Machine Learning in Finance : From Theory to Practice. 1st ed. 2020

種類:

電子ブック

責任表示:

by Matthew F. Dixon, Igor Halperin, Paul Bilokon

出版情報:

Cham : Springer International Publishing : Imprint: Springer, 2020

著者名:

• Dixon, Matthew F.
• Halperin, Igor.
• Bilokon, Paul.
• SpringerLink (Online service)

ISBN:

9783030410681 [3030410684]    

注記:

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.
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

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