Machine learning for asset management and pricing

By:

Schellhorn, Henry

Contributor(s):

Kong, Tianmin

Material type: Text

TextPublication details: Society for Industrial and Applied Mathematic Philadelphia 2024Description: xxiii, 242 pISBN:

9781611977899

Subject(s):

DDC classification:

332.0285 SCH

Summary: his 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; and 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. (https://epubs.siam.org/doi/book/10.1137/1.9781611977905)

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Holdings
Item type	Current library	Collection	Call number	Copy number	Status	Date due	Barcode
Book	Indian Institute of Management LRC General Stacks	Finance & Accounting	332.0285 SCH (Browse shelf(Opens below))	1	Available		007727

his 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; and

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.

(https://epubs.siam.org/doi/book/10.1137/1.9781611977905)

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