Financial and Investment Management System using Transformers

Abstract

In the contemporary landscape of stock market analysis, the utilization of advanced nat- ural language processing techniques has become increasingly prevalent. This work presents the application of the Phi 2 Transformer Model, a compact yet potent language model, for the classification of stock news articles as either indicative of a buy or sell recommendation. Leveraging the Phi 2 model’s demonstrated accuracy of 89% in stock news classification, this study contributes to the development of sophisticated decision support systems for investors.The methodology involves preprocessing a comprehensive dataset of stock news ar- ticles, incorporating relevant contextual features such as industry-specific terminology, and market trends. Notably, the closing price of the respective company serves as a crucial deter- minant in analyzing the news’s trend and discerning the overall market trend. Through the integration of this contextual information with the Phi 2 Transformer Model, the system can effectively classify incoming news articles into actionable buy or sell recommendations.The proposed model offers valuable insights for investors by automating the time-consuming pro- cess of manually analyzing news articles and market trends. By harnessing the predictive capabilities of the Phi 2 model, coupled with real-time market data, investors can make more informed decisions, potentially enhancing their investment strategies and overall port- folio performance.