Machine Learning for Factor Investing: R Version

✍ Scribed by Guillaume Coqueret, Tony Guida

• Publisher: Chapman and Hall/CRC
• Year: 2020
• Tongue: English
• Leaves: 342
• Series: Chapman & Hall/CRC Financial Mathematics Series
• Edition: 1
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Dedication
Contents
Preface
I Introduction
1 Notations and data
2 Introduction
3 Factor investing and asset pricing anomalies
4 Data preprocessing
II 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
III From predictions to portfolios
10 Validating and tuning
11 Ensemble models
12 Portfolio backtesting
IV Further important topics
13 Interpretability
14 Two key concepts: causality and non-stationarity
15 Unsupervised learning
16 Reinforcement learning
V Appendix
17 Data description
18 Solutions to exercises
Bibliography
Index