Stock Price Prediction Using Ensemble Model and Sentiment Analysis
Keywords:
LSTM,RNN,GRU,SVR,TextBlob,Sentiment Analysis,yfinance,Abstract
The continued challenge in the world of finance is an accurate prediction of stock prices. This article investigates a trading system that uses machine learning algorithms to make recommendations on stocks to trade. This method integrates historical price analysis with sentiment examination in recent news articles. In this manner, the user is thus able to recognize patterns and predict future price movements using training models based on historical stock prices, turnover rates and other related indicators. Further, this study includes news articles’ sentiments which tell how much news-driven sentiment affects the values of shares, and recommend the user whether to buy or sell or hold onto a particular stock. Performing standard metric tests on our approach and comparing its outcomes with traditional trading strategies, helps refine this study. By combining ratios like Simple Moving Average (SMA), Relative Strength Index (RSI) and fundamental analysis, gave ability to gauge the bullish or bearish behavior of a stock, aiding in providing a recommendation to the user based on previous data and further predictions. Through this research, evidence is provided within the algorithmic trading literature for machine learning and sentiment analysis that support datadriven stock recommendations. Implementing various machine learning models, this study concluded that an Ensemble technique using LSTM,RNN and GRU gave the best accuracy for the user with R² having value 0.9976.
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