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.
