LLM-Enhanced Black-Litterman Portfolio Optimization

ArXiv ID: 2504.14345 “View on arXiv”

Authors: Unknown

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.

Keywords: Large Language Models, Black-Litterman Model, Portfolio Construction, Return Forecasting, Investment Style, Equities (S&P 500)

Complexity vs Empirical Score

• Math Complexity: 4.0/10
• Empirical Rigor: 8.5/10
• Quadrant: Street Traders
• Why: The paper’s core innovation is a systematic framework for translating LLM outputs into Black-Litterman inputs, involving moderate mathematical concepts (Bayesian blending), but the primary focus is on empirical validation. The study demonstrates high empirical rigor through a backtest on S&P 500 constituents, hyperparameter tuning (τ), performance metrics (Sharpe ratio), and the public release of source code and data.

  flowchart TD
    subgraph S1 ["Research Goal"]
        A["Goal: Systematically generate investor views for Black-Litterman model using LLMs"]
    end

    subgraph S2 ["Methodology"]
        B[""1. Generate forecasts & uncertainty from multiple LLMs
           2. Convert into BL views & confidence levels
           3. Construct portfolios via Black-Litterman
           4. Backtest on S&P 500 constituents""]
    end

    subgraph S3 ["Data & Inputs"]
        C["S&P 500 constituents historical data + LLM predictions"]
    end

    subgraph S4 ["Key Findings"]
        D[""- LLM-driven portfolios outperform traditional baselines
           - Each LLM exhibits distinct, consistent investment style
           - Performance depends on regime-alignment of LLM style""]
    end

    A --> B
    C --> B
    B --> D