Paper: arXiv 2601.17021

Authors: Muhammad Abro, Hassan Jaleel

Abstract

We attempt to mitigate the persistent tradeoff between risk and return in medium- to long-term portfolio management. This paper proposes a novel LLM-guided no-regret portfolio allocation framework that integrates online learning dynamics, market sentiment indicators, and large language model (LLM)-based hedging to construct high-Sharpe ratio portfolios tailored for risk-averse investors and institutional fund managers. Our approach builds on a follow-the-leader approach, enriched with sentiment-based trade filtering and LLM-driven downside protection. Empirical results demonstrate that our method outperforms a SPY buy-and-hold baseline by 69% in annualized returns and 119% in Sharpe ratio.

Complexity vs Empirical Score

  • Math Complexity: 4.5/10
  • Empirical Rigor: 6.0/10 (capped from 7.0: transaction costs not modelled; see penalty flags)
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper presents an innovative integration of LLM sentiment signals with online no-regret learning for asset allocation, demonstrating strong empirical performance over long backtesting horizons. The methodology is clearly explained and highly reproducible with a provided code link, though the mathematical formulation relies primarily on established online learning concepts rather than deep theoretical derivations.

Research Flowchart

  flowchart TD
    A[Goal: Mitigate Risk-Return Tradeoff for High Sharpe Ratio] --> B[Data Inputs: Historical Market Data & Sentiment Indicators]
    B --> C[Online Learning: Follow-the-Leader Allocation]
    C --> D[Sentiment-Based Trade Filtering]
    D --> E[LLM-Driven Hedging & Downside Protection]
    E --> F[Outcomes: +69% Annualized Returns & +119% Sharpe Ratio vs SPY Baseline]

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0 of 121 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.