A Multifactor Analysis Model for Stock Market Prediction

Stock Market predictions have historically been a problem tackled by different singular approaches even though markets are influenced by many different factors. This paper presents a novel multi-factor analysis model for stock price prediction that combines Technical analysis, Fundamental analysis, Machine learning, and, Sentiment Analysis (TFMS Analysis). The proposed model leverages Random Forest Regressor (RFR) to predict a stock price and long short-term memory(LSTM) approach to predict a multiplier. Sentiment analysis is then used to capture the impact of various factors on stock prices, including market trends, economic indicators, and public opinion. The results of the model are compared to traditional prediction models using historical stock data, and it is shown that the proposed model provides improved accuracy in predicting future stock prices. The proposed model represents a significant step forward in stock price prediction, providing a more comprehensive and effective approach to predicting stock prices based on multiple factors.