Stochastic Analysis, Filtering, and Stochastic Optimization 2022
DOI: 10.1007/978-3-030-98519-6_5
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Discrete-Time Portfolio Optimization under Maximum Drawdown Constraint with Partial Information and Deep Learning Resolution

Abstract: We study a discrete-time portfolio selection problem with partial information and maximum drawdown constraint. Drift uncertainty in the multidimensional framework is modeled by a prior probability distribution. In this Bayesian framework, we derive the dynamic programming equation using an appropriate change of measure, and obtain semi-explicit results in the Gaussian case. The latter case, with a CRRA utility function is completely solved numerically using recent deep learning techniques for stochastic optima… Show more

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