Can LLMs improve portfolio management?
Leveraging Large Language Models for Institutional Portfolio Management: Persona-Based Ensembles
December 2, 2024
https://arxiv.org/pdf/2411.19515This paper explores using Large Language Models (LLMs) with assigned personas (like "short-term investor" or "long-term investor") as an ensemble to manage a stock and bond portfolio. It investigates whether LLMs can predict price movements based on economic indicators and adjust portfolio positions accordingly.
While not explicitly a multi-agent system, the ensemble of LLM personas acts like a multi-agent system where each agent (persona) has a distinct investment strategy. Key findings relevant to LLM-based multi-agent development include:
- Persona-based differentiation: Different LLM personas exhibit varied prediction accuracies and investment behaviors, mirroring real-world investor diversity.
- Ensemble effectiveness: Combining predictions from multiple personas via a "mode" ensemble (majority vote) improves overall prediction accuracy and is particularly effective for predicting market declines.
- Conditional outperformance: LLM-driven strategies outperform traditional methods in terms of Sharpe ratio during periods of rising CPI, especially during overall downward market trends. This suggests that LLMs with broader knowledge can be more resilient than traditional algorithms focused solely on recent price movements.
- Rationale analysis: Examining the reasoning behind each persona's predictions reveals distinct investment philosophies based on time horizons and risk tolerance. This opens the door for more sophisticated prompt engineering to improve individual agent (persona) performance and ensemble composition.