Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/319230 
Year of Publication: 
2025
Series/Report no.: 
CESifo Working Paper No. 11862
Publisher: 
CESifo GmbH, Munich
Abstract: 
Retrieval-augmented generation (RAG) has emerged as a promising way to improve task-specific performance in generative artificial intelligence (GenAI) applications such as large language models (LLMs). In this study, we evaluate the performance implications of providing various types of domain-specific information to LLMs in a simple portfolio allocation task. We compare the recommendations of seven state-of-the-art LLMs in various experimental conditions against a benchmark of professional financial advisors. Our main result is that the provision of domain-specific information does not unambiguously improve the quality of recommendations. In particular, we find that LLM recommendations underperform recommendations by human financial advisors in the baseline condition. However, providing firm-specific information improves historical performance in LLM portfolios and closes the gap with human advisors. Performance improvements are achieved through higher exposure to market risk and not through an increase in mean-variance efficiency within the risky portfolio share. Notably, portfolio risk increases primarily for risk-averse investors. We also document that quantitative firm-specific information affects recommendations more than qualitative firm-specific information, and that equipping models with generic finance theory does not affect recommendations.
Subjects: 
generative artificial intelligence
large language models
domain-specific information
retrieval-augmented generation
portfolio management
portfolio allocation.
JEL: 
G00
G11
G40
Document Type: 
Working Paper
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