  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.
