Leveraging Large Language Models and Retrieval-Augmented Generation for Enhanced Multi-Asset Portfolio Construction

Abstract

This study assesses the Large Language Models (LLMs) in creating investment portfolios. We implement a few-shot learning technique, followed by Retrieval Augmented Generation (RAG) enhanced with comprehensive up-to-date financial data, using Meta's latest LLM, Llama 3.1-8b. In the first phase, We assess the models' efficacy using key financial indicators, including total returns, annualized volatility, riskadjusted performance (Sharpe ratio), potential loss estimates (value-at-risk), and their pre-training knowledge with the S&P 500 Index performance baseline. In the second phase, we enhance the LLM's knowledge base by RAG and the latest historical and statistical metrics (such as earnings per share (EPS), dividends per share (DPS), profit margin, and many more) for each asset from different classes. The study constrains model inputs to specific sets of financial assets, such as equities, exchangetraded funds (ETFs), commodities, cryptocurrencies, and bonds. To evaluate model performance and adaptability, we analyzed across two distinct time frames: (1) within the models' training data cutoff, and (2) from the cutoff date to the present. This approach enables the assessment of model generalization to past and present market conditions. The research quantifies LLMs’ capabilities in financial asset allocation, comparing baseline performance against RAG-augmented strategies. Our results demonstrate that RAG-enhanced LLM significantly outperforms vanilla LLM in portfolio construction across various asset classes. We contemplate that these results could influence AI-driven financial decision-making processes such as automated trading, real-time sentiment analysis, and investment management.