Enhanced Stock Price Prediction Across Global Markets Through Data-Driven Modeling

Abstract

The stock market is highly volatile and influenced by numerous factors operating simultaneously, making accurate prediction a challenging task. Over the years, several studies have attempted to forecast stock prices using statistical, machine learning, and deep learning approaches. However, these models often fail to capture the combined effect of external factors such as public sentiment, which has a significant impact on market movement. In this work, we propose a hybrid algorithm that integrates Twitter sentiment analysis with a Long Short-Term Memory (LSTM) network to predict the next day’s closing stock price. Sentiment analysis is performed using part-of-speech (POS) tagging to evaluate public opinion extracted from Twitter data, while LSTM is employed to model the temporal dependencies in historical stock prices. By combining sentiment-driven signals with sequential deep learning, our model provides a more reliable prediction of stock price movements compared to conventional approaches. Experimental results demonstrate that incorporating sentiment into LSTM improves accuracy and offers a clearer insight into the future direction of stock prices.