Abstract
Dynamic portfolio optimization has significantly benefited from a wider adoption of deep learning (DL). While existing research has focused on how DL can applied to solving the Hamilton–Jacobi–Bellman (HJB) equation, some very recent developments propose to forego the derivation of HJB in favor of empirical utility maximization over dynamic allocation strategies expressed through artificial neural networks. In addition to simplicity and transparency, this approach is universally applicable, as it is essentially agnostic about market dynamics. We apply it to optimal portfolio allocation between cash account and risky asset following Heston model. The results appear on par with theoretical ones.
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