Machine Learning for Factor Investing: Python Version

Machine learning (ML) is increasingly used in quantitative finance and algorithmic trading, particularly for alpha signal generation and stocks selection. This book provides a comprehensive tour of modern ML-based investment strategies that rely on firm characteristics, covering topics such as economic rationales, rigorous portfolio back-testing, data processing, and model interpretability. It is illustrated with self-contained Python code samples and snippets applied to a large public dataset, making it accessible to non-specialists with a basic knowledge of quantitative finance.

Format: Hardback
Length: 340 pages
Publication date: 08 August 2023
Publisher: Taylor & Francis Ltd

Machine learning (ML) is revolutionizing the fields of quantitative finance and algorithmic trading, with hedge funds and asset managers increasingly embracing its power for alpha signal generation and stock selection. However, the technical complexity of the subject can make it challenging for non-specialists to join the bandwagon. To address this gap, Machine learning for factor investing: Python version offers a comprehensive guide to modern ML-based investment strategies that leverage firm characteristics. The book covers a wide range of topics, from economic rationales to rigorous portfolio back-testing, encompassing data processing and model interpretability. It delves into common supervised learning algorithms such as tree models and neural networks, as well as more advanced techniques like autoencoder asset returns, Bayesian additive trees, and causal models. Each topic is illustrated with self-contained Python code samples and snippets applied to a large public dataset containing over 90 predictors. The material is freely available online, allowing readers to reproduce and enhance the examples at their convenience. Whether you have a basic understanding of quantitative finance or are seeking to expand your financial and technical expertise, this book provides a valuable resource for learning quickly and deepening your knowledge in the field.

Weight: 894g
Dimension: 184 x 262 x 23 (mm)
ISBN-13: 9780367639747