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Machine Learning for Factor Investing
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Table of contents
Preface
Introduction
1
Notations and data
2
Introduction
3
Factor investing and asset pricing anomalies
4
Data preprocessing
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
From predictions to portfolios
10
Validating and tuning
11
Ensemble models
12
Portfolio backtesting
Further important topics
13
Interpretability
14
Two key concepts: causality and non-stationarity
15
Unsupervised learning
16
Reinforcement learning
Appendix
17
Data description
18
Python notebooks
19
Solutions to exercises
18.1
Python notebooks: chapter 1
Back to Python notebooks
17
Data description
19
Solutions to exercises
On this page
18
Python notebooks