Coqueret G., Guida T. Machine Learning for Factor Investing: R Version

CRC, 2020. — 342 p. — ISBN 9780367473228.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: R 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 R 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.Factor investing and asset pricing anomalies
Data preprocessing
Common supervised algorithms
Penalized regressions and sparse hedging for minimum variance portfolios
Tree-based methods
Neural networks
Support vector machines
Bayesian methods
From predictions to portfolios
Validating and tuning
Ensemble models
Portfolio backtesting
Further important topics
Interpretability
Two key concepts: causality and non-stationarity
Unsupervised learning
Reinforcement learning
Appendixes
Data description
Solutions to exercises