Advances in Financial Machine Learning
- 5h 19m
- Marcos López de Prado
- John Wiley & Sons (US)
- 2018
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.
About the Author
Dr. Marcos López de Prado manages multibillion dollar funds applying machine learning (ML) and supercomputing technologies. He founded Guggenheim Partners’ Quantitative Investment Strategies (QIS) business, where he developed high-capacity ML strategies that consistently delivered superior risk-adjusted returns. After managing up to $13 billion in assets, Marcos acquired QIS and spun-out that business from Guggenheim in 2018.
In addition to managing funds, between 2011 and 2018 Marcos was also a research fellow at Lawrence Berkeley National Laboratory (U.S. Department of Energy, Office of Science). One of the top-10 most read authors in finance (SSRN's rankings), he has published dozens of scientific articles on ML and supercomputing in the leading academic journals, and he holds multiple international patent applications on algorithmic trading. Among several books, Marcos is the author of "Advances in Financial Machine Learning" (Wiley, 2018).
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 Financial ML course at the School of Engineering. Marcos has an Erdős #2 and an Einstein #4 according to the American Mathematical Society.
In this Book
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Financial Machine Learning as a Distinct Subject
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Financial Data Structures
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Labeling
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Sample Weights
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Fractionally Differentiated Features
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Ensemble Methods
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Cross-Validation in Finance
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Feature Importance
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Hyper-Parameter Tuning with Cross-Validation
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Bet Sizing
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The Dangers of Backtesting
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Backtesting Through Cross-Validation
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Backtesting on Synthetic Data
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Backtest Statistics
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Understanding Strategy Risk
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Machine Learning Asset Allocation
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Structural Breaks
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Entropy Features
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Microstructural Features
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Multiprocessing and Vectorization
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Brute Force and Quantum Computers
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High-Performance Computational Intelligence and Forecasting Technologies
