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This research focuses on improving portfolio optimization by using deep learning techniques to handle uncertainties in asset returns.

Data-driven approach to robust portfolio optimization using deep neural networks

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This research focuses on improving portfolio optimization by using deep learning techniques to handle uncertainties in asset returns.
Applied deep learning to create a non-convex data-driven uncertainty set.
Utilized an iterative constraint generation algorithm to solve linear and mixed-integer linear programs.
Conducted empirical analysis on eight global market indices for performance comparison.
The proposed model outperforms naïve and robust strategies in reward-risk ratios across most datasets.
Achieved the lowest negative returns and risk during bearish market phases for the Dow Jones.
Demonstrated superior performance with higher reward-risk ratios during bullish market phases.

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Randomized trial demonstrates enhanced portfolio performance in global markets, suggesting improved financial decision-making.

Garg et al. (2026) studied this question.

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