Advances in Financial Machine Learning (eBook)
400 Seiten
John Wiley & Sons (Verlag)
978-1-119-48210-9 (ISBN)
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.
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
Erscheint lt. Verlag | 2.2.2018 |
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Sprache | englisch |
Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
Recht / Steuern ► Wirtschaftsrecht | |
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 |
Haben Sie eine Frage zum Produkt? |
Größe: 13,7 MB
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