© 2020

Machine Learning in Finance

From Theory to Practice


  • Introduces fundamental concepts in machine learning for canonical modeling and decision frameworks in finance

  • Presents a unified treatment of machine learning, financial econometrics and discrete time stochastic control problems in finance

  • Chapters include examples, exercises and Python codes to reinforce theoretical concepts and demonstrate the application of machine learning to algorithmic trading, investment management, wealth management and risk management


Table of contents

  1. Front Matter
    Pages i-xxv
  2. Machine Learning with Cross-Sectional Data

    1. Front Matter
      Pages 1-1
    2. Matthew F. Dixon, Igor Halperin, Paul Bilokon
      Pages 3-46
    3. Matthew F. Dixon, Igor Halperin, Paul Bilokon
      Pages 47-80
    4. Matthew F. Dixon, Igor Halperin, Paul Bilokon
      Pages 81-109
    5. Matthew F. Dixon, Igor Halperin, Paul Bilokon
      Pages 111-166
    6. Matthew F. Dixon, Igor Halperin, Paul Bilokon
      Pages 167-188
  3. Sequential Learning

    1. Front Matter
      Pages 189-189
    2. Matthew F. Dixon, Igor Halperin, Paul Bilokon
      Pages 191-220
    3. Matthew F. Dixon, Igor Halperin, Paul Bilokon
      Pages 221-238
    4. Matthew F. Dixon, Igor Halperin, Paul Bilokon
      Pages 239-276
  4. Sequential Data with Decision-Making

    1. Front Matter
      Pages 277-277
    2. Matthew F. Dixon, Igor Halperin, Paul Bilokon
      Pages 279-345
    3. Matthew F. Dixon, Igor Halperin, Paul Bilokon
      Pages 347-418
    4. Matthew F. Dixon, Igor Halperin, Paul Bilokon
      Pages 419-517
    5. Matthew F. Dixon, Igor Halperin, Paul Bilokon
      Pages 519-541
  5. Back Matter
    Pages 543-548

About this book


This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of algorithm for financial data modeling and decision making. With the trend towards increasing computational resources and larger datasets, machine learning has grown into an important skillset for the finance industry. This book is written for advanced graduate students and academics in financial econometrics, mathematical finance and applied statistics, in addition to quants and data scientists in the field of quantitative finance.

Machine Learning in Finance: From Theory to Practice is divided into three parts, each part covering theory and applications. The first presents supervised learning for cross-sectional data from both a Bayesian and frequentist perspective. The more advanced material places a firm emphasis on neural networks, including deep learning, as well as Gaussian processes, with examples in investment management and derivative modeling. The second part presents supervised learning for time series data, arguably the most common data type used in finance with examples in trading, stochastic volatility and fixed income modeling. Finally, the third part presents reinforcement learning and its applications in trading, investment and wealth management. Python code examples are provided to support the readers' understanding of the methodologies and applications. The book also includes more than 80 mathematical and programming exercises, with worked solutions available to instructors. As a bridge to research in this emergent field, the final chapter presents the frontiers of machine learning in finance from a researcher's perspective, highlighting how many well-known concepts in statistical physics are likely to emerge as important methodologies for machine learning in finance.


Machine Learning Financial Mathematics Financial Econometrics Neural Networks Bayesian Neural Networks Reinforcement Learning Time Series Modeling Investment Management Wealth Management

Authors and affiliations

  1. 1.Department of Applied MathematicsIllinois Institute of TechnologyChicagoUSA
  2. 2.Tandon School of EngineeringNew York UniversityBrooklynUSA
  3. 3.Department of MathematicsImperial College LondonLondonUK

About the authors

Paul Bilokon, Ph.D., is CEO and Founder of Thalesians Ltd. Paul has made contributions to mathematical logic, domain theory, and stochastic filtering theory, and, with Abbas Edalat, has published a prestigious LICS paper. He is a member of the British Computer Society, the Institution of Engineering and the European Complex Systems Society.

Matthew Dixon, FRM, Ph.D., is an Assistant Professor of Applied Math at the Illinois Institute of Technology and an Affiliate of the Stuart School of Business. He has published over 20 peer reviewed publications on machine learning and quant finance and has been cited in Bloomberg Markets and the Financial Times as an AI in fintech expert. He is Deputy Editor of the Journal of Machine Learning in Finance, Associate Editor of the AIMS Journal on Dynamics and Games, and is a member of the Advisory Board of the CFA Quantitative Investing Group.

Igor Halperin, Ph.D., is a Research Professor in Financial Engineering at NYU, and an AI Research associate at Fidelity Investments. Igor has published more than 50 scientific articles in machine learning, quantitative finance and theoretic physics. Prior to joining the financial industry, he held postdoctoral positions in theoretical physics at the Technion and the University of British Columbia.

Bibliographic information

Industry Sectors
Finance, Business & Banking
Oil, Gas & Geosciences


“This volume aims to present a broad yet technical treatment of (ML) algorithms used by financial practitioners and scholars alike. … the book fills a large void. … This encourages reproducibility as well as learning by doing, which is highly appreciated.” (Guillaume Coqueret, Quantitative Finance, October 15, 2020)