Machine Learning for Asset Management and Pricing
Seiten
2024
Society for Industrial & Applied Mathematics,U.S. (Verlag)
978-1-61197-789-9 (ISBN)
Society for Industrial & Applied Mathematics,U.S. (Verlag)
978-1-61197-789-9 (ISBN)
Covers the latest advances in machine-learning methods for asset management and asset pricing. Cutting-edge material is integrated with mainstream finance theory and statistical methods to provide a coherent narrative.
This textbook covers the latest advances in machine-learning methods for asset management and asset pricing. Recent research in deep learning applied to finance shows that some of the techniques used by asset managers (usually kept confidential) result in better investments than the more standard techniques. Cutting-edge material is integrated with mainstream finance theory and statistical methods to provide a coherent narrative. Coverage includes
an original machine learning method for strategic asset allocation;
the no-arbitrage theory applied to a wide portfolio of assets as well as other asset management methods, such as mean-variance, Bayesian methods, linear factor models, and strategic asset allocation; and
techniques other than neural networks, such as nonlinear and linear programming, principal component analysis, reinforcement learning, dynamic programming, and clustering.
The authors use technical and nontechnical arguments to accommodate readers with different levels of mathematical preparation. Readers will find the book easy to read yet rigorous and a large number of exercises.
This textbook covers the latest advances in machine-learning methods for asset management and asset pricing. Recent research in deep learning applied to finance shows that some of the techniques used by asset managers (usually kept confidential) result in better investments than the more standard techniques. Cutting-edge material is integrated with mainstream finance theory and statistical methods to provide a coherent narrative. Coverage includes
an original machine learning method for strategic asset allocation;
the no-arbitrage theory applied to a wide portfolio of assets as well as other asset management methods, such as mean-variance, Bayesian methods, linear factor models, and strategic asset allocation; and
techniques other than neural networks, such as nonlinear and linear programming, principal component analysis, reinforcement learning, dynamic programming, and clustering.
The authors use technical and nontechnical arguments to accommodate readers with different levels of mathematical preparation. Readers will find the book easy to read yet rigorous and a large number of exercises.
Henry Schellhorn is a professor of mathematics at Claremont Graduate University, where he directs the financial engineering program. He was an assistant professor of finance at the University of Lausanne. Before entering academia, he worked in the financial software industry in California and Switzerland. His publications are in financial engineering, stochastic analysis, operations research, and epidemiology and he has two patents. Tianmin Kong is a Ph.D. candidate in engineering and computational mathematics at Claremont Graduate University and California State University, Long Beach.
| Erscheinungsdatum | 05.04.2024 |
|---|---|
| Verlagsort | New York |
| Sprache | englisch |
| Gewicht | 261 g |
| Themenwelt | Mathematik / Informatik ► Informatik ► Theorie / Studium |
| Mathematik / Informatik ► Mathematik ► Angewandte Mathematik | |
| Wirtschaft ► Betriebswirtschaft / Management ► Finanzierung | |
| ISBN-10 | 1-61197-789-4 / 1611977894 |
| ISBN-13 | 978-1-61197-789-9 / 9781611977899 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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