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 (usually confidential) techniques used by asset managers 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; recent techniques such as neural networks and reinforcement learning, and more classical ones, including nonlinear and linear programming, principal component analysis, dynamic programming, and clustering. The authors use technical and nontechnical arguments to accommodate readers with different levels of mathematical preparation. The book is easy to read yet rigorous and contains a large number of exercises.

About the Authors

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. He has two patents and has published various academic works on financial engineering, stochastic analysis, operations research, and epidemiology.

Tianmin Kong is a Ph.D. candidate in engineering and computational mathematics at Claremont Graduate University and California State University, Long Beach.

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