000			01631nam a22002177a 4500
005			20250313200215.0
008			250313b |||||||| |||| 00| 0 eng d
020			_a9781611977899
082			_a332.0285 _bSCH
100			_aSchellhorn, Henry _922388
245			_aMachine learning for asset management and pricing
260			_bSociety for Industrial and Applied Mathematic _aPhiladelphia _c2024
300			_axxiii, 242 p.
365			_aUSD _b74.00
520			_ahis 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)
650			_aFinance--Data processing
650			_aFinance--Mathematical models
650			_aMachine learning
700			_aKong, Tianmin _922389
942			_cBK _2ddc
999			_c8657 _d8657