Paper: arXiv 2504.14345

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.

Complexity vs Empirical Score

  • Math Complexity: 4.0/10
  • Empirical Rigor: 8.5/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: 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.

Research Flowchart

  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

Cited References (36)

  1. Portfolio Selection (1952) — link
  2. Global portfolio optimization (1992)
  3. A step-by-step guide to the Black-Litterman model: Incorporating user-specified confidence levels (2007)
  4. A hybrid approach for generating investor views in Black–Litterman model (2019)
  5. Revolutionizing finance with llms: An overview of applications and insights (2024) — link
  6. Mg-conv: A spatiotemporal multi-graph convolutional neural network for stock market index trend prediction (2022)
  7. Temporal relational ranking for stock prediction (2019)
  8. GPT-4o (2024)
and 28 more
  1. The llama 3 herd of models (2024) — link
  2. Gemma: Open models based on gemini research and technology (2024) — link
  3. Qwen2 Technical Report (2024) — link
  4. Efficient memory management for large language model serving with pagedattention (2023)
  5. The factor tau in the Black-Litterman model (2013)
  6. Capital asset prices: A theory of market equilibrium under conditions of risk (1964)
  7. An Overview of Optimization Models for Portfolio Management. (2024)
  8. Decision-informed Neural Networks with Large Language Model Integration for Portfolio Optimization — → 2502.00828
  9. Medec: A benchmark for medical error detection and correction in clinical notes (2024) — link
  10. Time-llm: Time series forecasting by reprogramming large language models (2023) — link
  11. Your AI, Not Your View: The Bias of LLMs in Investment Analysis — → 2507.20957
  12. THEME: Enhancing Thematic Investing with Semantic Stock Representations and Temporal Dynamics — → 2508.16936
  13. Enhancing portfolio optimization with transformer-GAN integration: A novel approach in the Black-Litterman framework (2024) — link
  14. Portfolio management transformed: An enhanced black–litterman approach integrating asset pricing theory and machine learning (2025)
  15. Empirical Asset Pricing with Large Language Model Agents (2024) — link
  16. Retrieval-augmented large language models for financial time series forecasting (2025) — link
  17. TradingAgents: Multi-agents LLM financial trading framework — → 2412.20138
  18. Finmem: A performance-enhanced llm trading agent with layered memory and character design (2025)
  19. Dynamic Black-Litterman — → 2404.18822
  20. The intuition behind Black-Litterman model portfolios (2002)
  21. The black-litterman approach: Original model and extensions (2010)
  22. A survey of behavioral finance (2003)
  23. Empirical asset pricing via machine learning (2020)
  24. A deep reinforcement learning framework for the financial portfolio management problem (2017) — link
  25. Bloomberggpt: A large language model for finance (2023) — link
  26. Fingpt: Democratizing internet-scale data for financial large language models (2023) — link
  27. Can ChatGPT forecast stock price movements? (2024)
  28. A survey of large language models in finance (finllms) (2024) — link

5 of 36 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.