Large language models (LLMs) have demonstrated promising performance in various financial applications, though their potential in complex investment strategies remains underexplored. To address this gap, we investigate how LLMs can predict price movements in stock and bond portfolios using economic indicators, enabling portfolio adjustments akin to those employed by institutional investors. Additionally, we explore the impact of incorporating different personas within LLMs, using an ensemble approach to leverage their diverse predictions. Our findings show that LLM-based strategies, especially when combined with the mode ensemble, outperform the buy-and-hold strategy in terms of Sharpe ratio during periods of rising consumer price index (CPI). However, traditional strategies are more effective during declining CPI trends or sharp market downturns. These results suggest that while LLMs can enhance portfolio management, they may require complementary strategies to optimize performance across varying market conditions.

翻译：大语言模型（LLM）已在众多金融应用中展现出良好性能，但其在复杂投资策略中的潜力仍未得到充分探索。为填补这一空白，本研究探讨了如何利用经济指标，使LLM预测股票与债券投资组合的价格变动，从而实现类似机构投资者所采用的投资组合调整。此外，我们通过集成方法融合LLM中不同角色设定所产生的多样化预测，探究了角色化策略的影响。研究结果表明，在消费者价格指数（CPI）上升期间，基于LLM的策略（尤其是结合众数集成的方法）在夏普比率上优于买入持有策略。然而，在CPI下降趋势或市场急剧下跌期间，传统策略更为有效。这些发现表明，尽管LLM能够提升投资组合管理能力，但在不同市场环境下可能需要辅助策略以实现最优绩效。