Advances in Financial Machine Learning (eBook)

DR. MARCOS LÓPEZ DE PRADO is a principal at AQR Capital Management, and its head of machine learning. Marcos is also a research fellow at Lawrence Berkeley National Laboratory (U.S. Department of Energy, Office of Science). SSRN ranks him as one of the most-read authors in economics, and he has published dozens of scientific articles on machine learning and supercomputing in the leading academic journals. Marcos earned a PhD in financial economics (2003), a second PhD in mathematical finance (2011) from Universidad Complutense de Madrid, and is a recipient of Spain's National Award for Academic Excellence (1999). He completed his post-doctoral research at Harvard University and Cornell University, where he teaches a graduate course in financial machine learning at the School of Engineering. Marcos has an Erdös #2 and an Einstein #4 according to the American Mathematical Society.

About the Author

Preamble

1. Financial Machine Learning as a Distinct Subject

Part 1: Data Analysis

2. Financial Data Structures

3. Labeling

4. Sample Weights

5. Fractionally Differentiated Features

Part 2: Modelling

6. Ensemble Methods

7. Cross-validation in Finance

8. Feature Importance

9. Hyper-parameter Tuning with Cross-Validation

Part 3: Backtesting

10. Bet Sizing

11. The Dangers of Backtesting

12. Backtesting through Cross-Validation

13. Backtesting on Synthetic Data

14. Backtest Statistics

15. Understanding Strategy Risk

16. Machine Learning Asset Allocation

Part 4: Useful Financial Features

17. Structural Breaks

18. Entropy Features

19. Microstructural Features

Part 5: High-Performance Computing Recipes

20. Multiprocessing and Vectorization

21. Brute Force and Quantum Computers

22. High-Performance Computational Intelligence and Forecasting Technologies

Dr. Kesheng Wu and Dr. Horst Simon

Index