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Enhancing Portfolio Optimization with Deep Learning Insights

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Listed:
  • Brandon Luo
  • Jim Skufca

Abstract

Our work focuses on deep learning (DL) portfolio optimization, tackling challenges in long-only, multi-asset strategies across market cycles. We propose training models with limited regime data using pre-training techniques and leveraging transformer architectures for state variable inclusion. Evaluating our approach against traditional methods shows promising results, demonstrating our models' resilience in volatile markets. These findings emphasize the evolving landscape of DL-driven portfolio optimization, stressing the need for adaptive strategies to navigate dynamic market conditions and improve predictive accuracy.

Suggested Citation

  • Brandon Luo & Jim Skufca, 2026. "Enhancing Portfolio Optimization with Deep Learning Insights," Papers 2601.07942, arXiv.org.
    • Handle: RePEc:arx:papers:2601.07942
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    File URL: http://arxiv.org/pdf/2601.07942
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    References listed on IDEAS

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    1. Chao Zhang & Zihao Zhang & Mihai Cucuringu & Stefan Zohren, 2021. "A Universal End-to-End Approach to Portfolio Optimization via Deep Learning," Papers 2111.09170, arXiv.org.
    2. Fuli Feng & Xiangnan He & Xiang Wang & Cheng Luo & Yiqun Liu & Tat-Seng Chua, 2018. "Temporal Relational Ranking for Stock Prediction," Papers 1809.09441, arXiv.org, revised Jan 2019.
    3. Stephen A. Ross, 2013. "The Arbitrage Theory of Capital Asset Pricing," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 1, pages 11-30, World Scientific Publishing Co. Pte. Ltd..
    4. Zexin Hu & Yiqi Zhao & Matloob Khushi, 2021. "A Survey of Forex and Stock Price Prediction Using Deep Learning," Papers 2103.09750, arXiv.org.
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