From Text to Returns: Using Large Language Models for Mutual Fund Portfolio Optimization and Risk-Adjusted Allocation

TL;DR

This paper explores how advanced Large Language Models, combined with real-time data retrieval, can generate effective, risk-aware mutual fund investment strategies, outperforming traditional methods in maximizing returns and risk management.

Contribution

It introduces a novel approach integrating LLMs with real-time data and financial optimization, demonstrating superior performance in portfolio allocation.

Findings

Zypher 7B outperformed other models in return and risk-adjusted metrics.

The combined RAG and optimization pipeline enhances decision quality.

GenAI methods significantly outperform basic allocation strategies.

Abstract

Generative AI (GenAI) has enormous potential for improving two critical areas in investing, namely portfolio optimization (choosing the best combination of assets) and risk management (protecting those investments). Our study works at this intersection, using Large Language Models (LLMs) to upgrade how financial decisions are traditionally made. This research specifically tested how well advanced LLMs like Microsoft Phi 2, Mistral 7B, and Zypher 7B can create practical, risk-aware strategies for investing mutual funds in different sectors of the economy. Our method is sophisticated: it combines a Retrieval-Augmented Generation (RAG) pipeline, which enables the LLM to check external, real-time data with standard financial optimization methods. The model's advice is context-aware because we feed it large economic signals, like changes in the global economy. The Zypher 7B model was the clear winner. It consistently produced strategies that maximized investment returns while delivering better risk-adjusted results than the other models. Its ability to process complex relationships and contextual information makes it a highly powerful tool for financial allocation. In conclusion, our findings show that GenAI substantially improves performance over basic allocation methods. By connecting GenAI to real-world financial applications, this work lays the groundwork for creating smarter, more efficient, and more adaptable solutions for asset management professionals.