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Machine Learning in Finance

From Theory to Practice

Authors: Dixon, Matthew F., Halperin, Igor, Bilokon, Paul

  • 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
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eBook 76,99 €
price for India (gross)
  • The eBook version of this title will be available soon
  • Due: June 13, 2020
  • ISBN 978-3-030-41068-1
  • Digitally watermarked, DRM-free
  • Included format:
  • ebooks can be used on all reading devices
Hardcover 89,99 €
price for India (gross)
  • Due: June 13, 2020
  • ISBN 978-3-030-41067-4
  • Free shipping for individuals worldwide
About this Textbook

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.

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.

Buy this book

eBook 76,99 €
price for India (gross)
  • The eBook version of this title will be available soon
  • Due: June 13, 2020
  • ISBN 978-3-030-41068-1
  • Digitally watermarked, DRM-free
  • Included format:
  • ebooks can be used on all reading devices
Hardcover 89,99 €
price for India (gross)
  • Due: June 13, 2020
  • ISBN 978-3-030-41067-4
  • Free shipping for individuals worldwide
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Bibliographic Information

Bibliographic Information
Book Title
Machine Learning in Finance
Book Subtitle
From Theory to Practice
Authors
Copyright
2020
Publisher
Springer International Publishing
Copyright Holder
Springer Nature Switzerland AG
eBook ISBN
978-3-030-41068-1
DOI
10.1007/978-3-030-41068-1
Hardcover ISBN
978-3-030-41067-4
Edition Number
1
Number of Pages
VIII, 674
Number of Illustrations
12 b/w illustrations, 82 illustrations in colour
Topics