Advances in Financial Machine Learning

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John Wiley & Sons, Feb 21, 2018 - Business & Economics - 400 pages

Learn to understand and implement the latest machine learning innovations to improve your investment performance

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.

 

Contents

DATA ANALYSIS 27
23
Labeling
43
CONTENTS
65
Fractionally Differentiated Features
75
MODELLING
93
xii
100
Feature Importance
113
HyperParameter Tuning with CrossValidation
129
CONTENTS
208
75
212
Machine Learning Asset Allocation
221
Appendices
239
91
245
139
247
Entropy Features
263
Microstructural Features
281

CONTENTS
135
103
139
BACKTESTING
141
1
151
Backtesting through CrossValidation
161
xiv
166
Backtest Statistics
195
CONTENTS
283
HIGHPERFORMANCE COMPUTING RECIPES
303
xviii
317
HighPerformance Computational Intelligence and Forecasting
329
301
352
221
353

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About the author (2018)

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.