Integrating Large Language Models and CNN-LSTM for Enhanced Portfolio Optimization

Arega Denekew (), Tesfahun Berehane () and Molalign Adam ()
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Arega Denekew: Bahir Dar University
Tesfahun Berehane: Bahir Dar University
Molalign Adam: Bahir Dar University

SN Operations Research Forum, 2025, vol. 6, issue 4, 1-31

Abstract: Abstract Portfolio optimization is a core financial problem that involves balancing risk and return. Although Modern Portfolio Theory (MPT) gives a fundamental framework, its reliance on Gaussian distributions of returns often fails in real-world practice. Recent deep learning developments present data-driven solutions; such approaches are typically marred by a lack of interpretability and theoretical foundation. In this paper, we present a hybrid deep learning approach (CNN-LSTM) augmented with large language models (LLMs) for enhanced prediction and explainability. Our approach employs a convolutional LSTM network to forecast asset prices, followed by mean-variance optimization. For additional performance improvement, we integrate LLM-based sentiment analysis of financial news, enabling real-time portfolio weight adjustments. On a range of ETFs (VTI, AGG, DBC, VXX), experiments demonstrate our model achieves a 1.623 Sharpe ratio outperforming traditional approaches (MVO, MAD, CVaR) by $$\varvec{104-300\%}$$ 104 - 300 % and the deep learning state-of-the-art baselines. The incorporation of LLM sentiment signals improves the Sharpe ratio by $$\varvec{28.18 \%}$$ 28.18 % , and sensitivity tests guarantee robustness across regimes. By combining deep learning with interpretable LLM knowledge, our model bridges the gap between data-driven performance and theoretical interpretability, delivering actionable value to institutional and retail investors.

Keywords: Modern portfolio theory; Portfolio optimization; Large language models(LLMs); Sentiment analysis; Sharpe ratio (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s43069-025-00564-4

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