Machine learning in finance : from theory to practice / Matthew F. Dixon, Igor Halperin, Paul Bilokon.
Dixon, Matthew F.; Halperin, Igor.; Bilokon, Paul A., 1982-
2020
HG173
Available Online

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Title Machine learning in finance : from theory to practice / Matthew F. Dixon, Igor Halperin, Paul Bilokon.
Author Dixon, Matthew F.
ISBN 9783030410681 (electronic book)
3030410684 (electronic book)
9783030410674
DOI https://doi.org/10.1007/978-3-030-41
Publication Details Cham : Springer, 2020.
Language English
Description 1 online resource (565 pages)
Item Number 10.1007/978-3-030-41
Call Number HG173
Dewey Decimal Classification 330.0285/631
Summary 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.
Bibliography, etc. Note Includes bibliographical references index.
Access Note Access limited to authorized users.
Source of Description Description based on print version record.
Added Author Halperin, Igor.
Bilokon, Paul A., 1982-
Available in Other Form Machine Learning in Finance : From Theory to Practice
Linked Resources Online Access
Record Appears in Online Resources > Ebooks
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Chapter 1. Introduction
Chapter 2. Probabilistic Modeling
Chapter 3. Bayesian Regression & Gaussian Processes
Chapter 4. Feed Forward Neural Networks
Chapter 5. Interpretability
Chapter 6. Sequence Modeling
Chapter 7. Probabilistic Sequence Modeling
Chapter 8. Advanced Neural Networks
Chapter 9. Introduction to Reinforcement learning
Chapter 10. Applications of Reinforcement Learning
Chapter 11. Inverse Reinforcement Learning and Imitation Learning
Chapter 12. Frontiers of Machine Learning and Finance.

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