Stock Price Trend Prediction using Emotion Analysis of Financial Headlines with Distilled LLM Model

Capturing the volatility of stock prices helps individual traders, stock analysts, and institutions alike increase their returns in the stock market. Financial news headlines have been shown to have a significant effect on stock price mobility. Lately, many financial portals have restricted web scraping of stock prices and other related financial data of companies from their websites. In this study we demonstrate that emotion analysis of financial news headlines alone can be sufficient in predicting stock price movement, even in the absence of any financial data. We propose an approach that eliminates the need for web scraping of financial data. We use API based mechanism to retrieve financial news headlines. In this study we train and subsequently leverage light and computationally fast Distilled LLM Model to gather emotional tone and strength of financial news headlines for companies. We then use this information with several machine learning-based classification algorithms to predict the stock price direction based solely on the emotion analysis of news. We demonstrate that emotion analysis-based attributes of financial news headlines are as accurate in predicting the price direction as running the algorithms with the financial data alone.