Guillaume Coqueret, Tony Guida

Machine Learning for Factor Investing (eBook, PDF)

Python Version

• Format: PDF

Produktbeschreibung

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 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.

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Produktdetails

• Verlag: Taylor & Francis eBooks
• Seitenzahl: 358
• Erscheinungstermin: 8. August 2023
• Englisch
• ISBN-13: 9781000912807
• Artikelnr.: 68206432

Autorenporträt

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.

Inhaltsangabe

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