发表机构
Amirkabur University of Technology（阿米尔卡比尔理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对非平稳金融市场，提出检索增强LLM引导的专家切换框架，经多市场实验验证其在累计收益、夏普比率上优于对比策略，为自适应投资组合管理提供有效方案。
AI 中文摘要
金融市场本质上是非平稳的，单个投资组合管理策略的有效性高度依赖于不断变化的市场条件。本研究提出一种检索增强的专家切换框架，该框架基于投资组合管理专家在相似市场情境下的历史表现，动态选择合适的专家。双路变分自编码器用于表征资产层面与全市场层面的信息，而基于检索的知识库则存储历史情境与专家表现。推理阶段，经过指令微调的大语言模型（LLM）会基于检索到的证据进行推理，以确定最合适的专家，而非直接生成投资组合操作。本研究进一步证明了单调性属性：添加局部更优的专家不会降低切换机制的性能。在加密货币、股票和外汇市场上开展的实验表明，所提出的选择器在这三类市场中，在所有评估的选择策略中实现了最高的累计收益与夏普比率。例如，在股票市场中，累计收益从最优固定专家的26%提升至34%，夏普比率从0.74提升至0.96。 ablation 结果证实了检索与 LLM 推理的重要性，而不同专家池规模的实验则证明了互补专业知识的价值。总体而言，研究结果支持基于检索的专家切换作为非平稳金融环境中自适应投资组合管理的有效方法。
英文摘要
Financial markets are inherently non-stationary, making the effectiveness of individual portfolio-management strategies highly dependent on changing market conditions. This work proposes a retrieval-augmented expert-switching framework that dynamically selects portfolio management experts based on their historical performance under similar market situations. A dual-stream variational autoencoder represents asset-level and market-wide information, while a retrieval-based knowledge base stores historical situations and expert performance. During inference, an instruction-tuned LLM reasons over the retrieved evidence to identify the most appropriate expert rather than directly generating portfolio actions. We further establish a monotonicity property showing that adding a locally superior expert cannot degrade the switching mechanism's performance. Experiments across cryptocurrency, stock, and foreign-exchange markets show that the proposed selector achieves the highest cumulative return and Sharpe ratio among the evaluated selection strategies in all three markets. In the stock market, for example, cumulative return increases from 26% for the best fixed expert to 34%, while the Sharpe ratio improves from 0.74 to 0.96. Ablation results confirm the importance of both retrieval and LLM reasoning, while experiments with different expert-pool sizes demonstrate the value of complementary expertise. Overall, the findings support retrieval-grounded expert switching as an effective approach to adaptive portfolio management in non-stationary financial environments.
