Multimodal Gen-AI for Fundamental Investment Research

TL;DR

This paper explores fine-tuning multimodal generative AI models, based on Llama2 and GPT-3.5, to automate financial analysis tasks like summarization, idea generation, and market insight, aiming to improve investment decision-making.

Contribution

It demonstrates the effectiveness of fine-tuning large language models for specific financial tasks, advancing AI applications in investment research.

Findings

Fine-tuned models outperform base models in finance-specific tasks.

State-of-the-art generative techniques improve summarization and reasoning.

Human and statistical evaluations confirm enhanced performance.

Abstract

This report outlines a transformative initiative in the financial investment industry, where the conventional decision-making process, laden with labor-intensive tasks such as sifting through voluminous documents, is being reimagined. Leveraging language models, our experiments aim to automate information summarization and investment idea generation. We seek to evaluate the effectiveness of fine-tuning methods on a base model (Llama2) to achieve specific application-level goals, including providing insights into the impact of events on companies and sectors, understanding market condition relationships, generating investor-aligned investment ideas, and formatting results with stock recommendations and detailed explanations. Through state-of-the-art generative modeling techniques, the ultimate objective is to develop an AI agent prototype, liberating human investors from repetitive tasks and allowing a focus on high-level strategic thinking. The project encompasses a diverse corpus dataset, including research reports, investment memos, market news, and extensive time-series market data. We conducted three experiments applying unsupervised and supervised LoRA fine-tuning on the llama2_7b_hf_chat as the base model, as well as instruction fine-tuning on the GPT3.5 model. Statistical and human evaluations both show that the fine-tuned versions perform better in solving text modeling, summarization, reasoning, and finance domain questions, demonstrating a pivotal step towards enhancing decision-making processes in the financial domain. Code implementation for the project can be found on GitHub: https://github.com/Firenze11/finance_lm.