Anmerkungen:
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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Beschreibung:
"Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Readers will learn how to structure Big data in a way that is amenable to ML algorithms; how to conduct research with ML algorithms on that data; how to use supercomputing methods; how to backtest your discoveries while avoiding false positives. The book addresses real-life problems faced by practitioners on a daily basis, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their particular 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."
"This book begins by structuring financial data in a way that is amenable to machine learning (ML) algorithms. Then, the author discusses how to conduct research with ML algorithms on that data and how to backtest your discoveries. Most of the problems and solutions are explained using math, supported by code. This makes the book very practical and hands-on. Readers become active users who can test the solutions proposed in their work. Readers will learn how to structure, label, weight, and backtest data. Machine learning is the future, and this book will equip investment professionals with the tools to utilize it moving forward."