ISBN 13 : 978-0367639723

Contents :
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

Language : English

Series : CHAPMAN AND HALL / CRC FINANCIAL MATHEMATICS SERIES

Location : Nice Library

Material : Paper

Statement : Présent

Owner : Bibliothèque