Book - Advances in Financial Machine Learning by Marcos Lopez de Prado Dec 19 2019 16:15 languageMoneyScience
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Today's machine learning (ML) algorithms have conquered the major strategy games, and are routinely used to execute tasks once only possible by a limited group of experts. Over the next few years, ML algorithms will transform finance beyond anything we know today. Advances in Financial Machine Learning was written for the investment professionals and data scientists at the forefront of this evolution.
This one-of-a-kind, practical guidebook is your go-to resource of authoritative insight into using advanced ML solutions to overcome real-world investment problems. It demystifies the entire subject and unveils cutting-edge ML techniques specific to investing. With step-by-step clarity and purpose, it quickly brings you up to speed on fully proven approaches to data analysis, model research, and discovery evaluation. Then, it shines a light on the nuanced details behind innovative ways to extract informative features from financial data. To streamline implementation, it gives you valuable recipes for high-performance computing systems optimized to handle this type of financial data analysis.
Advances in Financial Machine Learning crosses the proverbial divide that separates academia and the industry. It does not advocate a theory merely because of its mathematical beauty, and it does not propose a solution just because it appears to work. The author transmits the kind of knowledge that only comes from experience, formalized in a rigorous manner.
This turnkey guide is designed to be immediately useful to the practitioner by featuring code snippets and hands-on exercises that facilitate the quick absorption and application of best practices in the real world.
Stop guessing and profit off data by:
- Tackling today's most challenging aspects of applying ML algorithms to financial strategies, including backtest overfitting
- Using improved tactics to structure financial data so it produces better outcomes with ML algorithms
- Conducting superior research with ML algorithms as well as accurately validating the solutions you discover
- Learning the tricks of the trade from one of the largest ML investment managers
Put yourself ahead of tomorrow's competition today with Advances in Financial Machine Learning.
About the Author
Prof. Marcos López de Prado is the CIO of True Positive Technologies (TPT), and professor of practice at Cornell University’s School of Engineering. He has over 20 years of experience developing investment strategies with the help of machine learning algorithms and supercomputers. He launched TPT after he sold some of his patents to AQR Capital Management, where he was a principal and AQR’s first head of machine learning. Marcos also founded and led Guggenheim Partners’ Quantitative Investment Strategies business, where he managed up to $13 billion in assets, and delivered an audited risk-adjusted return (information ratio) of 2.3.
Concurrently with the management of investments, since 2011 Marcos has been a research fellow at Lawrence Berkeley National Laboratory (U.S. Department of Energy, Office of Science). He has published dozens of scientific articles on machine learning and supercomputing in the leading academic journals, is a founding co-editor of The Journal of Financial Data Science, has testified before the U.S. Congress on AI policy, and SSRN ranks him as the most-read author in economics. Among several monographs, Marcos is the author of several graduate textbooks, including Advances in Financial Machine Learning (Wiley, 2018) and Machine Learning for Asset Managers (Cambridge University Press, forthcoming).
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 is a faculty member. Marcos has an Erdős #2 according to the American Mathematical Society, and in 2019, he received the ‘Quant of the Year Award’ from The Journal of Portfolio Management.