosters a deeper understanding of programming concepts and enhances problem-solving skills. Emphasis on Process and Quality: Shaw underscores the importance of adhering to a structured process, nurturing creativity, and striving for quality in coding practices. These principles are woven throughout the exercises to cultivate disciplined and proficient programmers. Supplementary Video Content: The book is complemented by over 12 hours of online video tutorials, where Shaw demonstrates how to break, fix, and debug code. This visual aid reinforces the lessons from the exercises and provides deeper insights into effective coding practices. "Learn More Python 3 the Hard Way" is ideal for readers who have completed introductory Python courses and are seeking to advance their skills through practical application. By engaging with real-world projects and challenges, readers can transition from basic understanding to proficient coding, preparing them for more complex programming endeavors.
Python · Math · Computer Science
Mustafa Germec · Book
"Python Tutorial (Codes)" by Mustafa Germec, PhD, is a comprehensive guide aimed at both beginners and intermediate learners seeking to enhance their Python programming skills. The tutorial is structured into 21 chapters, each focusing on a specific aspect of Python, providing detailed explanations and practical code examples. Key Features: Foundational Concepts: The initial chapters introduce the basics of Python, including data types, strings, lists, tuples, sets, and dictionaries. These sections lay the groundwork for understanding how data is stored and manipulated in Python. Control Structures: Subsequent chapters delve into conditions and loops, explaining how to control the flow of a Python program using conditional statements and iterative processes. Functions and Exception Handling: The tutorial covers the creation and utilization of functions for modular programming and discusses exception handling to manage errors gracefully. Object-Oriented Programming (OOP): An in-depth exploration of classes and objects is provided, illustrating the principles of OOP and how they are implemented in Python. File Operations: Chapters on reading and writing files equip readers with the skills to handle file input and output operations, essential for data processing tasks. Advanced Topics: The latter sections introduce advanced concepts such as decorators, generators, lambda functions, list comprehensions, and the use of the math module. These topics are crucial for writing efficient and Pythonic code. Each chapter is designed to build upon the previous ones, ensuring a cohesive learning experience. The inclusion of practical code snippets throughout the tutorial allows readers to apply concepts immediately, reinforcing their understanding through hands-on practice. This tutorial is particularly beneficial for individuals aiming to solidify their Python programming abilities, offering a structured approach to both fundamental and advanced topics. By following this guide, readers can develop a robust foundation in Python, preparing them for more complex programming challenges.
Python · Computer Science · Math
Vikranth Dwaracherla, Seyed Mohammad Asghari, Botao Hao, Benjamin Van Roy · Report
"Efficient Exploration for LLMs" is a research paper authored by Vikranth Dwaracherla, Seyed Mohammad Asghari, Botao Hao, and Benjamin Van Roy, focusing on enhancing large language models (LLMs) through efficient exploration strategies in gathering human feedback. Key Contributions: Efficient Query Generation: The study introduces an agent that sequentially generates queries while concurrently fitting a reward model based on the feedback received. This approach aims to optimize the learning process by selecting the most informative queries. Double Thompson Sampling: The researchers employ double Thompson sampling for query generation, utilizing epistemic neural networks to represent uncertainty. This method balances exploration and exploitation, leading to more effective learning with fewer queries. Performance Improvement: The findings demonstrate that efficient exploration enables high levels of performance with significantly fewer queries, highlighting the importance of uncertainty estimation and the choice of exploration strategy in training LLMs. This research underscores the potential of incorporating advanced exploration techniques to enhance the efficiency and effectiveness of large language models, particularly in the context of human-in-the-loop training scenarios.
Machine Learning · AI · Quant Finance
George Serafeim · Report
"ESG: From Process to Product" is a working paper by George Serafeim that examines the evolution of Environmental, Social, and Governance (ESG) practices from internal corporate processes to marketable investment products. Key Insights: Transformation of ESG Practices: Initially, ESG encompassed internal processes such as measurement, analysis, management, and communication within organizations. Over time, the financial industry has transformed ESG into a product, leading to the proliferation of investment funds labeled as ESG-focused. Resulting Confusion: This shift has caused confusion among investors and stakeholders, as the ESG label is applied broadly without a standardized framework, making it challenging to assess the true impact and intentions of ESG-labeled investment products. Proposed Framework for ESG Products: Serafeim proposes a framework to define the objectives and characteristics of ESG investment products, emphasizing: Intentionality: The deliberate allocation of capital to achieve specific financial, environmental, and/or social outcomes. Measurability: The ability to quantify the outcomes to assess the effectiveness of the ESG strategies employed. Materiality: Focusing on ESG factors that are significant to the financial performance and long-term sustainability of the investment. Additionality: Ensuring that ESG investments lead to positive impacts beyond what would have occurred without the investment. By adopting this framework, the paper aims to reduce confusion and enhance the credibility and effectiveness of ESG-labeled investment products, ensuring they deliver on their promised financial, environmental, and social outcomes.
Corporate Finance · Finance · Risk Management
Eugene F. Fama & Kenneth R. French · Report
"Production of U.S. Rm-Rf, SMB, and HML in the Fama-French Data Library" is a working paper by Eugene F. Fama and Kenneth R. French that delves into the methodologies and data adjustments involved in constructing key financial factors used in asset pricing models. Key Insights: Factor Definitions: Rm-Rf (Market Excess Return): The return on the value-weighted portfolio of all NYSE, AMEX, and NASDAQ stocks minus the one-month U.S. Treasury bill rate. SMB (Small Minus Big): The return difference between small-cap and large-cap stocks, serving as a proxy for the size effect. HML (High Minus Low): The return difference between stocks with high and low book-to-market ratios, representing the value effect. Data Corrections and Rule Changes: The paper analyzes how various data corrections and rule modifications have impacted the returns of these factors. Understanding these effects is crucial for researchers and practitioners who rely on the Fama-French Data Library for empirical analyses. Methodological Transparency: By detailing the construction processes and the influence of data adjustments, the authors aim to enhance transparency and assist users in accurately interpreting factor returns derived from the library. This paper serves as a valuable resource for those utilizing the Fama-French Data Library, providing clarity on the construction and evolution of widely used financial factors.
Finance · Risk Management · Corporate Finance
D.A Davindson · Slides
"FINTECH, DATA & ANALYTICS: Mergers & Acquisitions and Valuation Trends in the Public and Private Markets" is a December 2021 report by D.A. Davidson & Co., authored by Ken Marlin, Vice Chairman of Tech Investment Banking. Key Insights: Resilience Amidst the Pandemic: The report highlights the surprising resilience of the FinTech, Data, and Analytics sectors during the COVID-19 pandemic, with global M&A activity rebounding to record levels in 2021. Strategic M&A Activity: Companies are actively engaging in M&A to expand customer bases, enhance product offerings, and achieve economies of scale. Buyers, including those with substantial cash reserves, are pursuing opportunities to add value beyond what standalone firms might achieve. Market Sustainability and Rationality: Despite high activity levels, the report suggests that the market operates on rational expectations of future performance and risk, with valuations grounded in sustainable business models and recurring revenue streams. Overall, the report provides a comprehensive analysis of the dynamic M&A landscape in the FinTech, Data, and Analytics sectors, emphasizing strategic growth, resilience, and rational market behaviors.
Corporate Finance · Finance · Risk Management
Eli Stevens, Luca Antiga, Thomas Viehmann · Book
"Deep Learning with PyTorch" by Eli Stevens, Luca Antiga, and Thomas Viehmann is an insightful and practical guide to mastering deep learning using the PyTorch framework. The book is designed for those who are familiar with basic machine learning concepts and want to dive deeper into the world of deep learning with a hands-on approach. Key Concepts Covered: Introduction to PyTorch: The book begins with an introduction to the PyTorch framework, highlighting its flexibility, ease of use, and efficient handling of dynamic neural networks. It emphasizes how PyTorch’s tensor operations form the backbone of deep learning models, similar to NumPy but with added support for GPU acceleration. Tensors and Autograd: The authors provide an in-depth exploration of tensors, the fundamental data structure in PyTorch, explaining their creation, manipulation, and efficient use. They also introduce autograd, PyTorch's automatic differentiation library, which simplifies the process of computing gradients for backpropagation in neural networks. Building Neural Networks: The book walks through the steps of constructing various neural network architectures using PyTorch's nn.Module class. It covers feedforward neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs), providing code examples and explanations on how to define, train, and evaluate these models. Training Neural Networks: A significant portion of the book focuses on model training, including defining loss functions, choosing optimizers (like SGD and Adam), and managing training loops. The authors dive into best practices for handling overfitting, using techniques like dropout, batch normalization, and data augmentation. Working with Real-World Data: The book emphasizes how to preprocess and load real-world data for deep learning tasks, including using PyTorch's DataLoader and Dataset classes. It also discusses the importance of data preprocessing and augmentation for improving model generalization. Transfer Learning: One of the standout sections of the book is on transfer learning. It explains how to leverage pre-trained models (like ResNet, VGG, etc.) for tasks like image classification, speeding up the training process and improving accuracy, especially when data is scarce. Generative Models and Advanced Topics: The book introduces advanced deep learning topics such as Generative Adversarial Networks (GANs), reinforcement learning, and other generative models, helping readers expand their knowledge into more complex areas of deep learning. Practical Considerations: The authors also provide practical insights on model evaluation, deployment, and debugging. They explain how to tune hyperparameters effectively and optimize models for deployment on CPUs and GPUs. Summary: "Deep Learning with PyTorch" is a comprehensive, practical guide that equips readers with the knowledge and tools to build state-of-the-art deep learning models using PyTorch. The book offers clear explanations, hands-on examples, and expert advice, making it an invaluable resource for both beginners and those looking to deepen their understanding of deep learning techniques. By the end of the book, readers will have a solid understanding of PyTorch's core concepts and be able to implement and train various types of deep learning models for real-world applications.
Machine Learning · Math · Computer Science
Mathew Salvaris, Danielle Dean, Wee Hyong Tok · Book
"Deep Learning with Azure" by Mathew Salvaris, Danielle Dean, and Wee Hyong Tok is a practical guide for data scientists, engineers, and AI practitioners who want to harness the power of Microsoft Azure to build, train, and deploy deep learning models. Through a series of hands-on tutorials, the book covers everything from setting up the development environment and creating deep learning models to deploying those models at scale on Azure. Key Concepts Covered Introduction to Azure and Deep Learning: The book begins with an introduction to Azure, particularly Azure Machine Learning (AML), a cloud-based service that simplifies the deployment of machine learning models. It explains how to use Azure's services to handle the computational resources required for deep learning tasks. Setting Up Your Deep Learning Environment: A key focus of the book is the setup and configuration of the deep learning environment on Azure. The authors walk through the steps required to set up Azure Machine Learning workspaces, create compute clusters, and use Azure Notebooks for efficient experimentation. The authors also discuss the integration of Azure Databricks for big data analytics and model training. Building Deep Learning Models: The book provides practical, hands-on examples of building various types of deep learning models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative models, using frameworks like TensorFlow, Keras, and PyTorch on Azure. By the end of the book, readers will have the skills necessary to leverage Azure’s cloud capabilities for deep learning, optimizing workflows, scaling model training, and deploying models for real-world applications. The book provides a comprehensive and structured approach to using Azure for deep learning, making it an essential resource for anyone working with AI in the cloud.
Machine Learning · SQL · AI
Stanley H.Chan · Book
"Introduction to Probability for Data Science" by Stanley H. Chan is an undergraduate-level textbook that emphasizes the integration of data computing and probability theory. The book aims to elucidate the motivations, intuitions, and implications of probabilistic tools used in science and engineering, highlighting their inseparability in modern data science. Key Concepts Covered: Mathematical Foundations: The book begins with essential mathematical concepts such as infinite series, approximations, integration, linear algebra, and basic combinatorics. These topics provide the necessary groundwork for understanding more advanced probabilistic theories. Probability Theory: Chan introduces fundamental probability concepts, including set theory, probability spaces, axioms of probability, and conditional probability. The text delves into independence, Bayes' theorem, and the law of total probability, offering a comprehensive understanding of these foundational principles. Random Variables and Distributions: The book explores discrete and continuous random variables, their probability mass functions (PMFs), probability density functions (PDFs), cumulative distribution functions (CDFs), expectations, moments, variances, and common distributions like Bernoulli, binomial, geometric, Poisson, uniform, exponential, and Gaussian. Joint Distributions and Multidimensional Analysis: Chan discusses joint PMFs and PDFs, marginal distributions, conditional distributions, covariance, correlation coefficients, and transformations of multivariate Gaussian distributions. The text also covers principal-component analysis and its applications. Sample Statistics and Estimation: The book addresses moment-generating and characteristic functions, probability inequalities, the law of large numbers, central limit theorem, regression principles, overfitting, bias-variance trade-off, regularization, and various estimation techniques such as maximum-likelihood estimation, maximum a posteriori estimation, and minimum mean-square estimation. Confidence, Hypothesis Testing, and Advanced Topics: Chan concludes with discussions on confidence intervals, bootstrapping, hypothesis testing, Neyman-Pearson tests, and other advanced topics, providing a holistic view of statistical inference methods used in data science.
Statistics · Computer Science · Quant Finance
Morningstar. · Report
Morningstar's Custom Calculation Data Points are specialized metrics that enable users to analyze a portfolio's performance by comparing its actual returns to those predicted based on its risk profile, as indicated by its Beta. These calculations provide deeper insights into investment performance beyond standard metrics. Key Features: Alpha (Excess Return): -Measures the difference between a portfolio's actual returns and its expected returns, given its Beta. -A positive Alpha indicates outperformance relative to the expected return based on Beta. -Calculated using the formula: α = (Average Monthly Excess Return of Investment) – (Beta × Average Monthly Excess Return of Benchmark) -Morningstar annualizes the monthly Alpha to present it in annual terms. Alpha (Non-Excess Return): -Assesses the difference between a portfolio's actual returns and its expected returns without considering its Beta. -A positive value suggests the portfolio has performed better than expected, while a negative value indicates underperformance. -Calculated by subtracting Beta-adjusted benchmark returns from the portfolio's raw returns. -Morningstar also annualizes this monthly Alpha for annual representation. Appraisal Ratio: -Evaluates the abnormal excess return per unit of non-systematic risk taken. -Computed by dividing the unannualized Alpha by the standard error of the residual. -A higher ratio indicates more efficient risk-adjusted returns. Custom Calculated Data Points: Morningstar offers custom calculated versions of certain data points, allowing users to access historical values and tailor calculations to specific analytical needs. For instance, custom calculated data points enable users to see past values for metrics like Morningstar Category, providing insights into a fund's classification history. Creating and Utilizing Custom Data Sets: Users can create custom data sets in Morningstar Direct by selecting relevant data points, including custom calculations, to focus on specific analysis criteria. This customization enhances the precision of performance evaluations and benchmarking processes. Integration with Excel: Morningstar's Excel Add-In allows users to retrieve various data points, including custom calculations, directly into Microsoft Excel. This integration facilitates further data manipulation, formatting, and charting, streamlining the analysis process. By leveraging these custom calculation data points, investors and analysts can gain a nuanced understanding of portfolio performance, enabling more informed investment decisions and comprehensive performance assessments.
Risk Management · Quant Finance · Finance
Huy Nguyen, Ha Pham, Cedric Chin · Book
"The Analytics Setup Guidebook," authored by Huy Nguyen, Ha Pham, and Cedric Chin, serves as a practical manual for constructing modern, scalable analytics and business intelligence (BI) systems. Targeted primarily at technical team members embarking on setting up analytics infrastructures, the book offers a structured approach to understanding and implementing contemporary data analytics architectures. Key Concepts Covered: High-Level Overview of Analytics Systems: The guidebook introduces the components of modern analytics stacks, emphasizing their interconnections and roles in data processing pipelines. Data Consolidation: It discusses strategies for centralizing data from diverse sources, highlighting best practices in Extract, Load, Transform (ELT) processes and the rationale behind the industry's shift from traditional Extract, Transform, Load (ETL) methods. Data Transformation and Modeling: The book delves into transforming and modeling data to align with business objectives, comparing modern approaches to traditional methodologies and offering insights into efficient data structuring. Data Utilization: It explores the application of data through various means such as ad-hoc reporting, data exploration, and visualization, providing guidance on selecting appropriate BI tools and understanding their roles within the analytics ecosystem.
SQL · Data Visualization · Math
Author Unknown · Slides
Definition of Corporate Finance 3. The Goal of Financial Management 5. In general, the business has no existence apart from its owner: its life is limited to the proprietor’s own life span.
Corporate Finance · Finance · Quant Finance
Author Unknown · Report
Pairs Trading Pairs trading is a market neutral trading strategy that involves buying and selling two highly correlated financial instruments simultaneously. The idea is to profit from the difference in price movements between the two instruments. Pairs trading is often used in the stock market, but can also be applied to other markets such as futures and options. The strategy is based on the idea that while individual stocks may be affected by company - specific or market - wide events, the relative relationship between two highly correlated stocks will remain relatively stable over time.
Quant Finance · Risk Management · Finance
Stack Overflow Documentation · Book
Learning Pandas (Stack Overflow Documentation) is a practical and comprehensive guide to the Pandas library, offering a structured approach to data manipulation and analysis in Python. Designed for data analysts, scientists, and developers, this book compiles expert knowledge from Stack Overflow, providing clear explanations and hands-on examples to enhance data processing skills. Key Highlights: Foundational Understanding of Pandas : Introduces Pandas' core data structures, Series and DataFrames, demonstrating their fundamental role in data analysis. Data Importing and Exporting: Covers techniques for reading and writing data in various formats, including CSV, Excel, JSON, and SQL, ensuring seamless integration with different data sources. Data Cleaning and Transformation: Guides readers through handling missing values, filtering, indexing, and data type conversions, essential for preparing datasets for analysis. Efficient Data Manipulation: Explains powerful Pandas functions such as groupby(), merge(), pivot(), and apply(), enabling effective aggregation, reshaping, and computation on datasets. Exploratory Data Analysis (EDA): Discusses summary statistics, value distributions, and data visualization using Pandas, Matplotlib, and Seaborn, aiding in uncovering key insights. Advanced Techniques and Performance Optimization: Introduces time series analysis, vectorization, and efficient memory usage, helping users work with large datasets effectively. Real-World Applications: Provides practical examples and workflows for finance, machine learning preprocessing, and business analytics, making Pandas a valuable tool across industries. With a practical, example-driven approach, this book equips readers with the knowledge to harness the full power of Pandas for data analysis, making complex tasks more manageable and efficient. Whether you’re a beginner or an experienced user, this guide serves as an essential reference for mastering Pandas in Python.
Python · Data Visualization · Machine Learning
Christopher M. Bishop · Book
Pattern Recognition and Machine Learning by Christopher M. Bishop is a fundamental textbook that provides a comprehensive introduction to statistical pattern recognition and machine learning. The book is well-regarded for its probabilistic approach, making it an essential resource for students, researchers, and practitioners in data science, artificial intelligence, and related fields. Key Highlights: Foundations of Pattern Recognition Introduces the Bayesian approach to pattern recognition, emphasizing probability distributions and decision theory. Covers fundamental concepts such as linear regression, classification, and generative models. Probability and Machine Learning Discusses core probability distributions, including Gaussian, Multivariate Gaussian, and Exponential families. Explains Bayesian inference, its role in machine learning, and its advantages over frequentist methods. Supervised Learning Techniques Covers linear and logistic regression, support vector machines (SVMs), and neural networks. Introduces regularization techniques to prevent overfitting. Unsupervised Learning and Clustering Explores principal component analysis (PCA), mixture models, and expectation-maximization (EM) algorithms. Discusses clustering techniques such as k-means and Gaussian mixture models (GMMs). Graphical Models and Probabilistic Inference Introduces Bayesian networks and Markov random fields for modeling dependencies between variables. Covers approximate inference techniques, including variational inference and Markov chain Monte Carlo (MCMC). Kernel Methods and Advanced Topics Discusses kernel-based learning, including Gaussian processes and support vector machines (SVMs). Covers hidden Markov models (HMMs) and their applications in sequential data analysis.
Machine Learning · Computer Science · Statistics
Alice Prahmann, Franziska Zangl, Oliver Dlugosch, Stefanie Milcke · Book
No encontrado
Software Engineering · Finance
Stack Overflow Documentation · Book
Learning Node.js (Stack Overflow Documentation) is a practical guide that compiles community-driven knowledge from Stack Overflow, offering a hands-on approach to mastering Node.js. This book is designed for developers of all levels who want to build scalable and high-performance applications using JavaScript on the server side. Key Highlights: Introduction to Node.js Explains the event-driven, non-blocking I/O model and why Node.js is ideal for real-time applications. Covers installation, setting up a development environment, and running a basic Node.js script. Core Node.js Modules and APIs In-depth coverage of built-in modules like fs (File System), http, events, path, os, and util. Working with buffers, streams, and process management. Asynchronous Programming Explains callbacks, Promises, and async/await to handle asynchronous operations effectively. Covers best practices for avoiding callback hell and improving code readability. Building Web Servers with Node.js Creating a basic HTTP server using the http module. Introduction to Express.js, a popular framework for building web applications and APIs. Working with Databases Connecting Node.js to relational (MySQL, PostgreSQL) and NoSQL (MongoDB) databases. Performing CRUD operations and handling database connections efficiently.
Software Engineering · Machine Learning · SQL
Usharani Bhimavarapu & Jude D. Hemanth · Book
Learning Professional Python by Usharani Bhimavarapu and Jude D. Hemanth is a two-volume series designed to provide a comprehensive understanding of Python programming, catering to both beginners and experienced programmers. The series emphasizes object-oriented programming concepts and practical applications, aiming to equip readers with the skills necessary for professional Python development. Volume 1: The Basics Overview: This volume introduces readers to the fundamentals of Python programming, focusing on object-oriented principles and essential programming constructs. It's suitable for individuals new to programming or those transitioning from other languages. Key Highlights: Python Fundamentals: Covers the basics of Python syntax, data types, and control structures, providing a solid foundation for further learning. Object-Oriented Programming (OOP): Explores core OOP concepts such as classes, objects, inheritance, dynamic dispatch, interfaces, and packages, enabling readers to write modular and reusable code. Advanced Features: Delves into Python's generics and collections, exception handling, multithreaded applications, and designing graphical user interface (GUI) applications, preparing readers for complex programming tasks. Volume 2: Advanced Overview: Building upon the foundational knowledge from Volume 1, this volume delves into advanced topics, providing readers with the expertise needed for complex application development. Key Highlights: Advanced OOP Concepts: Further explores object-oriented principles, enhancing the reader's ability to design sophisticated systems. Exception Handling and Multithreading: Provides in-depth coverage of robust error handling and concurrent programming, essential for developing reliable and efficient applications. GUI Application Development: Guides readers through designing and implementing user-friendly GUI applications, expanding the scope of Python projects. Case Studies: Presents real-world projects such as WhatsApp Analyzer, Breast Cancer Prediction, Stock Price Prediction, and more, offering practical insights into applying Python in various domains.
Python · Math · Computer Science
Roozbeh Hazrat · Book
A Course in Python: The Core of the Language by Roozbeh Hazrat is a comprehensive textbook that introduces readers to Python programming through clear examples and worked-out exercises. Originally developed for undergraduate students in mathematics, science, engineering, and finance, the book is also suitable for self-study, especially for researchers aiming to use Python as a computational tool. Key Highlights: Structured Learning: Each section begins with a new topic description, followed by basic examples and detailed exercises, facilitating a step-by-step learning process. Practical Application: Emphasizes programming and problem-solving, particularly in numerical problems that don't require advanced mathematics. Comprehensive Coverage: Includes chapters on data handling, calculus, equation solving, and graphics, covering fundamental Python topics. Author's Expertise: Roozbeh Hazrat, a Professor of Pure Mathematics at Western Sydney University, brings extensive teaching experience in computer programming across various institutions. This book serves as an ideal resource for those seeking a hands-on, example-driven introduction to Python, balancing theoretical concepts with practical coding skills.
Math · Computer Science · Python
ETC Group · Slides
ETC Group, a leading provider of cryptocurrency exchange-traded products (ETPs), regularly publishes the "Crypto Market Compass," offering in-depth analyses of the cryptocurrency market's performance, investor sentiment, and emerging trends. Key Highlights from Recent Reports: Record Inflows into Crypto ETPs: In late 2024, ETC Group observed unprecedented net inflows into global crypto ETPs, with weekly inflows reaching $2.94 billion, primarily driven by Bitcoin investments. Impact of U.S. Strategic Crypto Reserve Announcement: In March 2025, President Donald Trump's proposal to include major cryptocurrencies like Bitcoin and Ethereum in the U.S. strategic reserve led to an initial surge in cryptocurrency prices. However, these gains were short-lived, with prices stabilizing at lower levels. Market Volatility and Regulatory Developments: The cryptocurrency market has experienced significant fluctuations, influenced by global economic indicators and policy announcements. Notably, the largest-ever cryptocurrency theft occurred recently, further impacting market sentiment. Positive Sentiment Amidst Global Growth Concerns: Despite global growth risks, ETC Group's "Cryptoasset Sentiment Index" indicated a bullish outlook, reaching levels not seen since March 2024, reflecting renewed investor confidence
Finance · Risk Management
Author Unknown · Book
Overview of Correlation and Causation 2. Pearson Correlation Coefficient (r) 2. Spearman Rank Correlation Coefficient (or rs) 3. Kendall Tau Rank Correlation Coefficient (or Kendall’s ) 4.
Risk Management · Statistics
James P. Harrington, Carla S. Nunes, Anas Aboulamer, Roger J. Grabowski · Book
"Valuation Handbook—International Guide to Cost of Capital," authored by James P. Harrington, Carla S. Nunes, Anas Aboulamer, and Roger J. Grabowski, is a comprehensive resource for estimating the cost of capital across various international markets. This guide is essential for professionals involved in business valuation, investment analysis, and financial decision-making, offering methodologies and data to assess the cost of equity capital globally. Key Highlights: Global Cost of Capital Estimation: The handbook provides methodologies to estimate country-level cost of equity capital for over 180 countries, incorporating factors like country risk premia, equity risk premia, and relative volatility. Data-Driven Insights: It offers access to international data that is often costly and difficult to obtain, assembling it into easy-to-use inputs for cost of capital calculations. Practical Application: The guide translates complex concepts and methodologies into practical applications, supported by exemplifying cases that demonstrate their real-world usage. Updated Content: The 2023 Summary Edition includes updated content and all methodologies previously published, ensuring that readers have access to the most current data and practices in cost of capital estimation. This handbook serves as a vital tool for professionals seeking to understand and apply cost of capital concepts in international contexts, enhancing their ability to make informed financial decisions.
Corporate Finance · Risk Management · Finance
Damien Cleusix · Slides
No encontrado
Finance · Risk Management
Nikita Prasad · Report
No encontrado
Machine Learning · Risk Management · Quant Finance
Maciej Besta, Florim Memedi, Zhenyu Zhang, Robert Gerstenberger, Nils Blach, Piotr Nyczyk, Marcin Copik, Grzegorz Kwasniewski, Jurgen Muller, Lukas Gianinazzi, Ales Kubicek, Hubert Niewiadomski, Onur Mutlu,Torsten Hoefler · Report
"Topologies of Reasoning: Demystifying Chains, Trees, and Graphs of Thoughts" is a comprehensive study by Maciej Besta, Florim Memedi, Zhenyu Zhang, Robert Gerstenberger, Nils Blach, Piotr Nyczyk, Marcin Copik, Grzegorz Kwaśniewski, Jürgen Müller, Lukas Gianinazzi, Ales Kubicek, Hubert Niewiadomski, Onur Mutlu, and Torsten Hoefler, published in January 2024. The paper explores structured prompting techniques in large language models (LLMs), focusing on how different reasoning topologies—chains, trees, and graphs—can enhance model performance across various tasks. Key Highlights: Structured Prompting Techniques: The study introduces paradigms like Chain-of-Thought, Tree of Thoughts, and Graph of Thoughts, which guide LLM reasoning through explicit structures, leading to improved task-solving capabilities. Taxonomy of Reasoning Topologies: The authors present a taxonomy categorizing structure-enhanced LLM reasoning schemes, analyzing aspects such as topology class, scope, representation, and execution algorithms. Comparative Analysis: By dissecting existing prompting schemes, the paper evaluates how design choices influence performance metrics like accuracy, latency, and cost, providing insights into effective prompting strategies. Theoretical Insights and Future Challenges: The paper discusses the interplay between prompting techniques and other LLM components, highlighting research challenges and suggesting directions for future advancements in prompt engineering. This work serves as a valuable resource for understanding and advancing structured prompting methods in LLMs, offering a foundation for developing more efficient and effective language models.
AI · Machine Learning · Math
Andrew Ng & Tengyu Ma · Report
The CS229 Lecture Notes are comprehensive materials from Stanford University's Machine Learning course, CS229, primarily authored by Andrew Ng and later updated by Tengyu Ma. These notes serve as a foundational resource for understanding various machine learning algorithms and principles. Key Highlights: Supervised Learning: Linear Regression: Introduces the Least Mean Squares (LMS) algorithm and the normal equations for parameter estimation. Logistic Regression: Discusses classification tasks and the application of logistic functions. Regularization: Explores techniques like L1 and L2 regularization to prevent overfitting. Unsupervised Learning: Clustering: Covers methods such as the k-means algorithm for grouping similar data points. Dimensionality Reduction: Explains Principal Component Analysis (PCA) for reducing feature space while preserving variance. Reinforcement Learning: Markov Decision Processes (MDPs): Provides a framework for modeling decision-making in environments with uncertainty. Policy Gradient Methods: Discusses approaches for optimizing policies in reinforcement learning scenarios. These lecture notes are available on the official CS229 website and have been utilized globally by students and professionals seeking a structured understanding of machine learning concepts. For more detailed information, you can refer to the CS229 Lecture Notes.
Machine Learning · Computer Science · Math
Mike McGrath · Book
"Python Makes Programming Fun!" by Mike McGrath is an engaging introduction to Python, designed to make learning to program an enjoyable and accessible experience for beginners. The book provides a step-by-step approach to Python programming, ensuring that readers build a solid foundation in coding while having fun with practical examples and projects. Key Highlights: Beginner-Friendly Approach: The book is designed for those with little or no prior programming experience, making it an excellent resource for students, hobbyists, and aspiring developers who want to explore Python in a simple and engaging way. Core Python Concepts Explained Clearly: Readers are introduced to fundamental programming concepts such as variables, data types, loops, conditional statements, functions, and error handling. Each topic is explained in an easy-to-follow manner with practical examples. Hands-On Coding with Fun Projects: The book encourages hands-on learning by guiding readers through small, enjoyable coding projects. These include creating simple games, automating tasks, and building interactive programs that make learning Python an exciting experience. Graphical and Interactive Programming: In addition to traditional programming exercises, the book covers how to use Python libraries such as Tkinter for GUI development and turtle graphics to create visual projects, making programming more engaging for beginners. Logical Thinking and Problem-Solving Skills: By working through various challenges and exercises, readers develop logical thinking and problem-solving skills that are essential for programming in any language. Python’s Versatility and Real-World Applications: The book highlights Python's broad applications, from web development to data science, showing how learning Python can open doors to various career opportunities. Simple, Practical, and Fun Learning Experience: The author emphasizes a fun and enjoyable learning journey, making programming less intimidating and more rewarding. The use of humor, interactive examples, and step-by-step instructions ensures that readers stay engaged.
Python · Math · Data Visualization
David Freiberg, Jean Rogers, George Serafeim · Report
"How ESG Issues Become Financially Material to Corporations and Their Investors" by David Freiberg, Jean Rogers, and George Serafeim explores the growing significance of Environmental, Social, and Governance (ESG) factors in corporate financial performance. The book provides a comprehensive framework for understanding how ESG considerations transition from ethical concerns to financially material factors that influence investor decisions and corporate strategy. Key Highlights: Understanding ESG and Financial Materiality: Introduces ESG concepts and explains how they evolve into material financial factors that impact company performance, risk management, and long-term value creation. Framework for ESG Materiality: Discusses how ESG issues gain financial relevance based on industry context, regulatory changes, consumer preferences, and market trends. Empirical Evidence on ESG Impact: Presents research-backed insights on the correlation between strong ESG performance and financial outcomes such as profitability, cost of capital, and stock performance. Investor Perspective on ESG Integration: Explores how institutional investors and asset managers assess ESG risks and opportunities in making investment decisions. Case Studies of ESG Materiality in Action: Provides real-world examples of companies that have successfully integrated ESG factors into their business models, highlighting best practices and lessons learned. Future Trends in ESG Investing: Examines the evolving regulatory landscape, the role of data analytics in ESG measurement, and the increasing adoption of ESG reporting frameworks.
Corporate Finance · Finance · Risk Management
Mustafa Dereci, Mehmet Gerz, Arun S. Muralidhar · Report
"Addressing Infrastructure Funding and Retirement Security through Islamic Finance: Sukuk SeLFIES" is an insightful guide that explores the potential of Islamic finance to address two critical global challenges: infrastructure funding and retirement security. Authored by Mustafa Dereci, Mehmet Gerz, and Arun S. Muralidhar, the book introduces the innovative SeLFIES framework, which combines Sukuk (Islamic bonds) with long-term infrastructure investments to ensure both economic sustainability and financial security for individuals in the long term. Key Highlights: Introduction to Islamic Finance and Sukuk: The book starts by explaining the principles of Islamic finance, including the prohibition of interest (Riba) and the use of asset-backed securities. It provides an in-depth look at Sukuk, an important financial instrument that complies with Islamic law and facilitates investment in infrastructure projects. The SeLFIES Framework: The authors introduce the SeLFIES model, which stands for Sukuk for Long-term Financing of Infrastructure and Economic Sustainability. This innovative approach aims to combine the benefits of Sukuk with the urgent need for infrastructure funding and long-term retirement solutions. The framework shows how Islamic finance can be a viable tool for creating a sustainable future. Infrastructure Funding and Challenges: One of the primary focuses of the book is the growing global demand for infrastructure financing. The authors examine how traditional funding methods often fall short and how Sukuk can be utilized to fill this gap. By raising funds for infrastructure projects, Sukuk provides a mechanism for sustainable economic growth while adhering to Islamic financial principles. Retirement Security through Islamic Finance: The book discusses the challenges related to retirement funding, especially in economies with aging populations. It presents how SeLFIES can help individuals build long-term savings through asset-backed securities, addressing the dual need for infrastructure investment and retirement security. Real-World Applications and Case Studies: With practical examples and case studies, the book demonstrates the successful use of Sukuk in funding infrastructure projects and enhancing retirement security. It highlights the economic impact and scalability of the SeLFIES model across different regions and sectors. Social and Economic Impact: The book concludes by showcasing the broader economic and social benefits of using Sukuk and SeLFIES. This includes financial inclusion, job creation, and the promotion of ethical and socially responsible investments.
Finance · Corporate Finance · Quant Finance
Michael U. Gutmann · Book
"Exercises in Machine Learning" by Michael U. Gutmann is a comprehensive and hands-on resource for anyone seeking to deepen their understanding of machine learning algorithms through practical exercises. Designed for students, practitioners, and machine learning enthusiasts, the book emphasizes applying theoretical knowledge to real-world problems, ensuring readers not only understand key concepts but also know how to implement them effectively. Key Highlights: Comprehensive Coverage of Machine Learning Topics: This book covers a broad range of machine learning techniques, offering exercises on both supervised and unsupervised learning, model evaluation, and optimization. It helps readers understand the core principles of machine learning and how to implement them practically. Supervised Learning Techniques: Key algorithms such as linear regression, logistic regression, support vector machines (SVM), and decision trees are explored in detail. Each exercise provides hands-on experience, allowing readers to apply these models effectively to real datasets, making complex topics more accessible. Unsupervised Learning Methods: Unsupervised techniques like k-means clustering, hierarchical clustering, and principal component analysis (PCA) are covered with practical exercises to help readers work with unlabelled data and uncover hidden patterns. Model Evaluation and Tuning: The book introduces essential model evaluation methods such as cross-validation, bias-variance tradeoff, and overfitting, helping readers understand how to assess model performance. Additionally, it covers hyperparameter tuning, including techniques like grid search and random search to enhance model accuracy. Advanced Topics: For those looking to explore more advanced machine learning techniques, the book touches on deep learning, reinforcement learning, and Bayesian methods, providing exercises that introduce these complex methods in a manageable and practical way. Python-Based Implementation: Each exercise is designed with Python in mind, using libraries such as NumPy, scikit-learn, and TensorFlow. This allows readers to apply machine learning concepts using the same tools that data scientists and machine learning engineers use in the industry.
Machine Learning · Math · Computer Science
Scott Powell, Duncan McKeen, Jeff Schmidt · Guide
"Financial Ratios Definitive Guide" by Scott Powell, Duncan McKeen, and Jeff Schmidt is an essential resource for anyone looking to understand and apply financial ratios in real-world business analysis. The book provides comprehensive insights into the key ratios used in financial analysis, helping readers better assess a company's performance, financial health, and potential for growth. It is ideal for professionals, investors, and students who want to gain a strong foundation in financial ratio analysis. Key Highlights: Introduction to Financial Ratios: The book begins with an overview of financial ratios, explaining their importance in evaluating a company's financial statements. It covers the basic principles behind ratios and how they reflect various aspects of business performance, such as profitability, liquidity, and solvency. Profitability Ratios: Powell, McKeen, and Schmidt introduce the key profitability ratios used to assess how efficiently a company generates profits from its operations. Ratios such as gross profit margin, operating margin, and return on equity (ROE) are discussed in detail, along with formulas and interpretation tips. Liquidity Ratios: The authors highlight liquidity ratios, which measure a company's ability to meet its short-term obligations. Ratios like the current ratio and quick ratio are explained, providing readers with tools to assess whether a business is well-positioned to cover immediate liabilities. Leverage and Solvency Ratios: Leverage ratios, such as the debt-to-equity ratio, interest coverage ratio, and debt ratio, are thoroughly covered in this guide. These ratios assess how much debt a company has taken on in relation to its equity and its ability to pay off debt. Understanding these ratios is crucial for evaluating a company’s long-term stability. Efficiency Ratios: The book covers efficiency ratios that evaluate how effectively a company uses its assets to generate sales. Ratios like inventory turnover and receivables turnover are discussed, helping analysts assess operational efficiency. Market Ratios: Powell, McKeen, and Schmidt dive into market ratios, which are used to evaluate a company's market performance and investor sentiment. Ratios like the price-to-earnings (P/E) ratio, price-to-book (P/B) ratio, and dividend yield are explored in-depth, helping readers understand how the market views a company's value and growth prospects. Comprehensive Ratio Analysis: The authors provide a step-by-step approach to performing comprehensive financial ratio analysis, teaching readers how to combine multiple ratios to form a holistic view of a company's financial situation. They explain how different types of ratios work together and how to spot red flags or opportunities. Real-World Case Studies: Throughout the book, real-world examples and case studies are provided to demonstrate how financial ratios are applied in practice. These case studies help readers understand how financial ratios are used in decision-making, such as evaluating investment opportunities or analyzing financial performance during mergers and acquisitions.
Corporate Finance · Finance · Quant Finance
Marco Avellaneda · Report
"Dispersion Trading" by Marco Avellaneda offers an in-depth exploration of a unique and strategic approach to trading options, focusing on the concept of dispersion trading, which involves exploiting the differences between the volatility of individual stocks and the volatility of index options. This book is a comprehensive guide for both novice and experienced traders, aiming to provide insights into the techniques and strategies used in dispersion trading. Key Highlights: Concept of Dispersion Trading: The book introduces dispersion trading, which is based on the notion that individual stock volatilities deviate from the volatility of an index. Traders look to capitalize on these differences by buying or selling options on individual stocks while simultaneously trading index options. Mathematical Foundations: The author delves into the mathematical underpinnings of dispersion trading, using concepts from probability theory, stochastic processes, and statistical analysis to explain the behavior of volatility and correlations between individual stocks and index options. Risk Management: A significant portion of the book is dedicated to risk management strategies. It emphasizes how to mitigate potential risks associated with dispersion trades, such as managing exposure to index movements and individual stock price fluctuations. Modeling and Theoretical Framework: The book discusses various models used to predict price movements, including the Black-Scholes model and other options pricing models. It also presents advanced techniques for assessing the effectiveness of dispersion strategies. Practical Applications: Avellaneda emphasizes real-world applications of dispersion trading in different market conditions. The book provides step-by-step examples of how to execute these trades and evaluate their performance in different market environments. Market Efficiency and Arbitrage Opportunities: The author discusses the role of market efficiency in dispersion trading, outlining how arbitrage opportunities arise from mispricings between individual stock options and index options. He examines the dynamics of these opportunities and the conditions under which they can be exploited.
Quant Finance · Finance · Risk Management
Ajay Nagesh · Report
"Foundations of Machine Learning - Lecture Notes" by Ajay Nagesh is an in-depth guide to the foundational concepts of machine learning, offering a comprehensive overview of the theoretical underpinnings and practical applications of the field. The book is designed for students, professionals, and researchers who seek to understand the core principles of machine learning and their real-world applications. Key Highlights: Introduction to Machine Learning Concepts: The book starts with a strong introduction to machine learning, covering its history, evolution, and various paradigms such as supervised learning, unsupervised learning, and reinforcement learning. It establishes a solid theoretical foundation for understanding how machines can learn from data. Mathematical Foundations: The book dives into the key mathematical concepts that form the backbone of machine learning, such as linear algebra, probability theory, optimization, and statistics. These mathematical tools are essential for building, understanding, and analyzing machine learning models. Supervised Learning: A significant portion of the book focuses on supervised learning algorithms. The text explains the mechanics and applications of popular models, such as linear regression, logistic regression, decision trees, and support vector machines (SVMs). Each model is accompanied by examples, explaining the mathematical formulation, training process, and how to evaluate model performance. Unsupervised Learning: The book covers unsupervised learning techniques like clustering, dimensionality reduction, and anomaly detection. It introduces methods such as k-means clustering, hierarchical clustering, principal component analysis (PCA), and t-SNE, providing a solid understanding of how these techniques can be applied to real-world data without labeled outputs. Optimization and Model Evaluation: A critical section of the book is dedicated to optimization techniques used in machine learning, including gradient descent, stochastic gradient descent, and regularization methods. It also explores various performance metrics like accuracy, precision, recall, F1-score, and cross-validation, offering insights into how to evaluate model performance effectively. Deep Learning Basics: While the book focuses on foundational machine learning techniques, it also introduces readers to deep learning, discussing neural networks, backpropagation, and architectures like convolutional neural networks (CNNs) and recurrent neural networks (RNNs). This section serves as a bridge between classical machine learning and advanced deep learning techniques. Practical Applications: The book is rich with real-world examples and case studies, demonstrating how machine learning concepts can be applied to a wide range of fields, such as healthcare, finance, natural language processing (NLP), and computer vision. It highlights how machine learning models can be built and deployed to solve practical problems in these domains. Ethical Considerations: In addition to technical content, the book touches upon the ethical implications of machine learning, such as bias in data, fairness in algorithms, and the responsible use of AI. It discusses how to build ethical models and the importance of transparency in machine learning systems. Hands-On Learning: The book encourages hands-on learning by providing coding exercises and examples in Python, using libraries such as scikit-learn, TensorFlow, and Keras. The practical approach allows readers to implement and test the models they learn about in the book, reinforcing the theoretical concepts. Future Trends in Machine Learning: The book concludes with a discussion on emerging trends in machine learning, such as transfer learning, explainable AI (XAI), and the integration of machine learning with other fields like quantum computing. It helps readers understand where the field is headed and how to stay up-to-date with the latest advancements.
Machine Learning · Math · Computer Science
Aston Zhang, Zachary C. Lipton, Mu Li, and Alexander J. Smola · Book
"Dive into Deep Learning" is an open-source book that provides a comprehensive, hands-on introduction to deep learning techniques. Written by Aston Zhang, Zachary C. Lipton, Mu Li, and Alexander J. Smola, the book offers an in-depth exploration of the fundamental principles and applications of deep learning, with a focus on practical implementation using modern deep learning frameworks like Apache MXNet and PyTorch. Key Highlights: Introduction to Deep Learning: The book starts with an introduction to deep learning, explaining its significance and how it differs from traditional machine learning techniques. It covers the historical context of neural networks and their evolution into deep learning, highlighting key breakthroughs such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Mathematical Foundations: The authors provide a solid mathematical foundation for understanding deep learning. Topics like linear algebra, probability, optimization, and calculus are discussed, as they form the backbone of many deep learning algorithms. This foundational knowledge is essential for readers to grasp how deep learning models work under the hood. Neural Networks and Backpropagation: The book thoroughly explains the architecture of neural networks, covering perceptrons, multi-layer perceptrons (MLPs), and how backpropagation works to update model weights during training. The authors emphasize understanding how the model learns and how to compute gradients using the chain rule to minimize the loss function. Training Deep Networks: In this section, the book delves into the critical aspects of training deep learning models, including optimization algorithms (such as gradient descent), weight initialization, regularization techniques (dropout, batch normalization), and activation functions. The authors also discuss challenges in training deep models, such as vanishing and exploding gradients. Convolutional Neural Networks (CNNs): CNNs are covered in depth, as they are essential for tasks like image recognition and computer vision. The book explains convolution layers, pooling layers, and how CNNs can be used to automatically learn hierarchical feature representations from raw input data. It also provides hands-on examples for building and training CNNs for image classification tasks. Recurrent Neural Networks (RNNs) and Sequence Models: The book introduces RNNs and their variants, such as Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRUs), which are used for sequential data. It explores how RNNs can model time-dependent processes, such as speech recognition, language modeling, and machine translation. Generative Models: The authors introduce generative models, particularly Generative Adversarial Networks (GANs), which are used to generate new data samples that resemble a given dataset. GANs are explored with practical examples, showing their potential in generating images, videos, and even music. Transfer Learning: Transfer learning is another key topic in the book, where pre-trained models are fine-tuned for new tasks. The authors show how leveraging models that have been trained on large datasets can help with tasks where data is limited, improving model performance and reducing training time. Advanced Topics: The book also touches on more advanced topics in deep learning, such as reinforcement learning, attention mechanisms, and transformers, which have been key in the development of state-of-the-art models for tasks like natural language processing (NLP) and image generation.
Machine Learning · Math · Computer Science
Steven Cooper · Book
"Data Science from Scratch" by Steven Cooper is an excellent introduction to the world of data science for beginners and intermediate learners alike. The book focuses on the foundational concepts of data science and offers a practical approach to understanding its key principles, including algorithms, statistical analysis, and machine learning. The author breaks down complex topics into digestible pieces, providing a hands-on, Python-based approach for readers to learn and apply data science concepts from scratch. Key Highlights: Introduction to Data Science: The book begins with an overview of data science, its applications, and its importance in various industries. Cooper introduces the core concepts and workflows in data science, including data collection, cleaning, analysis, visualization, and modeling. He emphasizes the iterative process of working with data and the need for solid foundational knowledge to excel in the field. Python Basics for Data Science: Since the book assumes no prior experience in data science or programming, it starts with a primer on Python. The author introduces basic Python concepts, such as variables, loops, conditionals, functions, and data structures (lists, dictionaries, and tuples). The use of Python as a tool for data science is emphasized, with practical examples to illustrate key programming concepts. Data Collection and Preparation: A large portion of the book focuses on the critical step of data collection and preparation. Cooper walks readers through methods of gathering data from various sources, including APIs and databases. He also discusses techniques for cleaning data, handling missing values, and transforming raw data into a format suitable for analysis. The importance of data quality is stressed, and readers learn how to manage and preprocess data to get meaningful results. Exploratory Data Analysis (EDA): The book introduces exploratory data analysis (EDA) as a vital part of the data science process. Cooper explains how to summarize and visualize data using various Python libraries such as pandas, NumPy, and Matplotlib. Readers learn how to analyze distributions, detect patterns, and identify outliers in their datasets through descriptive statistics and visualizations. Probability and Statistics: The author covers the essential statistical concepts needed for data science, including probability theory, distributions, hypothesis testing, and confidence intervals. The book introduces concepts like mean, median, variance, correlation, and regression analysis to help readers understand how to quantify uncertainty and make inferences about data. Machine Learning: One of the key aspects of data science is machine learning, and the book provides a comprehensive introduction to this area. Cooper discusses supervised and unsupervised learning, explaining key algorithms like linear regression, k-nearest neighbors (KNN), decision trees, and clustering. The author also covers model evaluation techniques, such as cross-validation and confusion matrices, helping readers understand how to assess the performance of machine learning models.
Computer Science · Python · Statistics
messari.io · Book
"Crypto Theses for 2024" is Messari's annual report that provides an in-depth analysis of the cryptocurrency market, highlighting key trends, investment themes, influential figures, policy developments, and technical advancements expected to shape the crypto landscape in 2024. Key Highlights: Investment Themes: The report identifies several investment themes poised to influence the crypto market in 2024, emphasizing the importance of permissionless innovation over centralized entities. People to Watch: It profiles top individuals who are expected to have a significant impact on the crypto industry in 2024, offering insights into their contributions and potential influence. Policy Developments: The report examines legislative efforts and policy changes affecting the crypto space, including potential regulations and their implications for the industry. Technical Breakthroughs: It discusses anticipated technical innovations and developments within the crypto ecosystem, highlighting projects and protocols to watch in 2024. Overall, "Crypto Theses for 2024" serves as a comprehensive guide for professionals and enthusiasts seeking to understand and navigate the evolving crypto landscape in the coming year.
Finance · Risk Management
JP Morgan Chase & Co · Report
JPMorgan Chase & Co.'s "Carbon Market Principles" outlines the firm's perspective on the voluntary carbon market's role in achieving net-zero emissions. The document highlights key challenges and sets forth principles to guide the firm's engagement in the voluntary carbon market. Key Highlights: Role of Voluntary Carbon Markets: The paper emphasizes the importance of voluntary carbon markets in facilitating emissions reductions beyond regulatory requirements, supporting corporate sustainability goals, and driving innovation in carbon offset projects. Challenges in the Voluntary Carbon Market: It identifies challenges such as ensuring the additionality of carbon credits, maintaining transparency, preventing double-counting, and establishing standardized methodologies for measuring and verifying emissions reductions. JPMorgan Chase & Co.'s Carbon Market Principles: The firm introduces guiding principles to address the identified challenges and align with best practices: Additionality: Ensuring that carbon credits represent genuine emissions reductions that would not have occurred without the project. Transparency: Providing clear and accessible information about carbon credit projects, methodologies, and verification processes. Integrity: Adhering to high environmental and social standards, ensuring that projects deliver real and lasting benefits. Standardization: Supporting the development and adoption of standardized approaches for measuring, reporting, and verifying emissions reductions. Innovation: Encouraging the development of new methodologies and projects that enhance the effectiveness and efficiency of carbon markets. Commitment to Progress: JPMorgan Chase & Co. expresses its dedication to collaborating with stakeholders to advance the voluntary carbon market, aiming to overcome existing challenges and contribute to global climate objectives.
Corporate Finance · Risk Management · Finance
Daniel Vaughan · Book
"Data Science: The Hard Parts" by Daniel Vaughan is a comprehensive guide that delves into the more challenging and often overlooked aspects of data science. This book aims to equip readers with the knowledge and techniques required to navigate the complex, real-world challenges faced by data scientists. It goes beyond basic theory and covers essential techniques and strategies that help in tackling the most difficult aspects of data science projects. Key Highlights: Navigating Real-World Data Challenges: Vaughan emphasizes the importance of recognizing and dealing with the inherent complexity of real-world data. He stresses that a large portion of data science is understanding the intricacies and noise in data rather than relying purely on theoretical models. The book explains how to approach messy, incomplete, and imperfect data, which is a significant challenge in most data science tasks. Problem-Solving Framework: The book provides a clear framework for approaching and solving data science problems. It teaches readers how to break down problems into manageable chunks, ensuring that they can tackle complex issues systematically. Vaughan discusses how to structure data science workflows to optimize for success, while keeping the big picture in mind. Exploratory Data Analysis (EDA): The book dives deep into the process of exploratory data analysis, helping readers understand how to approach data, what techniques to use for cleaning and processing, and how to use EDA to find meaningful patterns. It goes beyond basic statistics, introducing methods for identifying hidden structures, correlations, and outliers that may impact model performance. Advanced Machine Learning Techniques: Vaughan covers advanced machine learning techniques, providing insights into more complex models and their real-world applications. The book tackles how to select the appropriate machine learning algorithm for a given problem, optimize model performance, and deal with challenges such as overfitting, underfitting, and bias in data. Model Evaluation and Validation: A significant part of the book is dedicated to model evaluation and validation. Vaughan explores the nuances of cross-validation, model selection, and the process of comparing models to ensure robustness and reliability. He emphasizes the importance of a clear evaluation strategy to avoid common pitfalls such as over-optimism in model performance. Dealing with Ambiguity and Uncertainty: Vaughan recognizes that data science often involves dealing with uncertainty and ambiguity. The book introduces probabilistic thinking and Bayesian techniques, helping readers make informed decisions when faced with incomplete or ambiguous data. This section is particularly useful for those working with predictive modeling and uncertain outcomes. Effective Communication and Presentation: The book highlights the importance of communication in data science. Vaughan discusses how to effectively present complex data insights to non-technical stakeholders, providing guidance on creating clear visualizations and communicating findings in a way that drives decision-making. This includes an emphasis on storytelling with data and visual storytelling. Practical Insights from Real-World Projects: Drawing from Vaughan’s own experience as a data scientist, the book offers real-world case studies and examples that illustrate how the principles discussed can be applied in practice. These examples provide valuable lessons on how to navigate the challenges of data science in different industries.
Computer Science · Data Visualization · Math
Urav Soni & Rhodri Preece · Report
"Valuation of Cryptoassets: A Guide for Investment Professionals" by Urav Soni and Rhodri Preece provides a comprehensive framework for understanding and evaluating cryptocurrencies and blockchain-based assets. This guide serves as an essential resource for investment professionals who are looking to navigate the complexities of the rapidly growing crypto market, offering a balanced and methodical approach to assessing the value of cryptoassets. Key Highlights: Introduction to Cryptoassets: The book begins with a clear introduction to the world of cryptoassets, distinguishing between cryptocurrencies (such as Bitcoin and Ethereum) and other types of blockchain-based assets, such as tokenized securities, utility tokens, and non-fungible tokens (NFTs). It outlines the key differences between traditional financial assets and digital assets, helping readers understand the unique characteristics of crypto investments. Valuation Framework: Soni and Preece present a systematic framework for valuing cryptoassets, which integrates traditional financial valuation models with the unique factors that affect digital currencies. The authors emphasize the importance of understanding the underlying blockchain technology, the token's utility or use case, and market demand when assessing the value of cryptoassets. Valuation Methods: The book explores various methods for valuing cryptoassets, including cost-based, income-based, and market-based approaches. These methods are adapted to account for the decentralization, volatility, and speculative nature of digital assets. The authors also discuss the limitations and challenges of applying traditional valuation techniques to the crypto world and highlight alternative metrics like network value and on-chain data analysis. Market Sentiment and Network Effects: A significant portion of the book focuses on understanding the role of market sentiment, network effects, and user adoption in the valuation of cryptoassets. The authors argue that the value of a cryptocurrency is highly influenced by the size of its user base, transaction volume, and the strength of its underlying community, as well as broader market trends and investor sentiment. Risk Factors: Cryptoassets are known for their high volatility and regulatory uncertainty. The book addresses various risk factors that can impact valuations, such as regulatory changes, technological risks, security concerns, and market liquidity. The authors provide practical strategies for mitigating risks and managing cryptoasset investments in a highly unpredictable market environment. Investor Considerations: Soni and Preece provide valuable insights for investment professionals regarding how to approach cryptoassets as part of an investment portfolio. They discuss the potential for diversification, the importance of due diligence, and the role of cryptocurrencies in hedging against traditional asset class risks. The book also explores the evolving regulatory landscape, emphasizing the importance of compliance for institutional investors. Case Studies and Real-World Applications: To further illustrate their valuation concepts, the authors include several case studies of well-known cryptocurrencies and blockchain projects. These case studies help to contextualize the valuation methods in real-world scenarios and demonstrate how the theories discussed in the book can be applied to practical investment decisions.
Corporate Finance · Risk Management · Quant Finance
Author Unknown · Notes
• GitHub - As Distributed version control system. • Jenkins - Continous Integration tool. • Anisible - Configuration Management & Deployment tool. • docker -Containerization • Kubernetes - As Container Management Tool.
Software Engineering · Data Visualization · SQL
Bilgin Ibryam & Roland Huß · Book
Kubernetes Patterns: Reusable Elements for Designing Cloud-Native Applications by Bilgin Ibryam and Roland Huß is a detailed guide that explores the design patterns essential for building scalable, reliable, and efficient cloud-native applications on Kubernetes. The book provides reusable solutions to common challenges faced during the design and implementation of Kubernetes-based systems, helping developers and architects optimize their workflows. Key Highlights: Introduction to Kubernetes and Cloud-Native Principles: The book starts with a solid foundation on Kubernetes and cloud-native application design. It introduces the core principles behind Kubernetes, including container orchestration, service management, and microservices architecture, which are fundamental to designing cloud-native applications. Understanding Design Patterns: The central theme of the book is design patterns—reusable solutions to recurring design problems. The authors demonstrate how design patterns are vital for building complex, cloud-native applications that need to scale, remain resilient, and be easily maintained over time. Observability and Monitoring Patterns: Kubernetes provides powerful tools for logging, monitoring, and tracing, and this book outlines patterns to implement observability in cloud-native applications. The authors explore how to monitor clusters, containers, and applications, ensuring that issues can be detected and resolved quickly. Scaling and Resilience Patterns: One of the key advantages of Kubernetes is its ability to scale applications efficiently. This section focuses on patterns to manage scaling horizontally and vertically, as well as ensuring that applications remain resilient to failures through automated self-healing processes, replica sets, and distributed systems. Security Patterns: Security is an essential part of designing cloud-native applications. The book outlines patterns for securing applications on Kubernetes, including role-based access control (RBAC), network policies, and managing sensitive data with Kubernetes secrets. Resource Management Patterns: Kubernetes allows for fine-grained control over resources, such as CPU and memory allocation. The authors explain patterns for managing resources effectively, ensuring that containers get the necessary resources while avoiding conflicts or bottlenecks. CI/CD and DevOps Integration: The book covers patterns for integrating Kubernetes with continuous integration and continuous deployment (CI/CD) pipelines. By automating testing and deployment workflows, developers can speed up release cycles and improve the quality and reliability of applications. Real-World Use Cases: Throughout the book, the authors provide practical case studies and real-world examples that demonstrate how the patterns discussed are implemented in live Kubernetes clusters. These case studies show how companies leverage Kubernetes to build resilient, scalable systems in production.
Software Engineering · Computer Science · Python
Author Unknown · Notes
Store now The Fama-French model is fully described in the chapter on Factor Models. In the previous chapter, we used the log-returns series of the PEP and KO stocks to create a combined portfolio, applying a Kalman filter to estimate the dynamic relationship between the two returns series. By taking weighted long and short positions in the two stocks, as determined by the βt coefficient estimated in the Kalman model, we were able to eliminate market risk and achieve a returns process that is close to being stationary. However, there is still the question of other risk factors such as size...
Risk Management · Quant Finance · Finance
BAI is Bank Administration Institute and BAI Center · Report
"2024 Regulations: How They Impact Your Compliance Training Programs" is a timely and essential guide for financial institutions, offering an in-depth exploration of the regulatory landscape and its influence on compliance training. The book addresses the evolving nature of regulations and provides practical strategies to ensure that institutions meet these challenges head-on, preparing their staff for an increasingly complex compliance environment. Key Highlights: Comprehensive Overview of 2024 Regulatory Changes: This book outlines the significant regulatory changes coming in 2024, breaking down their potential impacts on financial institutions. It explores how new and revised regulations affect key areas like data privacy, anti-money laundering (AML), cybersecurity, and financial reporting. Impact on Compliance Programs: The authors focus on the direct impact of these regulatory changes on compliance training programs. The book explains how institutions need to adapt their training content, delivery methods, and tracking mechanisms to stay compliant with the latest regulations. Adapting Training Programs to New Regulatory Demands: Curriculum Updates: The book highlights the need for compliance programs to be regularly updated to reflect changes in the regulatory environment, ensuring that staff members are well-equipped to navigate new rules. Tailored Training Approaches: Emphasizing the importance of personalized and role-specific training, the authors offer strategies for creating customized learning experiences that meet the needs of different departments within the institution. Technological Solutions for Compliance Training: Given the growing importance of technology in compliance, the book discusses how institutions can leverage learning management systems (LMS), automated compliance tools, and e-learning platforms to efficiently deliver up-to-date training content and monitor employee progress. Risk Management and Mitigation: The book also explores how compliance training can help mitigate risks related to regulatory breaches. By equipping employees with the knowledge of new regulations, institutions can reduce the likelihood of non-compliance and avoid costly penalties. Engagement and Retention in Training Programs: An essential aspect of any training program is ensuring engagement and retention of information. The authors delve into methods for making compliance training more interactive, engaging, and memorable, using real-world scenarios, case studies, and gamification techniques to enhance learning. Tracking and Measuring Effectiveness: The authors emphasize the importance of measuring the effectiveness of compliance training programs. The book provides guidance on how institutions can track employee progress, assess knowledge retention, and adjust training approaches based on data and feedback. Regulatory Trends to Watch Beyond 2024: The book goes beyond 2024, offering insights into the regulatory trends that are expected to shape compliance training in the coming years. This forward-looking perspective helps institutions stay proactive in preparing for future changes.
Corporate Finance · Risk Management · Finance
Micha Gorelick & Ian Ozsvald · Book
Understanding Performant Python. Profiling to Find Bottlenecks.
Python · Data Visualization · Software Engineering
Avalara · Slides
labor market remains strong, high inflation and the collapse of domestic and European banks are contributing to apprehension. As of September 2023, the Federal Reserve Bank of New York recession probability indicator suggested there is a 60.8% chance of a U.S. recession within the next year. inflation hit a 41-year high of 11.1% in 2022 and has been slow to fall, and some local economists predict a U.K.
Corporate Finance · Finance · Quant Finance
George Mount · Book
Foundations of Analytics in Excel 1. Foundations of Exploratory Data Analysis. 3 What Is Exploratory Data Analysis?
Data Visualization · SQL · Machine Learning
Eric Carter & Matthew Hurst · Book
Trademarked names, logos, and images may appear in this book. Rather than use a trademark symbol with every occurrence of a trademarked name, logo, or image we use the names, logos, and images only in an editorial fashion and to the benefit of the trademark owner, with no intention of infringement of the trademark. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. While the advice and information in this ...
Machine Learning · AI · Math
The Association Of The Bar Of The City Of New York · Report
Introduction ........................................................................................................................ What Are the Definitions of Artificial Intelligence and Machine Learning? What Are Some of the Use Cases for AI/ML in the Financial Services Sector?............ What Are Some of the Risks Associated With AI/ML?
Quant Finance · Machine Learning · Finance
Tutorials Point · Book
About the Tutorial Artificial intelligence is the intelligence demonstrated by machines, in contrast to the intelligence displayed by humans. This tutorial covers the basic concepts of various fields of artificial intelligence like Artificial Neural Networks, Natural Language Processing, Machine Learning, Deep Learning, Genetic algorithms etc., and its implementation in Python. Audience This tutorial will be useful for graduates, post graduates, and research students who either have an interest in this subject or have this subject as a part of their curriculum. The reader can be a beginner ...
AI · Python · Machine Learning
Loop Capital · Report
January 2024 2024 Fixed Income Outlook in One Word: Batman! At a recent conference, we were asked to summarize the state of the U.S. bond market in a single word. Unlike asset classes promising higher returns and lower volatility, bonds do not have superpowers.
Finance · Risk Management · Corporate Finance
Canonical Limited · Guide
summary Cloud technology and virtualisation enable developers and administrators to deploy infrastructure at a pace previously unheard of, which has brought them huge gains and enabled almost anyone to create internet services. However, this facility and flexibility does not mask the underlying need for deployments to be secure. The ever-present threats of ransomware attacks and data breaches make it imperative to lock down systems and prevent attackers from gaining an easy foothold. A fully secure system is made up of many layers, from the hardware to the operating system and the applicati...
Software Engineering · SQL · Computer Science
Sayan Mukhopadhyay · Book
Trademarked names, logos, and images may appear in this book. Rather than use a trademark symbol with every occurrence of a trademarked name, logo, or image we use the names, logos, and images only in an editorial fashion and to the benefit of the trademark owner, with no intention of infringement of the trademark. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. While the advice and information in this ...
Python · Data Visualization · Machine Learning
Matthew F. Dixon, Igor Halperin, Paul Bilokon · Book
The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for...
Quant Finance · Machine Learning · Finance
Enfusion · Slides
Investor & Analyst Day 2024 PROPRIETARY ©2024 ENFUSION. 2 Statements we make in this presentation may include statements which are not historical facts and are considered forward-looking within the meaning of Section 27A of the Securities Act of 1933 (the “Securities Act”) and Section 21E of the Securities Exchange Act of 1934 (the “Exchange Act”), including expectations regarding future financial performance. These forward-looking statements are usually identified by the use of words such as “anticipates,” “believes,” “estimates,” “expects,” “intends,” “may,” “plans,” “projects,” “seeks,” ...
Finance · Corporate Finance · Quant Finance
Author Unknown · Notes
Notes on analyzing NVIDIA stock options using the volatility Greeks: Delta, Gamma, Theta, Vega, and Rho. Explains how implied volatility and these sensitivity measures help traders assess risk and price movement expectations for options positions.
Quant Finance · Risk Management · Finance
World Economic Forum · Book
The final section examines the role of the risk function in organizational growth and innovation. The survey featured in this briefing was conducted in May 2024. A volatile mid-year outlook: the global perspective _ __________________________ 7 2. The overall global outlook appears
Risk Management · Finance · Corporate Finance
Department of Statistical Science University College London · Book
Introduction 4 1.1 Why design? 4 1.3 Experimentation versus sampling . 8 2.1.1 Principles of experimental design . 8 2.1.2 Planning of experiments .
Statistics · Risk Management · Software Engineering
Michael J de Smith · Book
The moral right of the authors has been asserted. Copies of this edition are available in electronic book and web-accessible formats only. Disclaimer: This publication is designed to offer accurate and authoritative information in regard to the subject matter. It is provided on the understanding that it is not supplied as a form of professional or advisory service.
Statistics · Data Visualization · Risk Management
U.S. Securities And Exchange Commission · Guide
It is not a rule, regulation, or statement of the U.S. Securities and Exchange Commission (SEC or Commission). The Commission has neither approved nor disapproved its content. This statement, like all staff statements, has no legal force or effect: it does not alter or amend applicable law, and it creates no new or additional obligations for any person.
Risk Management · Finance
Pablo Marchesi · Notes
Abstract This paper aims to provide a straightforward and intuitive introduction to Risk- Neutral Pricing for derivative securities. We begin by reviewing foundational concepts from Itˆo calculus, including Itˆo processes and martingales, along with the key theorems necessary for deriving the risk-neutral pricing formula. We then proceed to derive the formula, prioritizing intuition over mathematical rigor. We can define arbitrage as a trading strategy that begins with no money, has zero probability of losing money, and has a positive probability of mak- ing money [2].
Risk Management · Finance · Quant Finance
Zhiyuan Liu, Yankai Lin & Maosong Sun · Book
Preface In conventional natural language processing (NLP) systems, language items such as words and phrases are handled as distinct symbols. Many classical methods, such as n-gram and bag-of-words models, were proposed and have been widely used until now. All these methods take words as the minimum units for semantic representation, either used to estimate the conditional probabilities of the next word given previous words (e.g., n-gram) or used to represent semantic meanings of text (e.g., bag-of-words models). Even when people find it necessary to model word meanings, they either manually ...
AI · Machine Learning · Data Visualization
Peter Jäckel · Slides
Credit default swaps are widely used to hedge credit risk. For practically all reference entities, there is, at best, a liquid CDS market only in one currency. Credit risk hedging needs often arise for other currencies. A compound (back-to-back) package of a credit default swap in the liquid and the foreign currency is known as a Quanto CDS.
misc
Ankit Pandey · Book
Emphasis will also be placed on verifying the installation and resolving potential issues that may arise during the process. Windows Installation To install Python on a Windows operating system, the official Python distribution is recommended. Select the Downloads section and choose the appropria
Python · Data Visualization · Machine Learning
Hayden Van Der Post · Book
Excel, a stalwart of data manipulation, visualization, and analysis, is ubiquitous in business environments. Python, on the other hand, brings unparalleled versatility and efficiency to data handling tasks. Integrating these two can significantly enhance your data processing capabilities, streamline workflows, and open up new possibilities for advanced analytics. The Foundation: Why Integrate Python with Excel?
Python · Data Visualization · SQL
Hans-Petter Halvorsen · Book
Preface Python is a popular programming language, and it is one of the most used pro- gramming languages today. Python works on all the main platforms and operating systems used today, such Windows, macOS, and Linux. Python is a multi-purpose programming language, which can be use for simu- lation, creating web pages, communicate with database systems, etc. Here you can download the software, download code examples, etc.
Python · Math · Computer Science
AI Publishing · Guide
Introduction and Environment Set Up Data science libraries exist in various programming languages. However, you will be using Python programming language for data science and machine learning since Python is flexible, easy to learn, and offers the most advanced data science and machine learning libraries. Furthermore, Python has a huge data science community from where you can take help whenever you want. In this chapter, you will see how to set up the Python environment needed to run various data science and machine learning libraries.
Python · Machine Learning · Data Visualization
Nikita Lavrentyev · Notes
Introduction to Option Pricing in Commodity Markets As oil prices fluctuate amid uncertainty surrounding the Middle East and U.S. elections, traders are reminded of how unpredictable the energy markets can be. Commodity markets, such as oil, are particularly susceptible to sudden price swings due to a range of external factors, including geopolitical tensions, supply chain disruptions, and shifts in global demand. In such a volatile environment, accurately pricing options is crucial for managing risk and protecting against sudden price shifts.
Quant Finance · Python · Risk Management
Yuquan Li · Notes
Abstract This thesis improves the multilevel Monte Carlo simulation introduced in Giles [2008a] for option pricing. We use stratified sampling on the initial level and thus obtain a further variance reduction. Then we programme the whole procedure of option pricing using this algorithm by C++. Chapter 1 introduces the mathematical background needed for under- standing multilevel Monte Carlo methods.
Risk Management · Quant Finance · Statistics
Jonathan Weisberg · Book
A visual approach to understanding probability concepts, covering how odds relate to likelihood, Bayes' theorem, conditional probability, and common statistical distributions. Uses diagrams and intuitive examples to make abstract probability theory more accessible.
Risk Management · Finance
Alexandra Chouldechova · Slides
Lecture 2: Prediction Part I: Splines, Additive Models Part II: Model Selection and Validation Prof.
Statistics · Machine Learning · Risk Management
Amit Kumar Jha · Guide
At the heart of option pricing lies the concept of volatility - a measure of the uncertainty or risk associated with the magnitude of changes in an asset’s value. This comprehensive guide delves deep into three primary volatility models used in option pricing: the constant volatility model, the local volatility model, and the stochastic volatility model. Understanding these models is crucial for any practitioner in the field of quantitative finance. Each model offers unique insights and applications, with its own set of strengths and limitations.
Quant Finance · Risk Management · Finance
Michael Corral · Book
About the author: Michael Corral is an Adjunct Faculty member of the Department of Mathematics at Schoolcraft College. in Mathematics from the University of California at Berkeley, and received an M.A. in Industrial & Operations Engineering from the University of Michigan. This text was typeset in LATEX 2ε with the KOMA-Script bundle, using the GNU Emacs text editor on a Fedora Linux system.
Math · Quant Finance · Data Visualization
Aswath Damodaran · Slides
The only questions are how much and in which direction. • Truth 1.2: The direction and magnitude of the bias in your valuation is directly proportional to who pays you and how much you are paid. The more quantitative a model, the better the valuation • Truth 3.1: One’s understanding of a valuation model is inversely proportional to the number of inputs required for the model. • Truth 3.2: Simpler valuation models do much better than complex ones.
Corporate Finance · Risk Management · Quant Finance
CME Group · Guide
Treasury Futures Conversion Factors Find out how U.S. Treasury futures are standardized with conversion factors © 2024 CME Group. Treasury Futures Conversion Factors Each cash note or bond eligible for delivery into a Treasury futures contract is assigned a conversion factor, which considers its coupon and the time remaining until maturity as of a specific delivery month. The conversion factor represents the estimated decimal price at which $1 par value of the security would trade if it had a yield to maturity of 6%.
Quant Finance · Finance · Corporate Finance
Henri Poincaré · Book
Introduction 1 Topology before Poincar´e . 9 Comments on terminology and notation . Second definition of manifold . Oppositely oriented manifolds .
Math · Computer Science · Data Visualization
Alexandre Landi · Slides
Discounting is the process of determining the present value of a future amount of money or stream of cash flows given a specific interest rate. r is the continuous compounding rate (or yield). t is the time in years until the payment is made. Discounting reflects the time value of money, capturing how the value of a future payment decreases with time.
Finance · Quant Finance · Corporate Finance
Central Bank of the U.A.E. · Guide
3/70 CBUAE Classification: Public TABLE OF CONTENTS Definitions and interpretations .................................................................................................. 5 1 Context and Objective ...................................................................................................... 11 1.1 Regulatory context .................................................................................................... 11 1.2 Objectives .................................................................................................................
Software Engineering · Quant Finance · Risk Management
Andrija Djurovic · Slides
Validation of Credit Risk Models Does the P-Value Provide Sufficient Insight for Model Validation? The p-value resulting from statistical hypothesis testing is often the sole criterion used in reaching a final conclusion. Relying solely on the p-value raises several questions, such as: Should practitioners adopt a unified approach based on the p-value for all portfolio types? Should practitioners adopt a unified approach based on the p-value for all test types?
Risk Management · Quant Finance · Finance
Marc Deisenroth & Stefanos Zafeiriou · Report
5 1.2.1 Means and Covariances . 6 1.2.1.1 Sum of Random Variables . 7 1.2.1.2 Affine Transformation . 7 1.2.2 Statistical Independence .
Statistics · Math · Machine Learning
Christopher Mutschler, Christian Münzenmayer, Norman Uhlmann & Alexander Martin · Book
This book is an open access publication. The images or other third party material in this book are included in the book’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the book’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. The use of general descriptive names, registered names, trademarks, service marks, etc.
AI · Machine Learning · Math
Andrew Lesniewski · Slides
We have seen that a stationary time series in the ARMA(p, q) family can be written in the moving average (MA) form: Xt = µ + εt + γ1εt−1 + γ2εt−2 . = µ + γ(L)εt, (1) where L is the lag operator, and where P∞ j=1 |γj| < ∞. Stationary series are rather unusual in finance, and hence the need for developing models that capture the non-stationary nature of financial time series. There are various approaches to model non-stationarity.
Data Visualization · Risk Management · Python
Cole Nussbaumer Knaflic · Book
A guide based on Cole Nussbaumer Knaflic's influential book on effective data communication. It covers how to choose appropriate visuals, eliminate clutter, and use narrative structure to transform data into compelling stories for business audiences.
Data Visualization · Python · Statistics
Mehul Mehta · Slides
• It is a type of short-rate model, which means it focuses on modeling the short-term interest rate, or the instantaneous rate of return on a risk-free investment.
Quant Finance · Risk Management · Finance
Eric Bonabeau · Report
about the aircraft’s speed and acceleration. This confused the flight computers, which sent the Boeing 777 on a 3,000-foot roller-coaster ride. With more than five million lines of code, aircraft software programs have become too large and complex to be tested thoroughly and are fielded without any guarantee that they will always work. Comment on this article or contact the author through smrfeedback@mit.edu.
Risk Management · Finance · Quant Finance
Hayder Zaeem · Report
4 ○Modules ○Algebras ○Galois Theory: Study of symmetries in algebraic equations. ●Boolean Algebra: Algebraic structure used in logic and computer science.
Math · Data Visualization · Computer Science
Samuel Atkins, Ali Fathi & Sammy Assef · Report
Abstract A corporate bond trader in a typical sell side institution such as a bank provides liquidity to the market participants by buying/selling securities and maintaining an inventory. Upon receiving a request for a buy/sell price quote (RFQ), the trader provides a quote by adding a spread over a prevalent market price. For illiquid bonds, the market price is harder to observe, and traders often resort to available benchmark bond prices (such as MarketAxess, Bloomberg, etc.). In [BG], the concept of Fair Transfer Price for an illiquid corporate bond was introduced which is derived from...
Quant Finance · Risk Management · Finance
Amit Kumar Jha · Book
Introduction to Financial Markets 2 1.1 Stock Market . 6 2.2.8 AT1 (Additional Tier 1) Bonds .
Quant Finance · Finance · Corporate Finance
Antti Ilmanen & Rafey Sayood · Report
We review the performance of increas- ingly complex yet quite straightforward and transparent trading strategies. We first use single indicators to predict specific trades. We then pool these indicators into a multipredictor fore- casting model for each trade, and finally diver- sify across several trades. The success of these quantitative trading strategies rests on the twin pillars of the lim- ited forecastability of returns and diversifica- tion across strategies.
Quant Finance · Finance · Risk Management
Vaishali B. Bhagat · Book
BHAGAT Copyright © 2024 by Vaishali B. No part of this book may be used or reproduced in any form whatsoever without written permission except in the case of brief quotations in critical articles or reviews.
Python · Data Visualization · Software Engineering
Kavi & Shila · Book
A beginner-friendly tutorial on the Pandas library in Python for data manipulation and analysis. It covers fundamental operations such as creating DataFrames, reading CSV files, filtering data, and performing basic statistical summaries.
Python · Data Visualization · Machine Learning
Joakim Sundnes · Book
This book is an open access publication. The images or other third party material in this book are included in the book's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the book's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. The use of general descriptive names, registered names, trademarks, service marks, etc.
Python · Computer Science · Statistics
Rajender Kumar · Book
No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the copyright owner. This book is sold subject to the condition that it shall not, by way of trade or otherwise, be lent, resold, hired out, or otherwise circulated without the publisher's prior consent in any form of binding or cover other than that in which it is published and without a similar condition including this condition being imposed on the subsequent purchaser. Trad...
Machine Learning · Python · Statistics
Allen B. Downey · Book
Downey Think Python by Allen B. Downey Copyright © 2024 Allen Downey. Printed in the United States of America. Published by O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA 95472.
Python · Data Visualization · Math
The OpenEDG Python Institute · Book
About this eBook While ePUB is an open standard widely employed for the publication of electronic books, support and features may vary from one device to another. Every effort has been made to ensure that this book will display faithfully on all devices, but it may be necessary to adjust the settings of your particular device for optimum readability. There are many examples of code employed throughout this book. Due to the flowing nature of text in the ePUB format, this code, which is written line by line, may not always display correctly.
Python · Data Visualization · Math
Author Unknown · Book
Purpose Chapter 2 for beginners 1. Python uses indentation for code blocks 2. Variables are dynamically typed 3. Using snake_case Naming Conventions 4.
Python · Computer Science · Math
Jonathan Wayna Korn · Book
An introduction to implementing machine learning algorithms in Python using libraries such as scikit-learn. It covers key concepts including data preprocessing, model training, evaluation metrics, and common supervised and unsupervised learning techniques.
Machine Learning · Python · Data Visualization
Author Unknown · Guide
Warning: If you find errors, please raise an issue or contribute a better definition!
Data Visualization · Math · SQL
Peter Jackel · Report
An inadvertent consequence to this convention is that each day is assigned the same amount of future daily variance of the underlying financial asset’s future spot realisation. This, alas, does not reflect the real world. Whilst this approach is reasonably well known among praction- ers, it rarely appears in the literature, one exception be- ing [SV00]. For the respective trading desks, the BUS252 volatility day count convention is pragmatic and easy to use, though it has its drawbacks.
Risk Management · Quant Finance · Finance
Andrei Kucharavy, Octave Plancherel, Valentin Mulder, Alain Mermoud & Vincent Lenders · Book
This book is an open access publication. The images or other third party material in this book are included in the book’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the book’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. The use of general descriptive names, registered names, trademarks, service marks, etc.
Machine Learning · Risk Management · Computer Science
William Astle · Slides
to think, understand, and form judgements logically.
Statistics · Risk Management · Data Visualization
Aaron de la Rosa · Notes
Definition and Features: • A swaption (swap + option) gives the holder the right, but not the obligation, to enter into an interest rate swap at specified terms. • In a Bermudan swaption, the holder has the right to start the swap on any of several predetermined dates within a specific period, known as "exercise dates." These dates are usually aligned with the reset dates of the swap's floating leg. • This flexibility to exercise on multiple dates differentiates Bermudan swaptions from European swaptions (exercisable only on a single date) and American swaptions (exercisable on any date up ...
Quant Finance · Risk Management · Finance
Thomas R. Robinson, Elaine Henry, Wendy L. Pirie & Michael A. Broihahn · Book
Since 1963 the organization has developed and ad- ministered the renowned Chartered Financial Analyst® Program. With a rich history of leading the investment profession, CFA Institute has set the highest standards in ethics, education, and professional excellence within the global investment community and is the foremost authority on investment profession conduct and practice. Each book in the CFA Institute Investment Series is geared toward industry practition- ers along with graduate-level fi nance students and covers the most important topics in the industry. Th e authors of these cuttin...
Corporate Finance · Finance · Quant Finance
Roozbeh Sanaei · Report
11 3.1.2 Different Algorithms in ICA . 14 3.1.5 Fast Independent Component Analysis . 17 3.2.2 Comparative Analysis of SNE, t-SNE, and UMAP . 18 3.2.3 SNE, t-SNE and UMAP Comparison .
Machine Learning · Math · Computer Science
Gerhard Paaß & Sven Giesselbach · Book
It brings together the latest developments in all areas of this multidisciplinary topic, ranging from theories and algorithms to various important applications. Furthermore, it supports Open Access publica- tion mode. This book is an open access publication. The images or other third party material in this book are included in the book’s Creative Commons license, unless indicated otherwise in a credit line to the material.
AI · Machine Learning · Software Engineering
Kyoung-Yun Kim, Leslie Monplaisir & Jeremy Rickli · Book
This book is an open access publication, corrected publication 2023. The images or other third party material in this book are included in the book's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the book'
Machine Learning · AI · SQL
Alex G. Kim, Maximilian Muhn & Valeri V. Nikolaev · Report
Abstract We investigate whether an LLM can successfully perform financial statement analy- sis in a way similar to a professional human analyst. We provide standardized and anonymous financial statements to GPT4 and instruct the model to analyze them to determine the direction of future earnings. Even without any narrative or industry- specific information, the LLM outperforms financial analysts in its ability to predict earnings changes. The LLM exhibits a relative advantage over human analysts in sit- uations when the analysts tend to struggle.
Risk Management · Machine Learning · Corporate Finance
rearranging terms and ignoring higher-order terms, we can approximate the first derivative as: · Report
Introduction This chapter introduces the fundamental terminologies of Finite Difference Methods (FDM), provid- ing the reader with the essential context. It covers key approximation techniques, including forward, backward, and central difference methods. 1.1 Introduction to FDM Finite Difference Methods (FDM) are widely used numerical techniques for solving differential equa- tions. By approximating derivatives using finite differences, these methods convert differential equa- tions into systems of algebraic equations that can be solved numerically.
Math · Quant Finance · Computer Science
Author Unknown · Slides
A study of how pairs trading strategies have evolved since their origins at Morgan Stanley in the 1980s. It traces the development from simple correlation-based approaches to modern cointegration and algorithmic methods used in statistical arbitrage.
Quant Finance · Risk Management · Finance
Author Unknown · Guide
Basics of Python Programming Chapter 2. Python Control Structures Chapter 3. Python Functions and Modules Chapter 4. Python Data Structures Chapter 5.
Python · Software Engineering · Data Visualization
Aswath Damodaran · Slides
Proposition 1: For an asset to have value, the expected cash flows have to be positive some time over the life of the asset. Proposition 2: Assets that generate cash flows early in their life will be worth more than assets that generate cash flows later; the latter may however have greater growth and higher cash flows to compensate.
Corporate Finance · Risk Management · Quant Finance
Melissa Terras, Vikki Jones, Nicola Osborne & Chris Speed · Book
The exploration of innovation in this collection of essays champions the collaborative potential of creativity and technology and encourages the adoption of data-led creativity to shape the future of the creative economy. A must-read for those looking to drive meaningful change and innovation across the industry.” Lee Walters, CEO of Ffilm Cymru Wales. Formerly Programme Manager of Clwstwr, part of the Creative Industries Clusters Programme “This book brings to life real-world applications of data-led creativity and, importantly, addresses ethical considerations. But what do these mean in t...
Data Visualization · Computer Science · Python
Paul Hsiao & Jason Draho · Slides
History shows that elections serve as a “risk clearing” event for equities, with performance driven more by macro and financial conditions than by election outcomes. Markets & Deal Activity • Good macro has lifted equities, but that strength and election uncertainty is fueling rate volatility. Large rotations below the surface (e.g., cyclicals vs. defensives) are likely continue as the macro narrative evolves.
Finance · Corporate Finance · Quant Finance
Yang Hu · Notes
Algorithms Python YANG HU Simple is the beginning of wisdom. From the essence of practice, this book to briefly explain the concept, and vividly cultivate programming interest , you will learn it easy fast and well.
Python · Computer Science · Machine Learning
Andrija Djurovic · Slides
After reducing the data dimensionality, the selected principal components typically serve as inputs for the regression model. In the context of IFRS9 forward-looking modeling, PCA is a notable approach practitioners employ. PCA addresses a significant challenge in forward-looking modeling exercises: the relatively low ratio between the number of observations and the number of independent variables. Despite its popularity, is PCA always the optimal solution?
Risk Management · Corporate Finance · Quant Finance
@mjbaldbard · Report
Going through these notes without doing so won’t be as helpful since you’ll lack the basic context in which the underlying thoughts were penned. • In addition to the original thoughts, these notes contain certain takeaways, inputs & charts. Please reach out if you’ve further insights on any of those. • Bear in mind that the source content was published in the late 80’s and early 90’s.
Finance · Quant Finance · Corporate Finance
Jay Cao, Jacky Chen & John Hull · Report
Abstract We employ neural networks to understand volatility surface movements. We first use daily data on options on the S&P 500 index to derive a relationship between the expected change in implied volatility and three variables: the return on the index, the moneyness of the option, and the remaining life of the option. This model provides an improvement of 10.72% compared with a simpler analytic model. We then enhance the model with an additional feature: the level of the VIX index prior to the change being observed.
Quant Finance · Risk Management · Machine Learning
Aaron de la Rosa · Notes
It allows the conditional-on-past-history covariance matrix of the dependent variables to follow a flexible dynamic structure. This means that MGARCH models can capture the time-varying volatility and co-movements in multiple time series. Financial institutions typically use them to estimate the volatility of returns for stocks, bonds, and market indices. DCC (Dynamic Conditional Correlation): DCC is a specific type of MGARCH model.
Finance · Data Visualization · Risk Management
A. Taylan Cemgil · Slides
Introduction, Course structure, Motivating Examples, Applications Department of Computer Engineering, Bo˘gazic¸i University, Istanbul, Turkey Instructor: A. Taylan Cemgil Cemgil CMPE 58N Monte Carlo Methods. , Bo˘gazic¸i University, Istanbul Main study materials ▶Handouts, Papers ▶Jun S. Liu, Monte Carlo Strategies in Scientific Computing, 2001, Springer.
Math · Quant Finance · Statistics
Richard B. Levin, Kevin Tran & Robert Wenner · Guide
Law Business Research is not responsible for any actions (or lack thereof) taken as a result of relying on or in any way using information contained in this report and in no event shall be liable for any damages resulting from reliance on or use of this information.
Finance · Corporate Finance · Risk Management
Lehman Brothers Holdings INC. · Book
INTRODUCTION TO THE FINANCING MARKET Basics, Terminology, Risks, Legal Ill. ACCOUNT SUITABILITY Suitability, Trade Maintenance, Exposure IV. INFINITY QUICK REFERENCE GUIDE Front-End Repo Trade Booking System VII. MTS VERBS/ COMl\tlANDS Verbs and Commands to Mainframe System VIII.
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Jeroen Kerkhof · Report
overview .......................................................................................................8 3. Euro area......................................................................................................................9 3.2. France........................................................................................................................10 3.3. United Kingdom........................................................................................................11 3.4.
Quant Finance · Risk Management · Finance
Aurum · Report
**Bonds = Bloomberg Global Aggregate Bond Index. Risk Free Rate = period average of 3-month LIBOR-SOFR. All figures and charts use asset weighted returns unless otherwise stated. All Hedge Fund data is sourced from Aurum Hedge Fund Data Engine.
Finance · Corporate Finance · Quant Finance
Brendon J. Brewer · Book
Introduction to Bayesian Statistics Brendon J. Brewer This work is licensed under the Creative Commons Attribution-ShareAlike 3.0 Unported License. 5 1.2 This Version of the Notes . 12 3.1.2 Finding the Likelihood Values .
Statistics · Risk Management · Quant Finance
David Nincic · Guide
overview of qualitative and quantitative fundamental research methods, with a comprehensive survey of valuation techniques. The framework provided in the Primer should prove useful to those who wish to present stock recommendations, either verbally or in written form. We urge those with an interest in stock picking to present their investment ideas to the Wharton Fellows Fund. We hope that the Primer will be a valuable resource in this regard.
Corporate Finance · Finance · Quant Finance
Author Unknown · Notes
Brownian Motion: The Foundation When you look at a stock’s price over time, it doesn't move in a straight line. Instead, it fluctuates, moving up and down in an unpredictable manner. This kind of movement can be modeled using Brownian Motion, a fundamental stochastic process. Brownian Motion models the random movement observed in particles suspended in a fluid, and in finance, it models the random fluctuations of asset prices over time.
Math · Quant Finance · Statistics
Álvaro Cartea · Slides
A learning algorithm consists of An objective, e.g., maximise profits, minimise costs. Offline with historical data Online as market evolves Both: offline and online What key aspects affect the performance of the learnt strategies?
Quant Finance · Machine Learning · Risk Management
Tim Fingscheidt, Hanno Gottschalk & Sebastian Houben · Book
This book is an open access publication. The images or other third party material in this book are included in the book’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the book’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. The use of general descriptive names, registered names, trademarks, service marks, etc.
Machine Learning · AI · Computer Science
Nicholas Johnson, John Kerpel & Jonathan Kronstein · Report
"Understanding Treasury Futures" by CME Group (November 2017) is a primer on trading U.S. Treasury bond and note futures. It starts with the cash Treasury market: coupon securities, the price/yield relationship, quotation, accrued interest and settlement, the auction cycle, the on-the-run issue and repo financing. It then explains how futures relate to cash through delivery practices, the conversion-factor invoicing system, the cheapest-to-deliver issue, the basis, the implied repo rate and the delivery options, and closes with hedging: basis point value and duration, conversion-factor and BPV-weighted hedges, crossover risk and portfolio hedging.
Finance · Quant Finance · Corporate Finance
Alexander Roberts, William Wallace & Peter Moles · Report
Covers the foundational concepts of mergers and acquisitions in corporate finance, including deal structures, valuation methods, synergy analysis, and financing mechanisms. Discusses the distinction between mergers and acquisitions, the M&A process lifecycle, due diligence, and strategic rationales for corporate combinations.
Corporate Finance · Finance
Lehman Brothers Holdings, Inc. · Book
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Finance · Quant Finance · Corporate Finance
Yashavant Kanetkar & Aditya Kanetkar · Book
No part of this publication can be stored in a retrieval system or reproduced in any form or by any means without the prior written permission of the publishers. LIMITS OF LIABILITY AND DISCLAIMER OF WARRANTY The Author and Publisher of this book have tried their best to ensure that the programmes, procedures and functions described in the book are correct. However, the author and the publishers make no warranty of any kind, expressed or implied, with regard to these programmes or the documentation contained in the book. The author and publisher shall not be liable in any event of any damag...
Python · Data Visualization · Math
Behnam Khani · Book
About the author Hello My name is Behnam Khani. I’m a software engineer with 10 years of experience in the industry. I have a passion for technology, education, and software development and enjoy combining the three. This book and my website dejavucode.com are places to share my knowledge and experiences!
Python · Data Visualization · Machine Learning
Chris Ford · Book
Data Types in Python What Are Data Types? Understanding Data Types Type Casting 5. Lists and Tuples What Is a List Data Type?
Python · Data Visualization · Machine Learning
Mike McGrath · Book
No part of this book may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or by any information storage or retrieval system, without prior written permission from the publisher. Notice of Liability Every effort has been made to ensure that this book contains accurate and current information. However, In Easy Steps Limited and the author shall not be liable for any loss or damage suffered by readers as a result of any information contained herein. Trademarks All trademarks are acknowledged as belonging to their respective ...
Python · Data Visualization · Machine Learning
Nicholas Burgess · Slides
Hospitals, Transport (HS2), Energy & Defence Projects Interest Rate Markets – Why the need for Speed? 4 Cleared Electronic Trading & Auto-Hedging Real-Time, Highly Liquid & High Precision (Bid-Offer 1/10th bps i.e. USD 10 per MM) Trading Horizon: High Frequency Trading (HFT) vs Long-Term Fund Performance
Finance · Risk Management
Adrienne Watt & Nelson Eng · Book
Database Design - 2nd Edition by Adrienne Watt and Nelson Eng is licensed under a Creative Commons Attribution 4.0 International License, except where otherwise noted. If you redistribute all or part of this book, you must include the following on the copyright notice page: Download for free from the B.C. Sample APA-style citation: This textbook can be referenced. In APA citation style, it would appear as follows: Watt, A.
Software Engineering · SQL · Data Visualization
Adrian Rosebrock · Book
Deep Learning for Computer Vision with Python Practitioner Bundle Dr. Books like this are made possible by the time invested by the authors. First printing, September 2017 To my father, Joe; my wife, Trisha; and the family beagles, Josie and Jemma. Without their constant love and support, this book would not be possible.
Machine Learning · Python · Computer Science
Leonardo Giordani · Book
6 Prerequisites and structure of the book. 7 Why this book comes for free . 7 Submitting issues or patches. 8 Changes in the second edition.
Software Engineering · Python · Data Visualization
Amit Kumar Jha · Report
Introduction In this short pdf, I am breaking down the calibration of option pricing models and interest rate models step by step, focusing on clear and practical methods for understanding and implementing these processes. We’ll cover key models such as the Black-Scholes model, LV model, Vasicek model, CIR and HW Model. Data Collection • Market Prices of Options (Cmarket): These are the observed prices of options traded in the market. We collect these because they represent the ”true” value of the options under current market conditions.
Quant Finance · Risk Management · Finance
John Soldatos & Dimosthenis Kyriazis · Book
Preface The finance sector is among the most data-savvy and data-intensive of the global economy. The ongoing digital transformation of financial organizations, along with their interconnection as part of a global digital finance ecosystem, is producing petabytes of structured and unstructured data. The latter represent a significant opportunity for banks, financial institutions, and financial technology firms (Fin- Techs): Leveraging these data financial organizations can significantly improve both their business processes and the quality of their decisions. As a prominent example, modern banks can...
AI · Quant Finance · Machine Learning
FinShiksha · Slides
Or one that has listed recently? A company coming with an IPO, or a recently listed one, does not have the price history to calculate Beta. So it may be incorrect to take a simple average of betas of sector peers Can we take sector average?
Corporate Finance · Risk Management · Quant Finance
Andrea Giussani · Book
Via Salasco, 5 - 20136 Milano Tel.
Python · Machine Learning · Math
Ian Langmore & Daniel Krasner · Book
26 5.2 Coefficient Estimation: Bayesian Formulation . 29 5.2.2 Ideal Gaussian World . 30 5.3 Coefficient Estimation: Optimization Formulation . 33 5.3.1 The least squares problem and the singular value de- composition .
Python · Data Visualization · Computer Science
Author Unknown · Report
Introduction to Barrier Options 2. These options are particularly useful in cases where the buyer is only interested in payoffs under certain conditions. This fea- ture makes barrier options more cost-effective than vanilla options, as certain outcomes are excluded. Barrier options offer a cost-saving alternative to vanilla options.
Risk Management · Finance
D. Sanz-Alonso & O. Al-Ghattas · Book
A First Course in Monte Carlo Methods D. Here, B10000 = 7854 draws fell within the unit circle, leading to an estimate bπ10000 = 3.1416. 2 2.1 A strictly increasing c.d.f. 10 2.2 Inverse transformation method for sampling from an Exponential(1) distribu- tion.
Quant Finance · Math · Statistics
Steve Blank · Guide
about the competitive edge they’d have by today in business or as a nation. That’s where we are today with Artificial Intelligence and Machine Learning. These technologies will transform businesses and government agencies. Today, 100s of billions of dollars in private capital have been invested in 1,000s of AI startups.
Machine Learning · AI · Statistics
S. David Promislow · Book
The right of the author to be identified as the author of this work has been asserted in accordance with the Copyright, Designs and Patents Act 1988. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by the UK Copyright, Designs and Patents Act 1988, without the prior permission of the publisher. Wiley also publishes its books in a variety of electronic formats. Some content that appears in print may not be available in electronic books.
Math · Quant Finance · Finance
Riccardo Boffo, Hugh Miller, Juan Pavajeau Fuentes, Giulio Mazzone, Geraldine Ang · Report
This OECD report presents a methodological supervisory framework to help central banks and financial supervisors assess biodiversity-related financial risks within their financial sectors. It outlines a four-step approach covering risk identification and prioritization, economic risk assessment, financial risk transmission channels (credit, market, and liquidity risks), and supervisory considerations. The framework translates ecosystem service degradation into quantifiable financial risks while accounting for interconnections with climate change and broader environmental degradation.
Finance · Risk Management · Corporate Finance
Gyre Research · Gyre Publications
A practical guide for managing Claude Code projects using a living specification document (SPEC.md) instead of conversational chat. The document addresses the problem of context window limits causing Claude to lose track of prior decisions and requirements, and proposes consolidating all project knowledge into a structured specification file that is read at the start of each session. It includes a detailed SPEC.md template with sections for requirements, Q&A logs, decision records, and implementation status tracking.
AI · Software Engineering
Gyre Research · Gyre Publications
This paper outlines five high-ROI AI projects that small-to-mid-size institutional managers ($100M-$5B AUM) can deploy quickly with existing data and a small team. The five projects are: automated data quality and pipeline monitoring, LLM-powered regulatory filing analysis, intelligent trade reconciliation and exception management, AI-generated client reporting and commentary, and real-time portfolio risk anomaly detection. Each project includes detailed problem statements, proposed solutions using modern ML and LLM techniques, and concrete implementation timelines.
AI · Quant Finance · Risk Management
Tutorials Point · Book
A comprehensive tutorial from Tutorials Point covering the fundamentals of machine learning using Python. It introduces key ML concepts, the Python ecosystem for data science, methods and tasks suited for machine learning, data loading and preprocessing, and practical implementation of algorithms using NumPy, Scikit-learn, SciPy, and Matplotlib.
Machine Learning · Python · Computer Science
Gyre Research · Gyre Publications
This working paper reports the Gyre Research study of asymmetric beta in S&P 500 stocks. It tested two hypotheses: that a stock's asymmetry lasts long enough to exploit in a long/short equity portfolio, and that short-term asymmetry reverts in a way that can be traded. Both were tested on S&P 500 history back to 1992, survivorship-free, with every test fixed before it ran, and both were rejected. What held up is ordinary beta. The study's daily calculation stays published as a research tool: each S&P 500 stock's beta against SPY, the equal-weight S&P 500, the cap-weighted S&P 500 rebuilt from each day's members, its own sector or its own size tier, over time frames from 10 days to 2 years, with up- and down-beta and their 95% intervals.
Quant Finance · Risk Management · Finance
Gyre Research · Gyre Publications
This paper defines factor exposure drift — the gap between intended factor exposures at rebalance dates and the realized exposures portfolios actually carry mid-period. It decomposes drift into price-driven weight drift, security-level factor score evolution, and corporate event effects, then proposes a practical continuous monitoring framework so equity portfolio managers can identify uncompensated risk that traditional rebalance-only reporting misses.
Quant Finance · Risk Management · Finance
Gyre Research · Gyre Publications
This Gyre Research piece walks through the five most common performance reporting gaps observed across investment teams: inconsistent return calculations across systems, manual attribution that doesn't survive scrutiny, stale benchmarks, slow report production, and weak audit trails. Each gap is paired with a concrete diagnostic and a practical fix, so portfolio analytics and operations teams can close them before they surface in client conversations or regulatory inquiries.
Finance · Risk Management
Gyre Research · Gyre Publications
This paper examines why manual reconciliation quietly becomes a scaling ceiling for mid-size investment operations teams. It catalogs five recurring failure modes — silently compounding reconciliation errors, operational bandwidth lost to routine breaks, and others — and lays out an automation roadmap centered on daily automated position and cash comparisons, exception-based human review, and incremental rollout across custodian relationships.
Finance · Risk Management
Eugene F. Fama and Kenneth R. French · Report
A seminal survey paper reviewing the CAPM from its theoretical foundations through the empirical record. Fama and French document the failures of the Sharpe-Lintner model in explaining cross-sectional returns and motivate the three-factor extension. Essential reading for anyone using beta, alpha, or factor-based attribution.
Quant Finance · Finance · Risk Management
William F. Sharpe · Report
William Sharpe's 1990 Nobel Prize lecture summarizing his contributions to the CAPM and extending the framework to portfolios with short positions. Authoritative primary-source treatment of the model that earned Sharpe the prize.
Quant Finance · Finance · Risk Management
Fischer Black, Michael C. Jensen, and Myron Scholes · Report
The classic 1972 empirical test of the CAPM that first documented the low-volatility anomaly — a persistent premium to low-beta stocks that standard CAPM cannot explain. Foundational reading for anyone working with factor models or systematic risk.
Quant Finance · Finance · Risk Management
Darrell Duffie (Stanford GSB) · Report
Duffie's authoritative summary of the intellectual contributions of Black, Merton, and Scholes to economics and finance — covering the Black-Scholes-Merton option pricing framework, its extensions, and its impact on practice. Written on the occasion of the 1997 Nobel Prize.
Quant Finance · Finance
Heinz H. Müller · Report
ASTIN Bulletin survey article covering the main results of modern portfolio theory, the Sharpe-Lintner CAPM, and Roll's critique. Accessible treatment of the mathematical foundations.
Quant Finance · Finance · Risk Management
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, Illia Polosukhin (Google Brain) · Report
The 2017 NeurIPS paper that introduced the Transformer architecture — the foundation of every modern large language model. Dispensing with recurrence and convolutions entirely, the Transformer uses multi-head self-attention to parallelize sequence modeling. The single most-cited deep learning paper of the last decade.
AI · Machine Learning · Computer Science
Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova (Google AI Language) · Report
The 2018 paper introducing BERT, which established the pre-train/fine-tune paradigm that dominated NLP before the GPT era. Masked language modeling + next-sentence prediction on unlabeled text produced representations transferable to a wide range of downstream tasks.
AI · Machine Learning · Computer Science
Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun (Microsoft Research) · Report
The 2015 paper introducing residual connections and ResNet — the architecture that enabled training of networks 100+ layers deep. Residual connections solved the degradation problem in deep networks and became a near-universal building block of modern deep learning.
AI · Machine Learning · Computer Science
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, Ruslan Salakhutdinov (University of Toronto) · Report
The 2014 JMLR paper introducing dropout as a regularization technique. Randomly dropping units during training prevents co-adaptation and acts as an approximate ensemble method. A foundational technique used in nearly every deep neural network.
AI · Machine Learning · Statistics
Diederik P. Kingma and Jimmy Ba · Report
The 2015 paper introducing the Adam optimizer, combining adaptive per-parameter learning rates with momentum. Became the default optimizer for deep learning and remains the standard choice for training large models.
AI · Machine Learning · Math
Sergey Ioffe and Christian Szegedy (Google) · Report
The 2015 paper introducing batch normalization — a technique that normalizes layer inputs during training, dramatically accelerating convergence and enabling higher learning rates. Standard component of modern CNN and Transformer architectures.
AI · Machine Learning · Statistics
Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton (University of Toronto) · Report
The 2012 AlexNet paper that catalyzed the deep learning revolution. A deep CNN trained on two GPUs reduced the ImageNet top-5 error rate from 26% to 15%, convincing the broader ML community that deep networks were production-ready.
AI · Machine Learning · Computer Science
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio · Report
The 2014 paper that introduced GANs — a framework where a generator and discriminator are trained in a minimax game. Opened the era of modern generative modeling and led directly to the image synthesis breakthroughs of the late 2010s.
AI · Machine Learning · Statistics
Tom B. Brown et al. (OpenAI) · Report
The 2020 GPT-3 paper demonstrating that scale alone — 175 billion parameters — unlocks few-shot in-context learning. The paper that transformed the commercial viability of large language models and kicked off the modern LLM era.
AI · Machine Learning
Long Ouyang et al. (OpenAI) · Report
The 2022 InstructGPT paper establishing the RLHF recipe: supervised fine-tuning on high-quality demonstrations, reward model training from human preferences, and PPO optimization. The 1.3B InstructGPT beat the 175B GPT-3 on human preference — proof that alignment mattered more than scale.
AI · Machine Learning
Jordan Hoffmann et al. (DeepMind) · Report
The 2022 Chinchilla paper that corrected Kaplan's scaling laws. Trained 400+ models from 70M to 16B parameters, showing that model size and training tokens should scale equally — a 70B Chinchilla with 4× more data beat 280B Gopher. Reshaped the training strategy for every subsequent frontier LLM.
AI · Machine Learning · Statistics
Hugo Touvron et al. (Meta AI) · Report
The 2023 paper releasing LLaMA — a family of 7B–65B parameter models trained on publicly available data. LLaMA-13B outperformed GPT-3 (175B) on most benchmarks. Opened the era of capable open-weight LLMs and enabled the explosion of open-source LLM work.
AI · Machine Learning
Yuntao Bai et al. (Anthropic) · Report
The 2022 Constitutional AI paper introducing RLAIF — reinforcement learning from AI feedback — as a scalable alternative to RLHF. Models critique and revise their own outputs against a set of written principles. The foundation of Claude's training methodology.
AI · Machine Learning
Patrick Lewis et al. (Facebook AI Research) · Report
The 2020 paper that formalized RAG — combining a parametric seq2seq model with a non-parametric retrieval component. The architecture underlying most production LLM deployments that need current or private knowledge.
AI · Machine Learning
Jared Kaplan et al. (OpenAI) · Report
The 2020 Kaplan paper establishing empirical scaling laws for language models: loss is predictable as a power law in model size, dataset size, and compute. The paper that convinced OpenAI to bet on scale and led directly to GPT-3.
AI · Machine Learning · Statistics
Timo Schick et al. (Meta AI) · Report
The 2023 Toolformer paper showing how LLMs can be self-taught to invoke external APIs (calculator, search, translation) by inserting API call annotations into training data. A foundational paper in the agentic-LLM lineage.
AI · Machine Learning
Fischer Black and Myron Scholes · Report
The original 1973 Journal of Political Economy paper that introduced the Black-Scholes option pricing formula. One of the most consequential papers in modern finance, laying the foundation for derivatives markets and quantitative trading.
Quant Finance · Finance
Harry M. Markowitz · Report
Markowitz's 1990 Nobel Prize lecture presenting the foundations of portfolio theory in his own words — from the 1952 diversification principle through mean-variance optimization and the efficient frontier. Definitive primary-source treatment by the inventor of modern portfolio theory.
Quant Finance · Finance · Risk Management
Frank Hutter, Lars Kotthoff, Joaquin Vanschoren (Editors) · Book
An edited volume in The Springer Series on Challenges in Machine Learning, covering methods, systems, and open challenges in automated machine learning (AutoML). Contributions span algorithmic foundations, hyperparameter optimization, neural architecture search, and real-world applications.
Machine Learning · AI · Computer Science
Book
A practical introduction to machine learning using Python, covering NumPy, SciPy, and Matplotlib for data handling; classification (kNN, naive Bayes), clustering, and feature engineering; hands-on examples on the Iris and Seeds datasets; techniques for evaluation and model selection.
Python · Machine Learning · Computer Science
Jim Dowling · Book
O'Reilly Media early-release (2025) on designing and operating ML systems built around a feature store. Covers feature engineering pipelines, online/offline serving, data consistency, and the architecture of modern ML platforms.
Machine Learning · Software Engineering · AI
Harry Deng · Report
UNIDIR (United Nations Institute for Disarmament Research) primer on synthetic data: how it is generated, why it is used in AI and autonomous systems, and the security and policy implications for militarized applications. Published 2023 under UNIDIR Security and Technology Programme.
AI · Machine Learning
Chuck Collins & Kalena Thomhave · Report
An Institute for Policy Studies (IPS) Inequality Briefing Paper (May 2021) examining how family offices accumulate and protect inherited wealth dynasties, their role in the shadow financial system, and the systemic risks they pose — drawing on the 2021 Archegos collapse. Argues for oversight of this under-regulated segment of the financial system.
Finance
Adam Smith · Book
Seminal book by Adam Smith (1776). Access: public-domain. Source: https://www.gutenberg.org/ebooks/3300
Finance · Economics · Trade
Thorstein Veblen · Book
Seminal book by Thorstein Veblen (1899). Access: public-domain. Source: https://www.gutenberg.org/ebooks/833
Finance
Benjamin Graham and David Dodd · Book
Seminal book by Benjamin Graham and David Dodd (1934). Access: public-access. Source: https://archive.org/details/bwb_W9-CMT-294
Finance
Benjamin Graham · Book
Seminal book by Benjamin Graham (1949). Access: public-access. Source: https://archive.org/details/is-the-intelligent-investor-really-the-best-investing-book-ever-written
Finance
Jim Gatheral · Book
Seminal book by Jim Gatheral (2006). Access: public-access. Source: https://archive.org/details/arxiv-1204.0646
Quant Finance
David Ricardo · Book
Seminal book by David Ricardo (1817). Access: public-domain. Source: https://www.econlib.org/library/Ricardo/ricP.html
Economics · Trade
Karl Marx · Book
Seminal book by Karl Marx (1867). Access: public-domain. Source: https://www.marxists.org/archive/marx/works/1867-c1/
Economics
William Stanley Jevons · Book
Seminal book by William Stanley Jevons (1871). Access: public-domain. Source: https://www.econlib.org/library/YPDBooks/Jevons/jvnPE.html
Economics
Alfred Marshall · Book
Seminal book by Alfred Marshall (1890). Access: public-domain. Source: https://www.econlib.org/library/Marshall/marP.html
Economics
Joseph A. Schumpeter · Book
Seminal book by Joseph A. Schumpeter (1942). Access: public-access. Source: https://archive.org/details/enduringtensionc0000devi
Economics
Karl Polanyi · Book
Seminal book by Karl Polanyi (1944). Access: public-access. Source: https://archive.org/details/the-great-transformation-karl-polanyi
Economics
F. A. Hayek · Paper
Seminal paper by F. A. Hayek (1945). Access: open-access. Source: https://www.econlib.org/library/Essays/hykKnw.html
Economics
Robert A. Mundell · Paper
Seminal paper by Robert A. Mundell (1961). Access: open-access. Source: https://www.aeaweb.org/aer/top20/51.4.657-665.pdf
Economics
Niccolo Machiavelli · Book
Seminal book by Niccolo Machiavelli (1532). Access: public-domain. Source: https://www.gutenberg.org/ebooks/1232
Public Policy
Thomas Hobbes · Book
Seminal book by Thomas Hobbes (1651). Access: public-domain. Source: https://www.gutenberg.org/ebooks/3207
Public Policy
Hamilton, Madison, and Jay · Book
Seminal book by Hamilton, Madison, and Jay (1788). Access: public-domain. Source: https://www.gutenberg.org/ebooks/1404
Public Policy
Alexis de Tocqueville · Book
Seminal book by Alexis de Tocqueville (1835). Access: public-domain. Source: https://www.gutenberg.org/ebooks/815
Public Policy
John Stuart Mill · Book
Seminal book by John Stuart Mill (1859). Access: public-domain. Source: https://www.gutenberg.org/ebooks/34901
Public Policy
John Dewey · Book
Seminal book by John Dewey (1927). Access: public-access. Source: https://archive.org/details/essentialdewey0000dewe
Public Policy
Graham Allison · Book
Seminal book by Graham Allison (1971). Access: public-access. Source: https://archive.org/details/DTIC_ADA440826
Public Policy
Deborah Stone · Book
Seminal book by Deborah Stone (1988). Access: public-access. Source: https://archive.org/details/cia-readingroom-document-cia-rdp93t00837r000400060002-2
Public Policy
Hans J. Morgenthau · Book
Seminal book by Hans J. Morgenthau (1948). Access: public-access. Source: https://archive.org/details/essentialreading0000unse_c2c2ndEd.
International Relations
Thomas C. Schelling · Book
Seminal book by Thomas C. Schelling (1966). Access: public-access. Source: https://archive.org/details/conditional_viability_states_bargaining-problems
International Relations
Geoffrey Blainey · Book
Seminal book by Geoffrey Blainey (1973). Access: public-access. Source: https://archive.org/details/DTIC_ADA314773
International Relations
Lawrence Freedman · Book
Seminal book by Lawrence Freedman (1981). Access: public-access. Source: https://archive.org/details/ExtrasensoryPerceptionResearchFinding
International Relations
Robert Axelrod · Book
Seminal book by Robert Axelrod (1984). Access: public-access. Source: https://archive.org/details/DTIC_ADA151801
International Relations
Henry Kissinger · Book
Seminal book by Henry Kissinger (1994). Access: public-access. Source: https://archive.org/details/TLAV-31Aug2018
International Relations
Thucydides · Book
Seminal book by Thucydides (-431). Access: public-domain. Source: https://www.gutenberg.org/ebooks/7142
International Relations
Henry George · Book
Seminal book by Henry George (1886). Access: public-domain. Source: https://www.gutenberg.org/ebooks/40196
Trade
Dani Rodrik · Book
Seminal book by Dani Rodrik (2011). Access: public-access. Source: https://archive.org/details/bmi-leak-merged
Trade
Claude E. Shannon · Paper
Seminal paper by Claude E. Shannon (1948). Access: open-access. Source: https://people.math.harvard.edu/~ctm/home/text/others/shannon/entropy/entropy.pdf
Programming
C. A. R. Hoare · Paper
Seminal paper by C. A. R. Hoare (1978). Access: open-access. Source: https://www.cs.cmu.edu/~crary/819-f09/Hoare78.pdf
Programming
Brian W. Kernighan and Dennis M. Ritchie · Book
Seminal book by Brian W. Kernighan and Dennis M. Ritchie (1978). Access: public-access. Source: https://archive.org/details/bliss-11-a-lesson-in-object-code-optimization
Programming
Frederick P. Brooks Jr. · Paper
Seminal paper by Frederick P. Brooks Jr. (1986). Access: open-access. Source: https://worrydream.com/refs/Brooks-NoSilverBullet.pdf
Programming
Gamma, Helm, Johnson, and Vlissides · Book
Seminal book by Gamma, Helm, Johnson, and Vlissides (1994). Access: public-access. Source: https://archive.org/details/dpatternsshort
Programming
Alan M. Turing · Paper
Seminal paper by Alan M. Turing (1936). Access: open-access. Source: https://www.cs.virginia.edu/~robins/Turing_Paper_1936.pdf
Computer Science
Leslie Lamport · Paper
Seminal paper by Leslie Lamport (1978). Access: open-access. Source: https://lamport.azurewebsites.net/pubs/time-clocks.pdf
Computer Science
Leslie Lamport, Robert Shostak, and Marshall Pease · Paper
Seminal paper by Leslie Lamport, Robert Shostak, and Marshall Pease (1982). Access: open-access. Source: https://lamport.azurewebsites.net/pubs/byz.pdf
Computer Science
Saltzer, Reed, and Clark · Paper
Seminal paper by Saltzer, Reed, and Clark (1984). Access: open-access. Source: https://web.mit.edu/Saltzer/www/publications/endtoend/endtoend.pdf
Computer Science
Rumelhart, Hinton, and Williams · Paper
Seminal paper by Rumelhart, Hinton, and Williams (1986). Access: open-access. Source: https://www.cs.toronto.edu/~hinton/absps/naturebp.pdf
Machine Learning
LeCun et al. · Paper
Seminal paper by LeCun et al. (1998). Access: open-access. Source: http://yann.lecun.com/exdb/publis/pdf/lecun-98.pdf
Machine Learning
Pedro Domingos · Paper
Seminal paper by Pedro Domingos (2012). Access: open-access. Source: https://homes.cs.washington.edu/~pedrod/papers/cacm12.pdf
Machine Learning
Krizhevsky, Sutskever, and Hinton · Paper
Seminal paper by Krizhevsky, Sutskever, and Hinton (2012). Access: open-access. Source: https://proceedings.neurips.cc/paper_files/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf
Machine Learning
Kingma and Ba · Paper
Seminal paper by Kingma and Ba (2014). Access: open-access. Source: https://arxiv.org/abs/1412.6980
Machine Learning
He et al. · Paper
Seminal paper by He et al. (2015). Access: open-access. Source: https://arxiv.org/abs/1512.03385
Machine Learning
Ronneberger, Fischer, and Brox · Paper
Seminal paper by Ronneberger, Fischer, and Brox (2015). Access: open-access. Source: https://arxiv.org/abs/1505.04597
Machine Learning
Vaswani et al. · Paper
Seminal paper by Vaswani et al. (2017). Access: open-access. Source: https://arxiv.org/abs/1706.03762
Machine Learning
Frankle and Carbin · Paper
Seminal paper by Frankle and Carbin (2018). Access: open-access. Source: https://arxiv.org/abs/1803.03635
Machine Learning
Mikolov et al. · Paper
Seminal paper by Mikolov et al. (2013). Access: open-access. Source: https://arxiv.org/abs/1310.4546
LLMs
Mikolov et al. · Paper
Seminal paper by Mikolov et al. (2013). Access: open-access. Source: https://arxiv.org/abs/1301.3781
LLMs
Cho et al. · Paper
Seminal paper by Cho et al. (2014). Access: open-access. Source: https://arxiv.org/abs/1406.1078
LLMs
Bahdanau, Cho, and Bengio · Paper
Seminal paper by Bahdanau, Cho, and Bengio (2014). Access: open-access. Source: https://arxiv.org/abs/1409.0473
LLMs
Sutskever, Vinyals, and Le · Paper
Seminal paper by Sutskever, Vinyals, and Le (2014). Access: open-access. Source: https://arxiv.org/abs/1409.3215
LLMs
Merity et al. · Paper
Seminal paper by Merity et al. (2016). Access: open-access. Source: https://arxiv.org/abs/1609.07843
LLMs
Devlin et al. · Paper
Seminal paper by Devlin et al. (2018). Access: open-access. Source: https://arxiv.org/abs/1810.04805
LLMs
Radford et al. · Paper
Seminal paper by Radford et al. (2018). Access: open-access. Source: https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf
LLMs
Radford et al. · Paper
Seminal paper by Radford et al. (2019). Access: open-access. Source: https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf
LLMs
Liu et al. · Paper
Seminal paper by Liu et al. (2019). Access: open-access. Source: https://arxiv.org/abs/1907.11692
LLMs
Raffel et al. · Paper
Seminal paper by Raffel et al. (2019). Access: open-access. Source: https://arxiv.org/abs/1910.10683
LLMs
Yang et al. · Paper
Seminal paper by Yang et al. (2019). Access: open-access. Source: https://arxiv.org/abs/1906.08237
LLMs
Brown et al. · Paper
Seminal paper by Brown et al. (2020). Access: open-access. Source: https://arxiv.org/abs/2005.14165
LLMs
Lewis et al. · Paper
Seminal paper by Lewis et al. (2020). Access: open-access. Source: https://arxiv.org/abs/2005.11401
LLMs
Kaplan et al. · Paper
Seminal paper by Kaplan et al. (2020). Access: open-access. Source: https://arxiv.org/abs/2001.08361
LLMs
Hu et al. · Paper
Seminal paper by Hu et al. (2021). Access: open-access. Source: https://arxiv.org/abs/2106.09685
LLMs
Wei et al. · Paper
Seminal paper by Wei et al. (2022). Access: open-access. Source: https://arxiv.org/abs/2201.11903
LLMs
Bai et al. · Paper
Seminal paper by Bai et al. (2022). Access: open-access. Source: https://arxiv.org/abs/2212.08073
LLMs
Chowdhery et al. · Paper
Seminal paper by Chowdhery et al. (2022). Access: open-access. Source: https://arxiv.org/abs/2204.02311
LLMs
Wang et al. · Paper
Seminal paper by Wang et al. (2022). Access: open-access. Source: https://arxiv.org/abs/2203.11171
LLMs
Ouyang et al. · Paper
Seminal paper by Ouyang et al. (2022). Access: open-access. Source: https://arxiv.org/abs/2203.02155
LLMs
Rafailov et al. · Paper
Seminal paper by Rafailov et al. (2023). Access: open-access. Source: https://arxiv.org/abs/2305.18290
LLMs
Touvron et al. · Paper
Seminal paper by Touvron et al. (2023). Access: open-access. Source: https://arxiv.org/abs/2302.13971
LLMs
Dettmers et al. · Paper
Seminal paper by Dettmers et al. (2023). Access: open-access. Source: https://arxiv.org/abs/2305.14314
LLMs
Schick et al. · Paper
Seminal paper by Schick et al. (2023). Access: open-access. Source: https://arxiv.org/abs/2302.04761
LLMs
Gyre Research · Gyre Publications
A role-by-role walkthrough of the Gyre Analytics and Operations Hub, showing what the platform does for the portfolio manager, risk officer, performance and investor-reporting team, compliance officer, middle office, fund accountant, trading desk, quant researcher and IR team in turn. Covers the Gyre Assistant, the internal and external data layers, what runs underneath, and closes with a full capability catalogue.
Portfolio Analytics · Investment Operations
Gyre Research · Gyre Publications
Competitive analysis of the next generation of AI accelerators challenging Nvidia across datacenter training, inference and edge workloads. Covers Nvidia (Blackwell Ultra, Vera Rubin), AMD MI350/MI400, Apple M5, Google TPU (Trillium, Ironwood and the v8 Sunfish/Zebrafish parts), Amazon Trainium3, Meta MTIA, Microsoft Maia 200, Broadcom's XPU platform, ARM and OpenAI's Broadcom-built accelerator, each assessed on processing power, memory architecture, software maturity, time to market and positioning versus Nvidia. Argues that memory bandwidth has replaced raw FLOPS as the binding constraint on LLM inference, that open interconnects are starting to contest NVLink, and that CUDA remains a software moat no competitor closed in 2026. This is the v2 revision, which corrects the May 2026 draft and carries an explicit list of the errors it fixes. Closes with a chip-by-chip competitive matrix.
AI · Computer Science · Finance
Newey, W. K. and West, K. D. · Paper
Gives a way to estimate the uncertainty of regression coefficients when the errors change in size over time and are correlated with their own past values. It weights the lagged covariances with weights that fall linearly with the lag, which guarantees the estimated covariance matrix is never negative, and proves the estimate is consistent. Its standard errors are the usual choice for regressions on overlapping or serially dependent financial data. Citation: Newey, W. K. and West, K. D. (1987). A Simple, Positive Semi-Definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix. Econometrica 55(3), 703-708. doi:10.2307/1913610. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Statistics · Quant Finance
Newey, W. K. and West, K. D. · Paper
Proposes a data-driven rule for choosing how many lags to include when estimating a covariance matrix that allows for changing error variance and serial correlation. For a given weighting scheme the rule is shown to behave, in large samples, like the choice that minimises mean squared error. Simulations show it works acceptably, though tests can still reject too often. It is the source of common default lag lengths in statistical software. Citation: Newey, W. K. and West, K. D. (1994). Automatic Lag Selection in Covariance Matrix Estimation. Review of Economic Studies 61(4), 631-653. doi:10.2307/2297912. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Statistics · Quant Finance
Benjamini, Y. and Hochberg, Y. · Paper
Introduces the false discovery rate: the expected share of rejected hypotheses that are in fact true. Controlling this share, rather than the chance of any single false rejection, gives more power when many hypotheses are tested. The paper gives a simple ranked-p-value procedure that controls the rate for independent tests and shows by simulation that the power gain is large. It is the standard method for screening many tests at once. Citation: Benjamini, Y. and Hochberg, Y. (1995). Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society, Series B 57(1), 289-300. doi:10.1111/j.2517-6161.1995.tb02031.x. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Statistics · Quant Finance
Benjamini, Y. and Yekutieli, D. · Paper
Shows that the Benjamini-Hochberg false discovery rate procedure remains valid when the test statistics are positively dependent in a defined sense, which covers cases such as many treatments compared with one control and positively correlated normal statistics. For arbitrary dependence it gives a more conservative version of the procedure. It is used to justify or adjust false discovery rate control when tests are correlated, as tests on related market series usually are. Citation: Benjamini, Y. and Yekutieli, D. (2001). The Control of the False Discovery Rate in Multiple Testing under Dependency. Annals of Statistics 29(4), 1165-1188. doi:10.1214/aos/1013699998. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Statistics · Quant Finance
Romano, J. P. and Wolf, M. · Paper
Presents a step-by-step testing procedure that decides which of many hypotheses to reject while keeping the chance of any false rejection under a set level in large samples. Because it uses resampling, it takes account of how the test statistics move together, so it rejects more false hypotheses than one-step methods. The setting is comparing many trading strategies with one benchmark. It is the origin of the Romano-Wolf step-down. Citation: Romano, J. P. and Wolf, M. (2005). Stepwise Multiple Testing as Formalized Data Snooping. Econometrica 73(4), 1237-1282. doi:10.1111/j.1468-0262.2005.00615.x. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Statistics · Quant Finance
Romano, J. P. and Wolf, M. · Paper
A three-page note. The authors' 2005 step-down procedure was stated only for testing at a fixed significance level; this note gives an algorithm that computes an adjusted p-value for each hypothesis from the same resampling runs, so each result can be reported with its own p-value. It is the method behind the adjusted p-values reported by common Romano-Wolf software and by our analytics. Citation: Romano, J. P. and Wolf, M. (2016). Efficient computation of adjusted p-values for resampling-based stepdown multiple testing. Statistics & Probability Letters 113, 38-40. doi:10.1016/j.spl.2016.02.012. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Statistics · Quant Finance
Politis, D. N. and Romano, J. P. · Paper
Introduces a bootstrap for data that depend on their own past, such as daily returns. Instead of resampling single observations, it resamples blocks of consecutive observations whose lengths are random, so the rebuilt series stays stationary. The method gives standard errors and confidence intervals for statistics of a dependent series. It is used when resampling time series to test significance without assuming the observations are independent. Citation: Politis, D. N. and Romano, J. P. (1994). The Stationary Bootstrap. Journal of the American Statistical Association 89(428), 1303-1313. doi:10.1080/01621459.1994.10476870. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Statistics · Quant Finance
Holm, S. · Paper
Gives a multiple testing procedure that rejects hypotheses one at a time, in sequence, until no further rejection is possible. It keeps the chance of any false rejection below the chosen level whatever combination of hypotheses is true, and it is more powerful than the plain one-step Bonferroni correction it refines. It is the Holm (Holm-Bonferroni) correction, widely used in applied statistics and in our multiple testing. Citation: Holm, S. (1979). A Simple Sequentially Rejective Multiple Test Procedure. Scandinavian Journal of Statistics 6(2), 65-70. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Statistics · Quant Finance
Westfall, P. H. and Young, S. S. · Book
A book on adjusting p-values when many tests are run at once, using bootstrap and permutation resampling so the adjustment reflects how the tests are correlated. It defines adjusted p-values for procedures that bound the chance of any false rejection, including the single-step and step-down minimum-p and maximum-t methods. These methods are the basis of later step-down procedures, including Romano and Wolf's. Citation: Westfall, P. H. and Young, S. S. (1993). Resampling-Based Multiple Testing: Examples and Methods for p-Value Adjustment. Wiley, New York; Wiley Series in Probability and Mathematical Statistics; ISBN 978-0-471-55761-6. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Statistics · Quant Finance
Sidak, Z. · Paper
Proves a conjecture of O. J. Dunn: a rectangular confidence region for the means of a multivariate normal distribution, built as if the coordinates were independent, is still conservative when they are correlated. The proof rests on an inequality for the probability of rectangles under the normal distribution. This inequality, known as Sidak's inequality, justifies treating a joint maximum as conservative in our multiple testing. Citation: Sidak, Z. (1967). Rectangular Confidence Regions for the Means of Multivariate Normal Distributions. Journal of the American Statistical Association 62(318), 626-633. doi:10.1080/01621459.1967.10482935. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Statistics · Quant Finance
Diebold, F. X. and Mariano, R. S. · Paper
Proposes tests of whether two competing forecasts are equally accurate. The tests work with many accuracy measures, not only squared error, and allow forecast errors that are not normal, have a nonzero mean, and are correlated over time and with each other. Both large-sample and exact small-sample versions are given. It is the standard test for comparing a model's out-of-sample forecasts with a benchmark. Citation: Diebold, F. X. and Mariano, R. S. (1995). Comparing Predictive Accuracy. Journal of Business & Economic Statistics 13(3), 253-263. doi:10.1080/07350015.1995.10524599. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Statistics · Quant Finance
Campbell, J. Y. and Thompson, S. B. · Paper
Responds to the finding that the historical average return forecasts stock returns as well as any predictive regression. The authors show that many predictors do beat the average out of sample once simple limits are placed on the signs of the coefficients and forecasts. The gain in explained variance is small but matters to an investor. The paper is the usual reference for the out-of-sample R squared measured against the historical mean. Citation: Campbell, J. Y. and Thompson, S. B. (2008). Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?. Review of Financial Studies 21(4), 1509-1531. doi:10.1093/rfs/hhm055. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Statistics · Quant Finance
Raftery, A. E., Karny, M. and Ettler, P. · Paper
Develops dynamic model averaging for online prediction when the best model is not known and may change over time. Each candidate model's coefficients follow a state space model, and a Markov chain describes which model is currently right; both use forgetting factors, which keeps the method simple and fast. It is applied to predicting strip thickness in a steel rolling mill. Our analytics cite it for dynamic model averaging. Citation: Raftery, A. E., Karny, M. and Ettler, P. (2010). Online Prediction Under Model Uncertainty via Dynamic Model Averaging: Application to a Cold Rolling Mill. Technometrics 52(1), 52-66. doi:10.1198/TECH.2009.08104. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Statistics · Quant Finance
McCormick, T. H., Raftery, A. E., Madigan, D. and Burd, R. S. · Paper
Extends dynamic model averaging to yes/no outcomes. The method classifies cases online while both the choice of model and its coefficients drift over time, and it tunes its forgetting factor as data arrive using the predictive distribution. It is applied to data on children treated for appendicitis by open or keyhole surgery. Our analytics cite it with Raftery and others 2010 for dynamic model averaging. Citation: McCormick, T. H., Raftery, A. E., Madigan, D. and Burd, R. S. (2012). Dynamic Logistic Regression and Dynamic Model Averaging for Binary Classification. Biometrics 68(1), 23-30. doi:10.1111/j.1541-0420.2011.01645.x. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Statistics · Quant Finance
Cochran, W. G. · Paper
Discusses how to combine estimates of the same quantity from several experiments when each reports an estimate and its standard error, such as a physical constant measured in different laboratories. It asks when a weighted mean should replace the plain average and which weights suit which situations. It is an early reference for combining results across studies; our analytics cite it for Cochran's Q, a test of whether separate estimates agree. Citation: Cochran, W. G. (1954). The Combination of Estimates from Different Experiments. Biometrics 10(1), 101-129. doi:10.2307/3001666. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Statistics · Quant Finance
Fama, E. F. and MacBeth, J. D. · Paper
Tests the capital asset pricing model by running a cross-sectional regression of returns on estimated betas every month and then studying the average and variation of the monthly coefficients. The tests ask whether the relation between risk and return is linear, whether beta is the only risk that is priced, and whether the risk premium is positive. The month-by-month regression method now carries the authors' names. Citation: Fama, E. F. and MacBeth, J. D. (1973). Risk, Return, and Equilibrium: Empirical Tests. Journal of Political Economy 81(3), 607-636. doi:10.1086/260061. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Quant Finance · Finance
Welch, B. L. · Paper
Extends Student's t test to comparing means when the groups have different variances. Instead of pooling the variances, the test uses each group's own variance and approximates the degrees of freedom of the resulting statistic. This is the Welch t test, the usual choice for comparing two means when equal variances cannot be assumed, used in our analytics to compare averages across samples. Citation: Welch, B. L. (1947). The Generalization of 'Student's' Problem when Several Different Population Variances are Involved. Biometrika 34(1-2), 28-35. doi:10.1093/biomet/34.1-2.28. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Statistics · Quant Finance
Mann, H. B. and Whitney, D. R. · Paper
Proposes the U statistic, built from the ranks of two samples, to test whether two populations have the same distribution without assuming any particular shape. The authors work out its exact distribution for small samples and show it approaches the normal curve as samples grow. It is the standard rank test for comparing two groups, for example returns in two regimes. Citation: Mann, H. B. and Whitney, D. R. (1947). On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other. The Annals of Mathematical Statistics 18(1), 50-60. doi:10.1214/aoms/1177730491. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Statistics · Quant Finance
Cohen, J. · Book
A reference book on how to plan studies so they have a good chance of detecting a real effect, with tables linking sample size, effect size and error rates for common tests. It also sets conventional labels for effect sizes; for a correlation coefficient, 0.10 is called small, 0.30 medium and 0.50 large. Our analytics use these labels to describe correlation strength. Citation: Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences. 2nd ed., Hillsdale, NJ: Lawrence Erlbaum Associates, xxi + 567 pp., ISBN 0-8058-0283-5. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Statistics
Marchenko, V. A. and Pastur, L. A. · Paper
Derives the limiting shape of the spread of eigenvalues for large random matrices when the number of variables and observations grow together. The resulting curve, now called the Marchenko-Pastur law, gives the range of eigenvalues that pure noise would produce. We use its upper edge to decide which principal components of a correlation matrix carry signal and which look like noise. Citation: Marchenko, V. A. and Pastur, L. A. (1967). Distribution of eigenvalues for some sets of random matrices. Mathematics of the USSR-Sbornik 1(4), 457-483 (English translation of Matematicheskii Sbornik (N.S.) 72(114)(4), 507-). doi:10.1070/SM1967v001n04ABEH001994. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Statistics · Math
Laloux, L., Cizeau, P., Bouchaud, J.-P. and Potters, M. · Paper
Compares the eigenvalues of correlation matrices built from S&P 500 stock returns with what random-matrix theory predicts for pure noise. Most eigenvalues fall inside the noise band, so most of the measured structure cannot be told apart from chance; only a few large eigenvalues stand out. It is used to filter noise out of correlation matrices before using them. Citation: Laloux, L., Cizeau, P., Bouchaud, J.-P. and Potters, M. (1999). Noise Dressing of Financial Correlation Matrices. Physical Review Letters 83(7), 1467-1470. doi:10.1103/PhysRevLett.83.1467. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Risk Management · Quant Finance
Friedman, J., Hastie, T. and Tibshirani, R. · Paper
Gives a fast algorithm, the graphical lasso, for estimating a covariance matrix whose inverse has many exact zeros. A zero in the inverse means two variables are unrelated once all the others are accounted for. The method makes these partial-correlation networks practical for hundreds of variables; we use it to separate direct links between series from links that pass through shared drivers. Citation: Friedman, J., Hastie, T. and Tibshirani, R. (2008). Sparse inverse covariance estimation with the graphical lasso. Biostatistics 9(3), 432-441. doi:10.1093/biostatistics/kxm045. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Statistics · Machine Learning
Sahm, C. · Paper
Proposes that the US government send lump-sum payments to individuals automatically when a recession starts, instead of waiting for Congress. The trigger, now called the Sahm rule, fires when the three-month average unemployment rate is at least 0.50 percentage points above its lowest level of the previous 12 months. We use that trigger as a recession signal. Citation: Sahm, C. (2019). Direct Stimulus Payments to Individuals. In Boushey, H., Nunn, R. and Shambaugh, J. (eds.), Recession Ready: Fiscal Policies to Stabilize the American Economy, Washington, DC: The Hamilton Project (Brookings Institution) and Washington Center for Equitable Growth, May 2019, pp. 67-92. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Economics · Statistics
Pearson, K. · Paper
A three-page note giving formulas for how a child's measurement relates to the measurements of both parents, using correlation coefficients between the parents and between each parent and the offspring, under the assumption that deviations follow the normal curve. It is the usual citation for the Pearson correlation coefficient, which our analytics use to measure linear association between two series. Citation: Pearson, K. (1895). Note on Regression and Inheritance in the Case of Two Parents. Proceedings of the Royal Society of London 58, 240-242. doi:10.1098/rspl.1895.0041.
Statistics
Spearman, C. · Paper
Sets out how to measure how strongly two things go together and how to correct a measured correlation for errors in the measurements. It introduces comparing items by their rank order rather than their raw values, the basis of the rank correlation coefficient named after Spearman. Our analytics use rank correlation where outliers or non-linear links would mislead the ordinary coefficient. Citation: Spearman, C. (1904). The Proof and Measurement of Association between Two Things. The American Journal of Psychology 15(1), 72-101. doi:10.2307/1412159.
Statistics
Fisher, R. A. · Paper
Works out how a correlation coefficient measured on a small sample is spread around its true value. Fisher shows that a simple transformation of the coefficient, now called the Fisher z transform, has a nearly normal distribution whose spread depends only on sample size. This makes confidence intervals and tests for correlations straightforward, which is how our analytics use it. Citation: Fisher, R. A. (1921). On the "probable error" of a coefficient of correlation deduced from a small sample. Metron 1(4), 3-32.
Statistics
Baum, L. E., Petrie, T., Soules, G. and Weiss, N. · Paper
Gives an iterative method for fitting a hidden Markov model, in which an unseen state switches over time and drives what is observed, and proves each step does not lower the likelihood of the data. The method, known as the Baum-Welch algorithm, is a special case of what was later called expectation-maximisation. We use it to fit regime models to market data. Citation: Baum, L. E., Petrie, T., Soules, G. and Weiss, N. (1970). A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains. The Annals of Mathematical Statistics 41(1), 164-171. doi:10.1214/aoms/1177697196. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Statistics · Machine Learning
Viterbi, A. J. · Paper
Bounds how often an optimal convolutional code is decoded wrongly over a noisy channel, as a function of the code's constraint length, and presents a decoding algorithm that is asymptotically optimal. That algorithm, now called the Viterbi algorithm, is the standard way to recover the most likely sequence of hidden states, which is how our regime models assign a market state to each day. Citation: Viterbi, A. J. (1967). Error Bounds for Convolutional Codes and an Asymptotically Optimum Decoding Algorithm. IEEE Transactions on Information Theory 13(2), 260-269. doi:10.1109/TIT.1967.1054010. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Statistics · Machine Learning
Mahalanobis, P. C. · Paper
Defines a distance between two populations measured on several correlated characteristics, which allows for the correlations between them through the dispersion matrix. Mahalanobis built it for comparing groups in anthropometric data. The same measure, the Mahalanobis distance, tells how unusual an observation is relative to a multivariate distribution, which is how our analytics use it. Citation: Mahalanobis, P. C. (1936). On the Generalised Distance in Statistics. Proceedings of the National Institute of Sciences of India 2(1), 49-55. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Statistics · Risk Management
Dimson, E. · Paper
Shows that betas estimated for shares that trade rarely are biased, because their recorded prices lag the market. Dimson's fix regresses share returns on market returns of several neighbouring periods and adds up the slopes to get one beta. Our beta analytics apply this correction for thinly traded shares. Citation: Dimson, E. (1979). Risk Measurement When Shares Are Subject to Infrequent Trading. Journal of Financial Economics 7(2), 197-226. doi:10.1016/0304-405X(79)90013-8. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Quant Finance · Finance
Blume, M. E. · Paper
Studies how stable stock betas are from one period to the next. Betas measured in one period tend to move toward the average of 1.0 in the next, so a raw past beta is a biased guide to the future. The finding underlies the common adjustment that shrinks a measured beta toward 1.0, which our beta analytics use. Citation: Blume, M. E. (1971). On the Assessment of Risk. The Journal of Finance 26(1), 1-10. doi:10.1111/j.1540-6261.1971.tb00584.x. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Quant Finance · Finance
Blume, M. E. · Paper
Returns to the tendency of betas to drift toward 1.0 over time and examines why it happens, attributing it to true betas changing rather than only to measurement error. It supports using an adjusted beta, pulled toward 1.0, when forecasting risk. Our beta analytics cite it alongside the 1971 paper for that adjustment. Citation: Blume, M. E. (1975). Betas and Their Regression Tendencies. The Journal of Finance 30(3), 785-795. doi:10.1111/j.1540-6261.1975.tb01850.x. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Quant Finance · Finance
Fama, E. F. and French, K. R. · Paper
Identifies five shared sources of risk in returns: three for stocks (the overall market, company size, and book-to-market ratio) and two for bonds (maturity and default risk). Together they explain much of the variation and average level of stock and bond returns. The stock factors became the Fama-French factors, which our analytics use to explain portfolio returns. Citation: Fama, E. F. and French, K. R. (1993). Common Risk Factors in the Returns on Stocks and Bonds. Journal of Financial Economics 33(1), 3-56. doi:10.1016/0304-405X(93)90023-5. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Quant Finance · Finance
Sharpe, W. F. · Paper
Proposes judging a mutual fund by the reward it earned per unit of variability of its returns, a measure Sharpe called the reward-to-variability ratio, and applies it to 34 US funds. On that measure the average fund did worse than the Dow Jones Industrial Average over 1954-1963. The ratio is now known as the Sharpe ratio and is a core measure in our performance analytics. Citation: Sharpe, W. F. (1966). Mutual Fund Performance. The Journal of Business 39(1, Part 2), 119-138. doi:10.1086/294846. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Portfolio Analytics · Finance
Sharpe, W. F. · Paper
Sharpe looks back at the fund performance measure he introduced in 1966 under the name reward-to-variability ratio, notes that others have called it the Sharpe index, measure or ratio, and discusses the original version and more general versions that later appeared. It is the author's own reference statement of the measure, and our Sharpe ratio calculations cite it as the definition. Citation: Sharpe, W. F. (1994). The Sharpe Ratio. The Journal of Portfolio Management 21(1), 49-58. doi:10.3905/jpm.1994.409501. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Portfolio Analytics · Finance
Markowitz, H. M. · Paper
Markowitz's book-length treatment of choosing a portfolio by trading off expected return against risk. It explains averages, variances and covariances of securities, shows how to find the set of efficient portfolios, and sets out the expected-utility reasoning behind the method. Chapter 9 offers semi-variance, which counts only returns below a target, as an alternative risk measure. We cite it for the point that losses and gains need not be weighed alike. Citation: Markowitz, H. M. (1959). Portfolio Selection: Efficient Diversification of Investments. Cowles Foundation for Research in Economics at Yale University, Monograph 16; New York: John Wiley & Sons. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Quant Finance · Finance
Jegadeesh, N. · Paper
Shows that monthly returns of individual US stocks can be forecast from their own past returns: a stock's return last month tends to reverse, while its return about a year earlier tends to repeat. Ranking stocks on these forecasts over 1934-1987 gave a large gap between the top and bottom groups. Used as early evidence for both short-term reversal and the twelve-month continuation behind momentum. Citation: Jegadeesh, N. (1990). Evidence of Predictable Behavior of Security Returns. The Journal of Finance 45(3), 881-898. doi:10.1111/j.1540-6261.1990.tb05110.x. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Quant Finance · Finance
Lehmann, B. N. · Paper
Looks at weekly stock returns to separate two explanations of predictable returns: changing expected returns or market overreaction. Stocks that rose most in one week tended to fall the next, and the reverse for the biggest losers, with profits that survived bid-ask and trading-cost adjustments. Lehmann reads this as a short-horizon inefficiency around large price moves; cited as a source of the one-week reversal effect. Citation: Lehmann, B. N. (1990). Fads, Martingales, and Market Efficiency. The Quarterly Journal of Economics 105(1), 1-28. doi:10.2307/2937816. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Quant Finance · Finance
Jegadeesh, N. and Titman, S. · Paper
The standard reference for stock price momentum. Buying the stocks with the best returns over the past three to twelve months and selling the worst gave significant positive returns over the next three to twelve months, not explained by market risk. Part of the gain faded in the two years after. Used as the base definition and evidence for the momentum signal. Citation: Jegadeesh, N. and Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. The Journal of Finance 48(1), 65-91. doi:10.1111/j.1540-6261.1993.tb04702.x. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Quant Finance · Finance
Novy-Marx, R. · Paper
Questions what drives momentum. Novy-Marx finds that a stock's performance from twelve to seven months before ranking predicts future returns better than its most recent months, so the effect is not simply recent trends continuing. The same pattern shows up in country stock indices, commodities and currencies. Cited when choosing which past months to use in a momentum score. Citation: Novy-Marx, R. (2012). Is Momentum Really Momentum?. Journal of Financial Economics 103(3), 429-453. doi:10.1016/j.jfineco.2011.05.003. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Quant Finance · Finance
De Bondt, W. F. M. and Thaler, R. · Paper
Tests whether investors overreact to dramatic news, as psychology experiments suggest. Using monthly US stock returns, past multi-year losers went on to beat past winners in later years, with a large part of the losers' gain coming in January. Treated as evidence of long-horizon reversal, the opposite of momentum, and cited for why momentum uses months rather than years of history. Citation: De Bondt, W. F. M. and Thaler, R. (1985). Does the Stock Market Overreact?. The Journal of Finance 40(3), 793-805. doi:10.1111/j.1540-6261.1985.tb05004.x. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Quant Finance · Finance
Barroso, P. and Santa-Clara, P. · Paper
Shows that the risk of a momentum strategy changes over time and can be forecast from its own recent volatility. Scaling the position down when that volatility is high and up when it is low greatly reduces the worst crashes and roughly doubles the Sharpe ratio. Cited for volatility-scaled momentum and for measuring momentum risk from the strategy's own returns. Citation: Barroso, P. and Santa-Clara, P. (2015). Momentum Has Its Moments. Journal of Financial Economics 116(1), 111-120. doi:10.1016/j.jfineco.2014.11.010. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Quant Finance · Finance
Blitz, D., Huij, J. and Martens, M. · Paper
Proposes ranking stocks on the part of their past return not explained by the Fama-French factors, instead of on total return. This residual momentum avoids the shifting factor bets of ordinary momentum, earns about twice the risk-adjusted return, and is steadier over time. Cited as the method for a factor-adjusted momentum score. Citation: Blitz, D., Huij, J. and Martens, M. (2011). Residual Momentum. Journal of Empirical Finance 18(3), 506-521. doi:10.1016/j.jempfin.2011.01.003. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Quant Finance · Finance
Moskowitz, T. J. and Grinblatt, M. · Paper
Finds that much of the momentum in individual stocks comes from momentum in whole industries. Buying stocks in recently strong industries and selling those in weak ones was highly profitable, while ordinary stock momentum became much weaker once industry momentum was controlled for. Cited for industry momentum and for the link between sector trends and stock trends. Citation: Moskowitz, T. J. and Grinblatt, M. (1999). Do Industries Explain Momentum?. The Journal of Finance 54(4), 1249-1290. doi:10.1111/0022-1082.00146. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Quant Finance · Finance
Da, Z., Gurun, U. G. and Warachka, M. · Paper
Tests whether investors pay less attention to news that arrives in many small steps than to a few large jumps. Stocks whose past gains or losses built up gradually showed much stronger and longer-lasting momentum than stocks with the same total move made in a few big days. Cited for measuring how smooth a stock's past path was when scoring momentum. Citation: Da, Z., Gurun, U. G. and Warachka, M. (2014). Frog in the Pan: Continuous Information and Momentum. The Review of Financial Studies 27(7), 2171-2218. doi:10.1093/rfs/hhu003. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Quant Finance · Finance
Daniel, K. and Moskowitz, T. J. · Paper
Studies the rare but severe losing streaks of momentum strategies. The crashes tend to come after market falls, when volatility is high, and at the moment the market rebounds, when past losers jump. The authors build a strategy that scales momentum using forecasts of its mean and variance, which roughly doubles its risk-adjusted return. Cited for momentum crash risk and dynamic weighting. Citation: Daniel, K. and Moskowitz, T. J. (2016). Momentum Crashes. Journal of Financial Economics 122(2), 221-247. doi:10.1016/j.jfineco.2015.12.002. Open access: the link goes to the publisher's free copy.
Quant Finance · Finance
Hong, H., Lim, T. and Stein, J. C. · Paper
Tests the idea that momentum comes from news spreading slowly among investors. Beyond the very smallest firms, momentum profits fall as firm size rises, and for firms of the same size they are larger when fewer analysts follow the stock. The effect is stronger for past losers, suggesting bad news spreads slowest. Cited for size and analyst coverage as conditions on momentum. Citation: Hong, H., Lim, T. and Stein, J. C. (2000). Bad News Travels Slowly: Size, Analyst Coverage, and the Profitability of Momentum Strategies. The Journal of Finance 55(1), 265-295. doi:10.1111/0022-1082.00206. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Quant Finance · Finance
Carhart, M. M. · Paper
Using mutual fund data free of survivor bias, shows that funds' apparent repeat winning is mostly explained by common stock-return factors, including one-year stock momentum, and by fund expenses. Only the persistent poor results of the worst funds remain unexplained. The paper introduced the four-factor model (market, size, value, momentum) that we cite as a standard momentum factor. Citation: Carhart, M. M. (1997). On Persistence in Mutual Fund Performance. The Journal of Finance 52(1), 57-82. doi:10.1111/j.1540-6261.1997.tb03808.x. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Quant Finance · Finance
Asness, C. S., Moskowitz, T. J. and Pedersen, L. H. · Paper
Finds value and momentum return premiums in eight different markets and asset classes, from stocks in several countries to bonds, currencies and commodities. Value returns move together across markets, as do momentum returns, while value and momentum are negatively correlated with each other. Cited as evidence that momentum is not limited to US stocks and for combining value with momentum. Citation: Asness, C. S., Moskowitz, T. J. and Pedersen, L. H. (2013). Value and Momentum Everywhere. The Journal of Finance 68(3), 929-985. doi:10.1111/jofi.12021. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Quant Finance · Finance
Moskowitz, T. J., Ooi, Y. H. and Pedersen, L. H. · Paper
Shows that an asset's own past twelve-month return predicts its next month's return across 58 futures markets in stock indices, currencies, commodities and bonds. The effect lasts up to a year and partly reverses later. A diversified trend-following portfolio earned strong returns with little exposure to standard factors and did best in extreme markets. Cited for absolute, trend-based momentum. Citation: Moskowitz, T. J., Ooi, Y. H. and Pedersen, L. H. (2012). Time Series Momentum. Journal of Financial Economics 104(2), 228-250. doi:10.1016/j.jfineco.2011.11.003. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Quant Finance · Finance
Brock, W., Lakonishok, J. and LeBaron, B. · Paper
Tests moving-average and trading-range breakout rules on the Dow Jones index from 1897 to 1986, using bootstrap simulations to judge significance. Days after buy signals had higher and less volatile returns than days after sell signals, a pattern that common random-walk and GARCH models could not reproduce. Cited for moving-average rules and for the bootstrap method of testing them. Citation: Brock, W., Lakonishok, J. and LeBaron, B. (1992). Simple Technical Trading Rules and the Stochastic Properties of Stock Returns. The Journal of Finance 47(5), 1731-1764. doi:10.1111/j.1540-6261.1992.tb04681.x. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Quant Finance · Finance
Faber, M. T. · Paper
Tests a simple rule: hold an asset class when its price is above its long moving average and move to cash when it is below. Applied to US stocks and then to more than twenty other markets, and to a mix of US stocks, foreign stocks, commodities, real estate and Treasury bonds since 1972, the rule kept stock-like returns with much lower volatility and drawdowns. Cited for moving-average timing. Citation: Faber, M. T. (2007). A Quantitative Approach to Tactical Asset Allocation. The Journal of Wealth Management 9(4), 69-79. doi:10.3905/jwm.2007.674809. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Quant Finance · Finance
George, T. J. and Hwang, C.-Y. · Paper
Shows that how close a stock's price is to its highest price of the past 52 weeks explains much of the profit from momentum strategies, and forecasts returns better than past returns do. Returns predicted this way do not reverse later. The authors argue that short-term continuation and long-term reversal are separate effects. Cited for the 52-week-high signal. Citation: George, T. J. and Hwang, C.-Y. (2004). The 52-Week High and Momentum Investing. The Journal of Finance 59(5), 2145-2176. doi:10.1111/j.1540-6261.2004.00695.x. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Quant Finance · Finance
Appel, G. · Book
A practical book on stock market timing by Gerald Appel, who devised the MACD indicator. It covers trend and momentum measures, chart patterns, market cycles, breadth and volume extremes, moving-average channels, and gives a full chapter to advanced uses of MACD. We cite it as a reference description of how MACD is calculated and read. Citation: Appel, G. (2005). Technical Analysis: Power Tools for Active Investors. Upper Saddle River, NJ: Financial Times Prentice Hall (FT Press), 264 pp., ISBN 978-0-13-147902-9. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Quant Finance · Finance
Wilder, J. W., Jr. · Book
A 1978 book, published by the author through his own firm, that sets out a set of trading rules and indicators for futures and stocks. It is the source of the Relative Strength Index, Average True Range, the Parabolic stop-and-reverse rule and the directional movement index (ADX). We cite it as the original definition of RSI and of the smoothing Wilder used. Citation: Wilder, J. W., Jr. (1978). New Concepts in Technical Trading Systems. Trend Research, ISBN 978-0-89459-027-6. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Quant Finance · Finance
Grinold, R. C. and Kahn, R. N. · Book
A textbook on running an actively managed portfolio against a benchmark using economics, statistics and optimisation. It defines returns and risk relative to the benchmark, treats forecasting skill, portfolio construction and performance measurement, and in the second edition adds asset allocation, long/short investing and forecast horizons. We cite it for the information ratio and the benchmark-relative framework. Citation: Grinold, R. C. and Kahn, R. N. (2000). Active Portfolio Management: A Quantitative Approach for Producing Superior Returns and Controlling Risk. 2nd ed., New York: McGraw-Hill (Irwin Library of Investment & Finance), xv + 596 pp., ISBN 0-07-024882-6. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Portfolio Analytics · Finance
Granville, J. E. · Book
A 1963 book on stock market timing by Joseph Granville in which he introduced on-balance volume, an indicator that keeps a running total of trading volume according to whether prices rise or fall. His premise was that changes in volume tend to come before changes in price. We cite it as the origin of the on-balance volume indicator. Citation: Granville, J. E. (1963). Granville's New Key to Stock Market Profits. Englewood Cliffs, NJ: Prentice-Hall. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Quant Finance · Finance
Ang, A., Chen, J. and Xing, Y. · Paper
The paper measures how strongly each stock moves with the market on days when the market falls, separately from its ordinary beta, and finds that stocks that fall more with the market earn higher average returns, about 6% a year. The premium is not explained by regular beta, coskewness, liquidity, size, value or momentum. We cite it for the downside beta definition. Citation: Ang, A., Chen, J. and Xing, Y. (2006). Downside Risk. The Review of Financial Studies 19(4), 1191-1239. doi:10.1093/rfs/hhj035. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Quant Finance · Finance
Bawa, V. S. and Lindenberg, E. B. · Paper
The paper builds a version of the capital asset pricing model in which risk is measured only by returns falling below a target (the lower partial moment) instead of by variance. It derives equilibrium prices for risky assets and a test that does not depend on the return distribution, and shows the model reduces to the usual CAPM in some cases. We cite it as the origin of downside beta. Citation: Bawa, V. S. and Lindenberg, E. B. (1977). Capital market equilibrium in a mean-lower partial moment framework. Journal of Financial Economics 5(2), 189-200. doi:10.1016/0304-405X(77)90017-4. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Quant Finance · Finance
Harlow, W. V. and Rao, R. K. S. · Paper
The paper extends the downside-risk asset pricing model so that risk is the shortfall below any chosen target return, not only the risk-free rate. Tested on US stock returns, the model is not rejected for a wide range of targets while the standard CAPM is, and the targets implied by the data sit near the average market return. We cite it for the downside beta measure. Citation: Harlow, W. V. and Rao, R. K. S. (1989). Asset Pricing in a Generalized Mean-Lower Partial Moment Framework: Theory and Evidence. Journal of Financial and Quantitative Analysis 24(3), 285-311. doi:10.2307/2330813. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Quant Finance · Finance
Welch, I. · Paper
The paper proposes a simple way to estimate a stock's market beta: before the usual regression, cap each daily stock return at -2 and +4 times that day's market return, and optionally give older days less weight (half-life about four months). This predicts future betas better than ordinary regression betas and several published alternatives. We cite it for our beta estimator. Citation: Welch, I. (2022). Simply Better Market Betas. Critical Finance Review 11(1), 37-64. doi:10.1561/104.00000108. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Quant Finance · Finance
Brier, G. W. · Paper
A three-page paper proposing a score for forecasts given as probabilities: the average squared difference between the forecast probabilities and what happened (1 for the outcome that occurred, 0 otherwise). Brier shows that a forecaster gets the best expected score by stating his true belief, so the score cannot be gamed by hedging. We cite it as the definition of the Brier score. Citation: Brier, G. W. (1950). Verification of Forecasts Expressed in Terms of Probability. Monthly Weather Review 78(1), 1-3. doi:10.1175/1520-0493(1950)078<0001:VOFEIT>2.0.CO;2. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Statistics · Machine Learning
Chow, G., Jacquier, E., Kritzman, M. and Lowry, K. · Paper
The paper separates historical returns into ordinary periods and turbulent periods, picked out as statistical outliers across many assets at once, and estimates a separate covariance matrix for each. Investors can then blend the two according to how likely and how unwelcome they think turbulence is when building a portfolio. We cite it as the origin of the multivariate turbulence (outlier distance) measure. Citation: Chow, G., Jacquier, E., Kritzman, M. and Lowry, K. (1999). Optimal Portfolios in Good Times and Bad. Financial Analysts Journal 55(3), 65-73. doi:10.2469/faj.v55.n3.2273. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Risk Management · Quant Finance
Hamilton, J. D. · Paper
The paper models a series whose growth rate switches now and then between unobserved states, and gives a method to infer from the data which state held at each date while estimating the model by maximum likelihood. Applied to US output, the switch to negative growth tracks recessions, each linked to a lasting drop of about 3% in GNP. We cite it for regime-switching models. Citation: Hamilton, J. D. (1989). A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle. Econometrica 57(2), 357-384. doi:10.2307/1912559. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Statistics · Machine Learning
Hoeting, J. A., Madigan, D., Raftery, A. E. and Volinsky, C. T. · Paper
A tutorial on Bayesian model averaging: instead of choosing one model and treating it as true, predictions are averaged over many candidate models, each weighted by how well the data support it. The authors review ways to carry this out, show examples where the averaged forecasts do better out of sample, and list software. We cite it for combining forecasts across models. Citation: Hoeting, J. A., Madigan, D., Raftery, A. E. and Volinsky, C. T. (1999). Bayesian Model Averaging: A Tutorial. Statistical Science 14(4), 382-417. doi:10.1214/ss/1009212519. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Statistics · Machine Learning
Kalman, R. E. · Paper
The paper restates the problem of estimating a signal from noisy measurements in terms of a system's state and how it changes over time. From an equation for the covariance of the estimation error it obtains the optimal linear filter directly, for changing as well as stable statistics, and shows filtering is the mirror image of a control problem. This is the Kalman filter, which we cite for time-varying estimates. Citation: Kalman, R. E. (1960). A New Approach to Linear Filtering and Prediction Problems. Journal of Basic Engineering 82(1), 35-45. doi:10.1115/1.3662552. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Statistics · Machine Learning
Kritzman, M. and Li, Y. · Paper
The paper takes a statistical distance first devised in 1927 to compare human skulls and uses it to score how unusual each period's asset returns are, as a measure of financial turbulence. It shows how that score can be used to measure and manage portfolio risk and to improve investment results. We cite it for the turbulence index. Citation: Kritzman, M. and Li, Y. (2010). Skulls, Financial Turbulence, and Risk Management. Financial Analysts Journal 66(5), 30-41. doi:10.2469/faj.v66.n5.3. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Risk Management · Quant Finance
Kritzman, M., Li, Y., Page, S. and Rigobon, R. · Paper
The paper introduces the absorption ratio: the share of the total variance of a set of asset returns explained by a fixed small number of principal components. A high ratio means markets are tightly linked, so shocks spread faster and further. It is offered as a market-based measure of systemic risk when the direct links between institutions cannot be seen. We cite it for the absorption ratio. Citation: Kritzman, M., Li, Y., Page, S. and Rigobon, R. (2011). Principal Components as a Measure of Systemic Risk. The Journal of Portfolio Management 37(4), 112-126. doi:10.3905/jpm.2011.37.4.112. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Risk Management · Quant Finance
López de Prado, M. · Book
A book on applying machine learning to investment research. It covers how to structure and label financial data, weight samples, and build features, how to fit models and cross-validate them, how to size bets, and how to backtest while limiting false discoveries, with code examples. We cite it for its cross-validation and backtesting methods. Citation: López de Prado, M. (2018). Advances in Financial Machine Learning. Hoboken, NJ: Wiley, ISBN 978-1-119-48208-6. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Machine Learning · Quant Finance
Murphy, A. H. · Paper
The paper splits the Brier (probability) score into three parts: the uncertainty of the events themselves, the reliability of the forecasts (whether stated probabilities match observed frequencies), and their resolution (how well forecasts separate occasions with different outcomes). It compares this with an earlier split and illustrates both on sample forecasts. We cite it for the reliability and resolution terms of the Brier score. Citation: Murphy, A. H. (1973). A New Vector Partition of the Probability Score. Journal of Applied Meteorology 12(4), 595-600. doi:10.1175/1520-0450(1973)012<0595:ANVPOT>2.0.CO;2. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Statistics
Rabiner, L. R. · Paper
A teaching article on hidden Markov models, in which an unseen state changes over time and each observation depends on that state. It reviews Markov chains, works through simple examples such as coin tossing and balls in urns, states the three basic problems these models raise and gives practical ways to solve them, with speech recognition as the application. Our analytics cite it for the hidden-state model method. Citation: Rabiner, L. R. (1989). A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. Proceedings of the IEEE 77(2), 257-286. doi:10.1109/5.18626. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Statistics · Machine Learning
Zadrozny, B. and Elkan, C. · Paper
Shows how to turn classifier scores into probabilities that match observed frequencies. It gives a two-class method that needs only the order in which the classifier ranks examples, and builds estimates for problems with many classes by combining two-class results. Tests use naive Bayes and support vector machines on marketing, text and digit data. Our analytics cite it for isotonic calibration. Citation: Zadrozny, B. and Elkan, C. (2002). Transforming Classifier Scores into Accurate Multiclass Probability Estimates. Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '02), 694-699. doi:10.1145/775047.775151. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Statistics · Machine Learning
Platt, J. C. · Paper
Proposes turning the output of a support vector machine into a probability by fitting a sigmoid (S-shaped) curve to its scores after training, with empirical tests and pseudo-code for the fit. The step is widely known as Platt scaling. Our analytics cite it when a model's raw scores are mapped to probabilities with a fitted sigmoid curve. Citation: Platt, J. C. (2000). Probabilities for SV Machines. In Smola, A. J., Bartlett, P., Schölkopf, B. and Schuurmans, D. (eds.), Advances in Large-Margin Classifiers, MIT Press, 61-74. doi:10.7551/mitpress/1113.003.0008. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Statistics · Machine Learning
Ledoit, O. and Wolf, M. · Paper
Proposes estimating the covariance matrix of stock returns by shrinking the sample matrix toward a target built from a single-factor (market) model, so that noisy estimates are pulled toward a more stable structure. It applies the estimate to portfolio selection. Our analytics cite it for shrinkage toward a single-factor target when the covariance matrix feeds portfolio calculations. Citation: Ledoit, O. and Wolf, M. (2003). Improved estimation of the covariance matrix of stock returns with an application to portfolio selection. Journal of Empirical Finance 10(5), 603-621. doi:10.1016/s0927-5398(03)00007-0. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Risk Management · Quant Finance
Ledoit, O. and Wolf, M. · Paper
Gives a covariance matrix estimator meant for cases with many variables relative to the number of observations, where the sample matrix is badly conditioned. The estimate shrinks the sample matrix toward a multiple of the identity matrix so that it stays well conditioned and invertible. Our analytics cite it for shrinkage toward the identity when estimating large covariance matrices. Citation: Ledoit, O. and Wolf, M. (2004). A well-conditioned estimator for large-dimensional covariance matrices. Journal of Multivariate Analysis 88(2), 365-411. doi:10.1016/s0047-259x(03)00096-4. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Risk Management · Quant Finance
Ledoit, O. and Wolf, M. · Paper
Argues that the sample covariance matrix should not be used directly for portfolio optimisation because its estimation errors mislead the optimiser. It proposes shrinkage, which pulls extreme coefficients toward central values, and addresses how much to shrink. The authors report lower tracking error and a higher information ratio. Our analytics cite it for shrinkage toward a constant-correlation target. Citation: Ledoit, O. and Wolf, M. (2004). Honey, I Shrunk the Sample Covariance Matrix. The Journal of Portfolio Management 30(4), 110-119. doi:10.3905/jpm.2004.110. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Risk Management · Quant Finance
Brinson, G. P., Hood, L. R. and Beebower, G. L. · Paper
Sets out a way to attribute a pension plan's return to investment policy (a passive benchmark of its long-term asset mix), market timing (over- or under-weighting asset classes) and security selection within asset classes. Using 91 large US pension plans over 1974-1983, it found investment policy dominated the other two. Our analytics cite it for the Brinson-Hood-Beebower attribution framework. Citation: Brinson, G. P., Hood, L. R. and Beebower, G. L. (1986). Determinants of Portfolio Performance. Financial Analysts Journal 42(4), 39-44. doi:10.2469/faj.v42.n4.39. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Portfolio Analytics · Finance
Brinson, G. P. and Fachler, N. · Paper
A short article on measuring the performance of non-US equity portfolios against international market indexes, discussing the Capital International indexes and their strengths and limits, including the split between local-currency and dollar returns. Our analytics cite it as the source of the Brinson-Fachler form of attribution, which splits active return into country allocation and stock selection effects. Citation: Brinson, G. P. and Fachler, N. (1985). Measuring Non-US Equity Portfolio Performance. The Journal of Portfolio Management 11(3), 73-76. doi:10.3905/jpm.1985.409005. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Portfolio Analytics · Finance
Almgren, R. and Chriss, N. · Paper
Models the execution of a large portfolio trade over time, trading off the price risk of trading slowly against the market-impact cost of trading quickly. It builds an efficient frontier of trading schedules, chooses among them by a utility function or by value-at-risk, and introduces liquidity-adjusted value-at-risk. Our analytics cite it for trade scheduling and expected execution cost. Citation: Almgren, R. and Chriss, N. (2001). Optimal Execution of Portfolio Transactions. Journal of Risk 3(2), 5-39. doi:10.21314/jor.2001.041. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Quant Finance · Investment Operations
Karnosky, D. S. and Singer, B. D. · Paper
A short book setting out a framework for evaluating global asset markets and a performance attribution system for portfolios invested across many countries. It separates the effects of market allocation, currency management and security selection on a global portfolio's return. Our analytics cite it as the source of the Karnosky-Singer attribution model for portfolios holding several currencies. Citation: Karnosky, D. S. and Singer, B. D. (1994). Global Asset Management and Performance Attribution. The Research Foundation of The Institute of Chartered Financial Analysts (now CFA Institute Research Foundation), February 1994. doi:10.2470/rf.v1994.n3.4444. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Portfolio Analytics · Finance
Carino, D. R. · Paper
Addresses how to combine single-period performance attribution effects over many periods so that the linked effects explain the return difference over the whole span. It is cited in later patents as an algorithm for linking attribution effects over time into a multi-period attribution. Our analytics cite it for the linking method known as Carino linking. Citation: Carino, D. R. (1999). Combining Attribution Effects Over Time. The Journal of Performance Measurement, Summer 1999, 5-14. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Portfolio Analytics · Finance
Jensen, M. C. · Paper
Evaluates the performance of US mutual funds over 1945-1964, asking whether managers added return by predicting security prices once the risk their portfolios carried is taken into account. The risk-adjusted excess return measure used in the study is known as Jensen's alpha. Our analytics cite it for that measure of performance relative to market risk. Citation: Jensen, M. C. (1968). The Performance of Mutual Funds in the Period 1945-1964. The Journal of Finance 23(2), 389-416. doi:10.1111/j.1540-6261.1968.tb00815.x. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Portfolio Analytics · Finance
Sortino, F. A. and Price, L. N. · Paper
Presents performance measurement in which risk counts only returns that fall below an investor's target, rather than all variation of returns around the average. Our analytics cite it as the source of the Sortino ratio, a return-to-downside-risk measure used to compare portfolios whose losses and gains are not spread evenly around the average return. Citation: Sortino, F. A. and Price, L. N. (1994). Performance Measurement in a Downside Risk Framework. The Journal of Investing 3(3), 59-64. doi:10.3905/joi.3.3.59. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Portfolio Analytics · Finance
Dietz, P. O. · Book
A book on measuring the investment results of pension funds. It explains how to measure the rate of return a fund earns and tries to measure both return and risk and combine them into one evaluation, applied to six real funds over ten years. It is cited as the origin of the Dietz formula for returns with cash flows. Citation: Dietz, P. O. (1966). Pension Funds: Measuring Investment Performance. The Free Press, New York (Graduate School of Business, Columbia University), 166 pp.. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Portfolio Analytics · Finance
Herfindahl, O. C. · Paper
A doctoral thesis on concentration in the US steel industry: changes in pig iron and steel capacity, regional shares, the spread of value added among firms, price behaviour and the leading firm in good and bad years. Its first chapter proposes a measure of concentration, now known as the Herfindahl index. Our analytics cite it for that index. Citation: Herfindahl, O. C. (1950). Concentration in the Steel Industry. PhD dissertation, Faculty of Political Science, Columbia University. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Economics · Portfolio Analytics
Hirschman, A. O. · Book
A book on how the structure of a country's foreign trade can give it power over its trading partners. In it Hirschman proposed a measure of how concentrated trade is, closely related to the measure Herfindahl later used for industry concentration. Our analytics cite it, with Herfindahl, as a source of the Herfindahl-Hirschman concentration index. Citation: Hirschman, A. O. (1945). National Power and the Structure of Foreign Trade. University of California Press, Berkeley and Los Angeles. doi:10.1525/9780520378179. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Economics · Portfolio Analytics
Bhola, I., Krishnan, A. and NS, M. · Paper
Describes a system that first sends a small 7-billion-parameter model to explore a code repository and write checked notes about the problem, then picks which of four large coding models should fix it. On 266 Python tasks from SWE-bench Pro it solved 159, against 158 for the best single model, at about a fifth of the cost per solved task. Citation: Bhola, I., Krishnan, A. and NS, M. (2026). Scrouting: Cost-Aware Routing of Coding Agents by Scouting the Repository First. arXiv:2608.04804 [cs.SE], v1, 5 August 2026.
LLMs · AI
Forbes, K. J. and Rigobon, R. · Paper
Shows that the ordinary correlation between two stock markets depends on how volatile the markets are, so a rise in correlation after a shock can come from higher volatility rather than true contagion. The paper gives an adjustment for this bias and, applying it to the 1997 Asian crisis, the 1994 Mexican devaluation and the 1987 US crash, finds almost no increase, only a steady high level of comovement. It is the source of our volatility-adjusted correlation. Citation: Forbes, K. J. and Rigobon, R. (2002). No Contagion, Only Interdependence: Measuring Stock Market Comovements. The Journal of Finance 57(5), 2223-2261. doi:10.1111/0022-1082.00494. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Risk Management · Quant Finance
Engle, R. · Paper
Proposes the dynamic conditional correlation (DCC) model, in which the correlation between assets is allowed to change over time. Each asset keeps the flexibility of its own univariate GARCH volatility model, while a few parameters describe how the correlations move, and the model can be estimated simply in two steps from the likelihood. We use it to tell genuine shifts in correlation apart from changes in volatility. Citation: Engle, R. (2002). Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models. Journal of Business & Economic Statistics 20(3), 339-350. doi:10.1198/073500102288618487. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Risk Management · Quant Finance
Longin, F. and Solnik, B. · Paper
Uses extreme value theory to model the tails of returns and measure how correlated national stock markets are in large moves, taking losses and gains separately. It finds that the loss tail does not behave as a normal distribution would, while the gain tail does, and that correlation follows the market trend rather than volatility as such: it rises in bear markets but not in bull markets. It is our reference for exceedance correlations. Citation: Longin, F. and Solnik, B. (2001). Extreme Correlation of International Equity Markets. The Journal of Finance 56(2), 649-676. doi:10.1111/0022-1082.00340. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Risk Management · Quant Finance
Ang, A. and Chen, J. · Paper
Measures the correlation between US stock portfolios and the market separately for days when both fall below a threshold and days when both rise above one. It finds correlation much higher in down moves than in up moves, most of all in extreme falls, and tests which models can reproduce this; regime-switching models do best. We use its down-market versus up-market comparison as a reference for our tail analysis. Citation: Ang, A. and Chen, J. (2002). Asymmetric correlations of equity portfolios. Journal of Financial Economics 63(3), 443-494. doi:10.1016/s0304-405x(02)00068-5. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Risk Management · Quant Finance
Bai, J. and Perron, P. · Paper
The paper sets out how to estimate a regression whose coefficients shift at several unknown dates, how precisely those dates are pinned down, and how to test whether there are breaks and how many. We cite it for dating the breaks in a pair's correlation from the data. Citation: Bai, J. and Perron, P. (1998). Estimating and Testing Linear Models with Multiple Structural Changes. Econometrica 66(1), 47-78. doi:10.2307/2998540. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Statistics · Economics
Bai, J. and Perron, P. · Paper
The companion to the 1998 paper: a dynamic-programming algorithm that finds the best set of break dates for any number of breaks with a manageable amount of computation, and practical advice on choosing the number of breaks. We use its algorithm for the break dates. Citation: Bai, J. and Perron, P. (2003). Computation and Analysis of Multiple Structural Change Models. Journal of Applied Econometrics 18(1), 1-22. doi:10.1002/jae.659. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Statistics · Economics
Liu, J., Wu, S. and Zidek, J. V. · Paper
The paper studies regressions that take a different straight-line form in different ranges of the data, and estimates how many ranges there are with a modified Schwarz criterion that is shown to pick the right number as the sample grows. We use that criterion to choose the number of correlation breaks. Citation: Liu, J., Wu, S. and Zidek, J. V. (1997). On Segmented Multivariate Regression. Statistica Sinica 7(2), 497-525. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Statistics
Bartlett, M. S. · Paper
The paper shows that when two series are each autocorrelated, the usual test of their correlation counts too many independent observations, and corrects it with a smaller effective number of observations. We cite it for the effective sample size reported beside each correlation test. Citation: Bartlett, M. S. (1935). Some Aspects of the Time-Correlation Problem in Regard to Tests of Significance. Journal of the Royal Statistical Society 98(3), 536-543. doi:10.2307/2342284. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Statistics
Novy-Marx, R. and Velikov, M. · Paper
Studies 23 well-known stock return anomalies after trading costs. Strategies rebalanced once a year (size, value, profitability) trade little and keep most of their gross returns; strategies rebalanced monthly turn over 14% to 35% of each side a month and lose a large part of their returns to costs (momentum about half), and most of the highest-turnover strategies have negative net returns. A buy/hold spread, stricter for opening positions than for keeping them, is the most effective way to cut costs. We cite its turnover table for how fast each factor's portfolio changes. Citation: Novy-Marx, R. and Velikov, M. (2016). A Taxonomy of Anomalies and Their Trading Costs. The Review of Financial Studies 29(1), 104-147. doi:10.1093/rfs/hhv063. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Quant Finance · Finance
Asness, C., Chandra, S., Ilmanen, A. and Israel, R. · Paper
Tests whether investors can improve the value, momentum and defensive factors by timing them on how cheap or expensive each looks. The authors find no robust evidence that such value-based factor timing delivers meaningful outperformance. Cited for the weak evidence on factor timing. Citation: Asness, C., Chandra, S., Ilmanen, A. and Israel, R. (2017). Contrarian Factor Timing is Deceptively Difficult. The Journal of Portfolio Management 43(5), 72-87. doi:10.3905/jpm.2017.43.5.072. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Quant Finance · Finance
Little, J. D. C. · Paper
Proves the queuing formula L = λW: in a queuing system in a steady state, the average number of items in the system equals the average arrival rate times the average time an item spends in the system. The result is known as Little's law. Cited for the link between the number of open reconciliation breaks, the new breaks a day and how long breaks stay open. Citation: Little, J. D. C. (1961). A Proof for the Queuing Formula: L = λW. Operations Research 9(3), 383-387. doi:10.1287/opre.9.3.383. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Math · Investment Operations
Page, E. S. · Paper
Introduces the cumulative sum (CUSUM) scheme for detecting a change in a process: it adds up the deviations of successive observations from a reference value and signals when the running sum passes a limit, so a small, persistent shift is found sooner than by checking each observation against a fixed threshold. It is the basis of CUSUM charts in statistical process control. Citation: Page, E. S. (1954). Continuous Inspection Schemes. Biometrika 41(1-2), 100-115. doi:10.1093/biomet/41.1-2.100. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Statistics · Risk Management
Liu, F. T., Ting, K. M. and Zhou, Z.-H. · Paper
Proposes the isolation forest, an anomaly detection method that isolates observations with random splits on random features instead of modelling normal data: anomalies are few and different, so they are isolated in fewer splits, and the average path length over many random trees is the anomaly score. It needs no distance or density measure and scales to large data sets. Citation: Liu, F. T., Ting, K. M. and Zhou, Z.-H. (2008). Isolation Forest. 2008 Eighth IEEE International Conference on Data Mining, 413-422. doi:10.1109/ICDM.2008.17. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Machine Learning · Statistics
Friedman, J. H. · Paper
Develops gradient boosting: a model is built in stages, each stage fitting a small regression tree to the negative gradient of the loss of the model so far, which generalises boosting to any differentiable loss for regression and classification. Gradient-boosted decision trees are a common method for tabular data. Citation: Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. The Annals of Statistics 29(5), 1189-1232. doi:10.1214/aos/1013203451. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Machine Learning · Statistics
Gatev, E., Goetzmann, W. N. and Rouwenhorst, K. G. · Paper
Tests a trading rule used by Wall Street desks: match stocks into pairs whose normalised prices moved most closely over the past year, then, over the next six months, buy the cheaper and sell the dearer member when their prices drift apart by two historical standard deviations, and close when they meet again. Uses daily data on US stocks and reports the rule's excess returns and their risk, and how they relate to known risk factors. It is the standard reference for the distance method of pairs trading. Citation: Gatev, E., Goetzmann, W. N. and Rouwenhorst, K. G. (2006). Pairs Trading: Performance of a Relative-Value Arbitrage Rule. Review of Financial Studies 19(3), 797-827. doi:10.1093/rfs/hhj020. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Quant Finance
Engle, R. F. and Granger, C. W. J. · Paper
Shows that when two series each wander without a fixed level but some combination of them stays stationary, the series are tied together by an error-correction relation: a gap that opens between them is pulled back. It gives a two-step way to estimate the relation and tests of whether such a tie exists, based on whether the residual of a regression of one series on the other has a unit root. Its residual test is the usual first check of whether two prices move together over the long run. Citation: Engle, R. F. and Granger, C. W. J. (1987). Co-Integration and Error Correction: Representation, Estimation, and Testing. Econometrica 55(2), 251-276. doi:10.2307/1913236. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Statistics · Quant Finance
MacKinnon, J. G. · Paper
Gives tables from which critical values of unit-root and cointegration tests (Dickey-Fuller and Engle-Granger types) can be computed for any sample size. Large simulation experiments are summarised by response-surface regressions in which the critical value depends on the number of observations; the 2010 version adds more accurate results for up to twelve series and further test variants. It is the source of the critical values most statistical software uses for these tests. Citation: MacKinnon, J. G. (2010). Critical Values for Cointegration Tests. Queen's Economics Department Working Paper No. 1227, Queen's University. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Statistics · Quant Finance
Uhlenbeck, G. E. and Ornstein, L. S. · Paper
Works out the statistics of a particle in Brownian motion whose velocity is pulled back toward zero by friction while random impacts push it around. The resulting process, now named after the authors, drifts back toward a mean at a rate proportional to its distance from it, so a gap decays by half in a fixed time. In finance it is the standard model of a spread or rate that reverts to a long-run level. Citation: Uhlenbeck, G. E. and Ornstein, L. S. (1930). On the Theory of the Brownian Motion. Physical Review 36(5), 823-841. doi:10.1103/PhysRev.36.823. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Statistics · Math
Do, B. and Faff, R. · Paper
Revisits simple pairs trading (the distance method) on later US data. Confirms that its profits kept falling over time, finds that the strategy still performed strongly in long turbulent periods, including the then recent global financial crisis, and reports that refinements of the pair choice combined with other measures raised profits, by 22 basis points a month for bank stocks. Citation: Do, B. and Faff, R. (2010). Does Simple Pairs Trading Still Work?. Financial Analysts Journal 66(4), 83-95. doi:10.2469/faj.v66.n4.1. Not hosted here: its licence does not let us redistribute it, so the link goes to the publisher.
Quant Finance · Finance
Gyre Research · Gyre Publications
US stocks no longer tell you what Tokyo will do next morning. When US energy stocks rose during the New York day, Japan's Nikkei 225 tended to rise at the next open; over the last 12 months that link fell from +0.42 to −0.06, the largest 12-month drop on record. Section 4.4 A “safe” part of the stock market has been behaving like a bond. Consumer Staples shares have been falling when interest rates rise, more sharply than in 99% of their history since 1999; part of this is a more volatile market rather than a lasting change. Section 4.2 Treasuries stopped cushioning stock falls. Since the Fed began raising rates in 2022, the S&P 500 and the 10-year yield went from moving clearly together (+0.36) to about no relation (−0.10), so falling stocks no longer bring rising bond prices. Section 4.7 Bonds and foreign holdings now tend to lose at the same time. Rising US rates now come with a stronger dollar (−0.08 before, +0.31 since 2021), so when rates jump, bonds fall and the stronger dollar shrinks the value of unhedged foreign holdings. Section 4.7 Real estate stocks now track mortgage rates far more closely. The link went from weak (−0.11 to 2020) to strong (−0.62 in 2021–23, −0.51 since). Section 4.8 Expected, not found: a turning point at the pandemic. The data date the stock-bond turn to 1 April 2022 and the rate-dollar turn to 30 March 2022, when the Fed began raising rates. Section 4.7 Expected, not found: economic data that warns ahead of US stock moves. None of the indicators tested leads US stocks once clustered calm and stormy spells are allowed for; across the study, 1,280 of 1,956 findings of the ordinary test no longer held. Section 4.9 Expected, not found: energy as an early signal. Of 23 energy leads tested, only one kind held: the price of oil gives about two months' notice of the next US consumer price figure. Section 4.3 Expected, not found: relationships breaking in a turbulent year. Over the last 1, 3 and 6 months nothing moved outside its own history by more than chance alone would produce (139 flags against 119 by chance). Section 4.2 Explore your own ideas for free: every relation in this paper, and any pair of your own, can be measured in Gyre's free Covariance Calculator at gyreresearch.com/covariance.
Economics · Quant Finance · Statistics
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