As a guest user you are not logged in or recognized by your IP address. You have
access to the Front Matter, Abstracts, Author Index, Subject Index and the full
text of Open Access publications.
Adopting AI in financial advisory is a challenging task as there exists multiple sources of information to digest and interpret. Such information consumption process are very lengthy for financial advisors, reducing the efficiency and timeliness for their 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 report to assist in fund recommendation process using Large Language Models (LLMs). To quantitatively evaluate the efficacy of the approach, we track the weekly and monthly market performance of representative industry-focused fund after news and report released date, and compute a Normalized Discounted Cumulative Gain (NDCG) score between the rankings of the fund performance and recommendation rating scores. We find that utilizing analyst report and self consistency in the framework increase the NDCG score from 0.72 to 0.93 comparing to consuming news only without self consistency, based on the time frame of our experimental evaluation.
This website uses cookies
We use cookies to provide you with the best possible experience. They also allow us to analyze user behavior in order to constantly improve the website for you. Info about the privacy policy of IOS Press.
This website uses cookies
We use cookies to provide you with the best possible experience. They also allow us to analyze user behavior in order to constantly improve the website for you. Info about the privacy policy of IOS Press.