LLM-Enhanced Black-Litterman Portfolio Optimization
“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.”
AssetEquitiesindices
StrategyPortfolio optimizationmachine learning
TechniqueLarge language model
GeographyUS
Also stated: Text and news, benchmark
Missing from abstract: out-of-sample test, costs, multiple testing, universe
Institutional-grade audit of this strategy.
£5 — Request the audit- Audit of the paper claims accounting for costs, luck, crises, originality, geography, etc.
- Reproduction code integratable into Bloomberg, IBKR, etc.
- Bias-free datasets for backtesting included
- Delivered in 2 working days
The paper's neighbourhoodPortfolio optimization by year
The family is counted across the whole universe. Asset, method and authors are counted inside the family.
| Similar papers | citation | same method | same asset | same authors | |
|---|---|---|---|---|---|
| Return Prediction for Mean-Variance Portfolio Selection: How Decision-Focused Learning Shapes Forecasting Models by Lee et al. | 2025 | ||||
| Sentiment-Aware Mean-Variance Portfolio Optimization for Cryptocurrencies by Chen | 2026 | ||||
| An Automated Black-Litterman Framework for Global Fixed Income by Carver et al. | 2026 | ||||
| On the Limits of Low-Frequency OHLCV Signals in Machine Learning-Driven Portfolio Optimization A Cost-Aware, Leak-Free Benchmark on Large-Cap US Equities by Pardo | 2026 | ||||
| Can Advanced Portfolio Optimization Beat Equal Weighting? Evidence from Indian Large-Cap Walk-Forward Backtests by Dabi and Dabi | 2026 |