Extent:
XIX, 366 Seiten
Illustrationen, Diagramme
23,5 x 16 cm
Type of publication: Book / Working Paper
Language: English
Notes:
Literaturangaben
Machine generated contents note: 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
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ISBN: 1-119-48208-9 ; 978-1-119-48208-6 ; 978-1-119-48211-6 ; 978-1-119-48210-9
Source:
ECONIS - Online Catalogue of the ZBW
Persistent link: https://www.econbiz.de/10011799864