Multicollinearity Least Square Recursive Optimized Deep Belief Network based Financial News Sentiment Classification for Stock Price Prediction

An essential component of finance and investing is stock price prediction, which attempts to project a stock’s future price. The objective is to use a variety of techniques and data sources to predict the direction and size of price changes. Sentiment analysis of financial news data offers insightful information about the state of the market and possible changes in stock prices. Stock price projections become more accurate and dependable when sentiment research is combined with additional machine learning and deep learning models. This research develops a multicollinearity Least Square Recursive Optimised Deep Belief Network Classification (MLSRODBN) method for sentiment analysis-based stock price prediction that promises better accuracy and shorter processing times. The MLSRODBN Method comprises multiple layers for efficient stock price prediction, including preprocessing, feature selection, and classification processes. In hidden layer, Treebank Word Tokenization is performed to partition the sentences into tokens or words. Finally, Partial Least Square Regression Analysis is carried out to perform efficient sentiment classification (i.e., positive, negative, or neutral) based on the extracted keywords from the financial news. The analysis’s conclusions show that the MLSRODBN strategy fared better at predicting stock prices than other deep learning methods that were currently in use.