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

2025-12-05

AI Summary

This study investigates the application of large language models (LLMs) to mutual fund portfolio optimization and risk-adjusted asset allocation. Addressing the limitation of conventional methods in integrating unstructured economic signals with real-time market data, we propose a retrieval-augmented generation (RAG)-driven, risk-aware asset allocation framework. The framework integrates Phi-2, Mistral-7B, and our proprietary Zypher-7B model, jointly leveraging macroeconomic indicators and classical financial optimization techniques. Our key contribution is the first native LLM-based implementation of risk-adjusted decision-making: Zypher-7B—enhanced for contextual modeling—significantly outperforms baseline models on critical metrics including Sharpe ratio and maximum drawdown. Empirical evaluation demonstrates improved situational adaptability and robustness of investment strategies without compromising computational efficiency, thereby substantiating the tangible value of generative AI in active asset management.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

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.

Problem

• Using LLMs to optimize mutual fund portfolios for better returns.
• Applying AI to enhance risk management in investment strategies.
• Integrating real-time data with financial models for smarter asset allocation.

Innovation

• Using LLMs for portfolio optimization and risk management
• Combining RAG pipeline with financial optimization methods
• Zypher 7B model maximizes returns with risk-adjusted results