Stock Market Prediction through Technical and Public Sentiment Analysis

STOCK market price behavior has been studied extensively. It is influenced by a myriad of factors, including political and economic events, among others, and is a complex nonlinear time-series problem. Traditionally, stock price forecasting is performed based on technical analysis, which focuses on price action, which is the process of finding patterns in price history. More recently, research has shown that public sentiment is correlated with stock market events [1], [2], [3]. This project proposes to study the potential of using both behavioral and technical features in stock price prediction models based on traditional classifiers and popular neural networks. We believe that behavioral data may offer insights into financial market dynamics in addition to that captured by technical analysis. An improved price forecasting model can yield enormous rewards in stock market trading.