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arXiv · 2504.14345

LLM-Enhanced Black-Litterman Portfolio Optimization

Youngbin Lee·Yejin Kim·Juhyeong Kim·Suin Kim·Yongjae Lee
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Abstract

The Black-Litterman model addresses the sensitivity issues of tra- ditional mean-variance optimization by incorporating investor views, but systematically generating these views remains a key challenge. This study proposes and validates a systematic frame- work that translates return forecasts and predictive uncertainty from Large Language Models (LLMs) into the core inputs for the Black-Litterman model: investor views and their confidence lev- els. Through a backtest on S&P 500 constituents, we demonstrate that portfolios driven by top-performing LLMs significantly out- perform traditional baselines in both absolute and risk-adjusted terms. Crucially, our analysis reveals that each LLM exhibits a dis- tinct and consistent investment style which is the primary driver of performance. We found that the selection of an LLM is therefore not a search for a single best forecaster, but a strategic choice of an investment style whose success is contingent on its alignment with the prevailing market regime. The source code and data are available at https://github.com/youngandbin/LLM-BLM.

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Youngbin Lee, Yejin Kim, Juhyeong Kim, Suin Kim, Yongjae Lee. 2025-04-19. LLM-Enhanced Black-Litterman Portfolio Optimization. https://arxiv.org/abs/2504.14345

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