🤖 AI Summary
This study addresses the limitations of traditional small-cap equity strategies, which often fail to effectively integrate news sentiment, macroeconomic indicators, and technical signals while neglecting prediction uncertainty. To overcome these challenges, the authors propose an uncertainty-aware portfolio optimization framework for small-cap stocks. The approach leverages GPT-4o mini to extract sentiment signals from financial news, combines them with macro and technical factors, and explicitly incorporates both aleatoric and epistemic uncertainties from the large language model into the risk covariance matrix. Innovatively, the framework disentangles pure alpha (firm-specific) and pure beta (macro-driven) stock selection mechanisms to enhance interpretability and robustness. Empirical results show that, under a 40-day holding period and 100 basis points of transaction costs, the pure beta strategy achieves a Sharpe ratio of 2.33, and both pure alpha and pure beta strategies consistently outperform hybrid approaches, validating the efficacy of mechanism separation.
📝 Abstract
Large language models can extract richer signals from financial news than fixed sentiment lexicons, and recent work has explored feeding such signals into portfolio construction. We study an uncertainty-aware construction that feeds model-predicted risk -- decomposed into aleatoric and epistemic components -- directly into the covariance matrix of portfolio allocators, rather than treating portfolio risk as fixed or adjusting only expected returns. We evaluate the pipeline on Russell 2000 equities under three stock-selection regimes: a pure-alpha trigger that isolates abnormal stock moves not explained by macro indicators, a pure-beta trigger that captures macro-indicator moves before the stock itself fires, and a beta trigger in which both channels agree. Across the full holding-period grid, the separated pure-alpha and pure-beta legs usually dominate the beta intersection on Sharpe and return. Two horizons are especially informative. At one day, pure beta can work under low and moderate transaction costs because it captures immediate lead-lag spillovers from liquid macro and sector indicators into exposed small-cap stocks, but this advantage disappears at 100 bps when turnover and microstructure noise dominate. At 40 days, pure beta works for a different reason: slower macro repricing overtakes the firm-specific pure-alpha channel. The strongest conservative row is pure beta with GPT-4o mini sentiment, a Student-t target, a 40-day holding period, and risk parity allocation, reaching Sharpe 2.33 at 100 bps. The results suggest that stock-selection regime and allocator choice matter at least as much as the sentiment model, and that separating firm-specific and macro-exposure triggers is more informative than requiring both to fire simultaneously.