You're reading from Machine Learning for Algorithmic Trading - Second Edition

Product type Book

Published in Jul 2020

Publisher Packt

ISBN-13 9781839217715

Pages 822 pages

Edition 2nd Edition

Languages

Python

Concepts

Machine Learning

Author (1):

Stefan Jansen

Table of Contents (27) Chapters

Preface

1. Machine Learning for Trading – From Idea to Execution

2. Market and Fundamental Data – Sources and Techniques

3. Alternative Data for Finance – Categories and Use Cases

4. Financial Feature Engineering – How to Research Alpha Factors

5. Portfolio Optimization and Performance Evaluation

6. The Machine Learning Process

7. Linear Models – From Risk Factors to Return Forecasts

8. The ML4T Workflow – From Model to Strategy Backtesting

9. Time-Series Models for Volatility Forecasts and Statistical Arbitrage

10. Bayesian ML – Dynamic Sharpe Ratios and Pairs Trading

11. Random Forests – A Long-Short Strategy for Japanese Stocks

12. Boosting Your Trading Strategy

13. Data-Driven Risk Factors and Asset Allocation with Unsupervised Learning

14. Text Data for Trading – Sentiment Analysis

15. Topic Modeling – Summarizing Financial News

16. Word Embeddings for Earnings Calls and SEC Filings

17. Deep Learning for Trading

18. CNNs for Financial Time Series and Satellite Images

19. RNNs for Multivariate Time Series and Sentiment Analysis

20. Autoencoders for Conditional Risk Factors and Asset Pricing

21. Generative Adversarial Networks for Synthetic Time-Series Data

22. Deep Reinforcement Learning – Building a Trading Agent

23. Conclusions and Next Steps

24. References

25. Index

Appendix: Alpha Factor Library

Summary

In this chapter, we explored linear time-series models for the univariate case of individual series, as well as multivariate models for several interacting series. We encountered applications that predict macro fundamentals, models that forecast asset or portfolio volatility with widespread use in risk management, and multivariate VAR models that capture the dynamics of multiple macro series. We also looked at the concept of cointegration, which underpins the popular pair-trading strategy.

Similar to Chapter 7, Linear Models – From Risk Factors to Return Forecasts, we saw how linear models impose a lot of structure, that is, they make strong assumptions that potentially require transformations and extensive testing to verify that these assumptions are met. If they are, model-training and interpretation are straightforward, and the models provide a good baseline that more complex models may be able to improve on. In the next two chapters, we will see two examples...