Machine Learning for Factor Investing : Python Version book cover
1st Edition

Machine Learning for Factor Investing
Python Version




  • Available for pre-order on June 19, 2023. Item will ship after July 10, 2023
ISBN 9780367639723
July 10, 2023 Forthcoming by Chapman & Hall
368 Pages 81 Color & 7 B/W Illustrations

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USD $89.95

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Book Description

Machine learning (ML) is progressively reshaping the fields of quantitative finance and algorithmic trading. ML tools are increasingly adopted by hedge funds and asset managers, notably for alpha signal generation and stocks selection. The technicality of the subject can make it hard for non-specialists to join the bandwagon, as the jargon and coding requirements may seem out-of-reach. Machine learning for factor investing: Python version bridges this gap. It provides a comprehensive tour of modern ML-based investment strategies that rely on firm characteristics.

The book covers a wide array of subjects which range from economic rationales to rigorous portfolio back-testing and encompass both data processing and model interpretability. Common supervised learning algorithms such as tree models and neural networks are explained in the context of style investing and the reader can also dig into more complex techniques like autoencoder asset returns, Bayesian additive trees and causal models.

All topics are illustrated with self-contained Python code samples and snippets that are applied to a large public dataset that contains over 90 predictors. The material, along with the content of the book, is available online so that readers can reproduce and enhance the examples at their convenience. If you have even a basic knowledge of quantitative finance, this combination of theoretical concepts and practical illustrations will help you learn quickly and deepen your financial and technical expertise.

Table of Contents

Part 1. Introduction  1. Notations and data  2. Introduction  3. Factor investing and asset pricing anomalies  4. Data preprocessing  Part 2. Common supervised algorithms  5. Penalized regressions and sparse hedging for minimum variance portfolios  6. Tree-based methods  7. Neural networks  8. Support vector machines  9. Bayesian methods  Part 3. From predictions to portfolios  10. Validating and tuning  11. Ensemble models  12. Portfolio backtesting  Part 4. Further important topics  13. Interpretability  14. Two key concepts: causality and non-stationarity  15. Unsupervised learning  16. Reinforcement learning  Part 5. Appendix  17. Data description  18. Solutions to exercises

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Author(s)

Biography

Guillaume Coqueret is associate professor of finance and data science at EMLYON Business School. His recent research revolves around applications of machine learning tools in financial economics.

Tony Guida is co-head of Systematic Macro at RAM Active Investments. He is the editor and co-author of Big Data and Machine Learning in Quantitative Investment.