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Portfolio optimization using deep learning with risk aversion utility function

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  • Kubo, Kenji
  • Nakagawa, Kei

Abstract

This paper explores portfolio optimization with deep learning (DL), which can model non-linear returns that traditional methods cannot capture. While Sharpe loss addresses the risk-return trade-off in DL-based portfolio construction, it has limitations, including interpretability issues with negative PnL and biased gradients under stochastic gradient descent (SGD). We propose a new loss function based on a risk-averse utility function, which provides unbiased gradients and clear interpretation even with negative PnL. Additionally, we use DL outputs as adjustments to baseline weights, achieving improved portfolio performance. Experiments on S&P 500 data show that our method outperforms Sharpe loss-based models across several metrics, including the Sharpe ratio.

Suggested Citation

  • Kubo, Kenji & Nakagawa, Kei, 2025. "Portfolio optimization using deep learning with risk aversion utility function," Finance Research Letters, Elsevier, vol. 74(C).
    • Handle: RePEc:eee:finlet:v:74:y:2025:i:c:s1544612325000261
    DOI: 10.1016/j.frl.2025.106761
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    References listed on IDEAS

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    1. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," Review of Finance, European Finance Association, vol. 33(5), pages 2223-2273.
    2. Victor DeMiguel & Lorenzo Garlappi & Raman Uppal, 2009. "Optimal Versus Naive Diversification: How Inefficient is the 1-N Portfolio Strategy?," The Review of Financial Studies, Society for Financial Studies, vol. 22(5), pages 1915-1953, May.
    3. Liu, Jianhe & Lu, Luze & Zong, Xiangyu & Xie, Baao, 2023. "Nonlinear relationships in soybean commodities Pairs trading-test by deep reinforcement learning," Finance Research Letters, Elsevier, vol. 58(PC).
    4. Tom Liu & Stephen Roberts & Stefan Zohren, 2023. "Deep Inception Networks: A General End-to-End Framework for Multi-asset Quantitative Strategies," Papers 2307.05522, arXiv.org.
    5. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
    6. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    7. Kieran Wood & Sven Giegerich & Stephen Roberts & Stefan Zohren, 2021. "Trading with the Momentum Transformer: An Intelligent and Interpretable Architecture," Papers 2112.08534, arXiv.org, revised Nov 2022.
    8. Horikawa, Hiroaki & Nakagawa, Kei, 2024. "Relationship between deep hedging and delta hedging: Leveraging a statistical arbitrage strategy," Finance Research Letters, Elsevier, vol. 62(PA).
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    Cited by:

    1. Arasteh, Abdollah, 2025. "A data-driven prediction method for multi-period portfolio optimization using the real options approach," Finance Research Letters, Elsevier, vol. 80(C).

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