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FundRecLLM: Fund recommendation based on financial news and research analyst report

By Guang Yang, Peiyang He, Xuefeng Liu
2023
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Adopting AI in financial advisory is a challenging task as there exist multiple sources of information to digest and interpret. Such information consumption processes are very lengthy for financial advisors, reducing the efficiency and timeliness for the advice and recommendation given to their clients. In this work, we introduce a multi-step framework that consumes and combines news and industry-focused fund research analyst reports to assist in fund recommendation using large language models (LLMs). To quantitatively evaluate the efficacy of the approach, we track the weekly and monthly market performance of a representative industry-focused fund after news and report release dates and compute a Normalized Discounted Cumulative Gain (NDCG) score between the rankings of the fund performance and recommendation rating scores. We find that utilizing an analyst report and self-consistency in the framework increases the NDCG score from 0.72 to 0.93 compared to consuming news only without self-consistency, based on the time frame of our experimental evaluation.
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