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Portfolio optimization with feedback strategies based on artificial neural networks

Author

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  • Kopeliovich, Yaacov
  • Pokojovy, Michael

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.

Suggested Citation

  • Kopeliovich, Yaacov & Pokojovy, Michael, 2024. "Portfolio optimization with feedback strategies based on artificial neural networks," Finance Research Letters, Elsevier, vol. 69(PB).
    • Handle: RePEc:eee:finlet:v:69:y:2024:i:pb:s1544612324012145
    DOI: 10.1016/j.frl.2024.106185
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    More about this item

    Keywords

    Merton problem; Asset allocation; Deep learning; Artificial neural networks; Empirical risk minimization; Stochastic volatility; Heston model;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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