[2H1-OS-8d] OS-8

[2H1-OS-8d-03] Plug-FIN : Pseudo-Labeling Using Generative Approaches to Find Investment Strategies

〇Katsuya Ito¹, Kei Nakagawa^2,3 (1. INDX & Co., Ltd., 2. Nomura Asset Management Co.,Ltd., 3. Osaka Metropolitan University)

Keywords:Finance, Data Scinence, LLM

We introduce Plug-FIN (Pseudo-Labeling Using Generative Modeling Approaches to Find Investment Strategies), a novel framework that leverages large language models to enhance quantitative investment strategies in three key ways. First, the framework assigns pseudo-labels to substantial volumes of text data. Second, it transforms these labels into interpretable long/short trading strategies. Finally, it incorporates a feedback system that systematically identifies top-performing strategies and integrates newly developed trading strategies as well as custom labeling functions. This iterative process ensures the models remain current as market conditions evolve. We conduct experiments with real stock market data and demonstrate that Plug-FIN not only improves predictive accuracy but also produces interpretable and profitable strategies.