Predictive modeling for equity trading using sentiment analysis

Abstract

Warren Buffett’s investment philosophy highlights the importance of generating wealth through available capital, but investors require more advanced tools for informed decision-making. Current research is focused on developing a modeling technique that leverages computer algorithms, including sentiment analysis. This method evaluates public sentiment about companies through social media, aiding investors in identifying promising stocks and safeguarding their wealth against unfavorable market conditions. In India, the banking, real estate, and pharmaceutical sectors are among the most robust and rapidly growing industries; however, deciding to invest in these sectors remains debatable. To address this, the proposed study aims to develop a hybrid prediction model that combines sentiment and technical analysis to uncover short-term trading opportunities. This model utilizes a two-layer ensemble stacking technique, training three distinct machine learning algorithms in the first layer and aggregating their outputs in the second layer. The proposed model significantly outperforms traditional methods in terms of accuracy, enabling investors to make more confident and profitable decisions in the Indian stock market.