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This paper investigates the application of Large Language Models (LLMs) to mutual fund portfolio optimization and risk-adjusted asset allocation, aiming to enhance traditional financial decision-making. The authors employed a Retrieval-Augmented Generation (RAG) pipeline, integrating real-time economic data with standard financial optimization techniques, to guide LLMs in generating investment strategies. The study found that the Zypher 7B model outperformed Microsoft Phi 2 and Mistral 7B, consistently producing strategies that maximized investment returns while delivering superior risk-adjusted results.
Zypher 7B crushes other LLMs at mutual fund portfolio optimization, delivering superior risk-adjusted returns thanks to its ability to process complex economic signals.
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.