Advances in Financial Machine Learning (eBook)

Marcos Lopez de Prado (Autor)

eBook Download: EPUB

2018 | 1. Auflage
400 Seiten
John Wiley & Sons (Verlag)
978-1-119-48210-9 (ISBN)

Infos

Learn to understand and implement the latest machine learning innovations to improve your investment performance

Machine learning (ML) is changing virtually every aspect of our lives. Today, ML algorithms accomplish tasks that - until recently - only expert humans could perform. And finance is ripe for disruptive innovations that will transform how the following generations understand money and invest.

In the book, readers will learn how to:

* Structure big data in a way that is amenable to ML algorithms

* Conduct research with ML algorithms on big data

* Use supercomputing methods and back test their discoveries while avoiding false positives

Advances in Financial Machine Learning addresses real life problems faced by practitioners every day, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their individual setting.

Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance.

Autor

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.

Inhalt

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

Produkt-Details

• Erscheint lt. Verlag: 2.2.2018
• Sprache: englisch
• Themenwelt: Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
• Themenwelt: Recht / Steuern ► Wirtschaftsrecht
• Themenwelt: Wirtschaft ► Betriebswirtschaft / Management ► Finanzierung
• Schlagworte: Artificial Intelligence • Computer Science • Finance & Investments • Finance & Investments Special Topics • Finanz- u. Anlagewesen • Informatik • Künstliche Intelligenz • Spezialthemen Finanz- u. Anlagewesen
• ISBN-10: 1-119-48210-0 / 1119482100
• ISBN-13: 978-1-119-48210-9 / 9781119482109