AlphaSharpe: LLM-Driven Discovery of Robust Risk-Adjusted Metrics

ArXiv ID: 2502.00029 “View on arXiv”

Authors: Unknown

Abstract

Financial metrics like the Sharpe ratio are pivotal in evaluating investment performance by balancing risk and return. However, traditional metrics often struggle with robustness and generalization, particularly in dynamic and volatile market conditions. This paper introduces AlphaSharpe, a novel framework leveraging large language models (LLMs) to iteratively evolve and optimize financial metrics to discover enhanced risk-return metrics that outperform traditional approaches in robustness and correlation with future performance metrics by employing iterative crossover, mutation, and evaluation. Key contributions of this work include: (1) a novel use of LLMs to generate and refine financial metrics with implicit domain-specific knowledge, (2) a scoring mechanism to ensure that evolved metrics generalize effectively to unseen data, and (3) an empirical demonstration of 3x predictive power for future risk-returns, and 2x portfolio performance. Experimental results in a real-world dataset highlight the superiority of discovered metrics, making them highly relevant to portfolio managers and financial decision-makers. This framework not only addresses the limitations of existing metrics but also showcases the potential of LLMs in advancing financial analytics, paving the way for informed and robust investment strategies.

Keywords: Large Language Models (LLMs), AlphaSharpe, Metric evolution, Portfolio optimization, Risk-adjusted returns

Complexity vs Empirical Score

• Math Complexity: 4.0/10
• Empirical Rigor: 7.0/10
• Quadrant: Street Traders
• Why: The paper introduces a novel LLM-driven framework for evolving financial metrics, which involves a structured iterative process (crossover, mutation, scoring) requiring some computational and algorithmic understanding, but the excerpt lacks dense mathematical derivations or proofs. The work is highly empirical, citing experiments on real-world datasets, demonstrating specific performance improvements (3x predictive power, 2x portfolio performance), and outlining a practical scoring mechanism for robustness and generalization, making it data and implementation-heavy.

  flowchart TD
    Goal["Research Goal: Discover robust, risk-adjusted metrics outperforming traditional Sharpe Ratio for dynamic markets?"] --> Inputs
    Inputs["Input Data: Historical Asset Returns & Market Conditions"] --> Process["LLM-Driven Evolution: Iterative Crossover, Mutation & Evaluation"]
    Process --> Compute["Computational Process: Scoring for Generalization & Robustness"]
    Compute --> Outcomes["Key Outcomes: 3x Predictive Power & 2x Portfolio Performance"] --> Impact["Impact: Advanced Financial Analytics for Portfolio Managers"]