Paper: arXiv 2608.12283

Authors: Alireza Kargarzadeh, Nariman Khaledian, Navid Parvini, Arman Khaledian

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.

Complexity vs Empirical Score

  • Math Complexity: 6.0/10
  • Empirical Rigor: 8.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper presents a sophisticated approach to portfolio construction, integrating LLM sentiment with macroeconomic and technical signals, and rigorously evaluates it across various dimensions. The methodology for decomposing risk into aleatoric and epistemic components and feeding it into the covariance matrix is particularly noteworthy. The empirical evaluation is extensive, covering different stock-selection regimes, holding periods, and transaction costs.

Research Flowchart

  flowchart TD
    A[Research Goal: LLM-Driven Small-Cap Trading] --> B(Methodology: Uncertainty-Aware Portfolio Construction);
    B --> C{Inputs: Financial News, Macro Indicators, Technical Signals};
    C --> D[LLM Processing: Extract Ricker Signals, Decompose Risk (Aleatoric & Epistemic)];
    D --> E(Portfolio Allocation: Covariance Matrix Integration, Risk Parity);
    E --> F{Stock Selection Regimes: Pure-Alpha, Pure-Beta, Beta Intersection};
    F --> G[Outcomes: Sharpe Ratio, Return Across Holding Periods (1-day, 40-day)];
    G --> H{Key Findings: Pure-Alpha & Pure-Beta Dominate; Regime & Allocator Matter; Pure-Beta Strongest (Sharpe 2.33)};