Authors: Research Scholar Mr. Surya C L, Assistant Professor Dr. S. Krishnakumar, Assistant Professor Dr. S. Parthiban
Abstract: The emergence of behavioral analysis based on big data has greatly impacted the field of finance by providing a deeper understanding of the role that psychology plays in shaping investors' behavior. The purpose of this paper is to explore the application of various big data techniques such as sentiment analysis, deep learning, and natural language processing as the tools to uncover the psychology behind investors' behavior. Using recently collected empirical evidence, this paper proposes a new model for predicting market movements based on the use of sentiment analysis, employee sentiment, and forecasting using machine learning models. The proposed methodology uses three sources of data: social media content, employees' online reviews, and conventional financial metrics. As shown in quantitative analysis, models based on sentiment provide better predictions than traditional forecasting, as latent profile analysis explains 47% of stock price variability compared to only 10% achieved by the latter.
