Portfolio optimization using deep learning with risk aversion utility function

IF 7.4 2区经济学 Q1 BUSINESS, FINANCE

Finance Research Letters Pub Date : 2025-01-13 DOI:10.1016/j.frl.2025.106761

Kenji Kubo, Kei Nakagawa

引用次数: 0

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.

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基于风险规避效用函数的深度学习组合优化

本文探讨了用深度学习（DL）进行投资组合优化，它可以模拟传统方法无法捕获的非线性回报。虽然夏普损失解决了基于dl的投资组合构建中的风险回报权衡问题，但它也有局限性，包括负PnL和随机梯度下降（SGD）下的偏梯度的可解释性问题。我们提出了一种新的基于风险规避效用函数的损失函数，即使是负PnL，它也能提供无偏梯度和清晰的解释。此外，我们使用深度学习输出作为基准权重的调整，以实现改进的投资组合绩效。对标普500指数数据的实验表明，我们的方法在包括夏普比率在内的几个指标上都优于基于夏普损失的模型。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Finance Research Letters BUSINESS, FINANCE-

CiteScore

11.10

自引率

14.40%

发文量

863

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