用大模型自动优化投资回报风险指标，提升预测力与稳定性。
AlphaSharpe: LLM-Driven Discovery of Robust Risk-Adjusted Metrics
- 用大模型迭代生成和优化金融指标，融合领域知识
- 新指标预测未来收益能力提升3倍，组合表现提升2倍
- 适合量化投资、基金经理等关注稳健策略的决策者
金融指标如夏普比率在评估投资绩效中至关重要，但传统指标在动态波动市场中常缺乏鲁棒性和泛化能力。本文提出AlphaSharpe框架，利用大语言模型（LLMs）通过迭代交叉、变异与评估，演化并优化风险-回报指标，以发现更优的度量方法。关键贡献包括：(1) 首次将LLM用于生成与精炼具有隐含领域知识的金融指标；(2) 设计评分机制确保演化指标在未见数据上有效泛化；(3) 实验表明新指标对未来收益的预测能力提升3倍，投资组合绩效提升2倍。基于真实世界数据集的实验验证了所发现指标的优越性，对投资组合管理及金融决策极具价值。该框架不仅弥补现有指标局限，也展示了LLM在金融分析中的潜力，为制定更稳健的投资策略铺平道路。
原文摘要 · Abstract (English)
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.
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