The Impact of Big Data Analytics on Stock Price Prediction in the Bangladesh Stock Market: A Machine Learning Approach

Abstract

The stock market is a volatile and complex environment impacted by various unpredictable factors, making accurate stock price prediction challenging. This research paper explored the potential and capability of big data analytics and machine learning techniques in terms of enhancing stock price prediction accuracy in the setting of the Bangladesh stock market. The methodology adopted in the study entailed a data gathering process, which comprised collecting financial data from the Bangladesh stock market, such as news articles, financial statements, macroeconomic indicators, and historical stock prices. Based on a literature review, various fundamental and technical indicators are chosen as predictive features. The research paper employed a combined methodology that consolidates technical calculations and sentimental analysis to predict and forecast stock market patterns. By adopting machine learning and sentiment analysis techniques, this technique provides future predictions for the stock market while considering the impact of political events, economic factors, and dynamics in social media. The consolidation of big data analytics enables real-time predictions of stock market movements. The sentiment analysis algorithm facilitates prompt and extensive evaluations of tweets and news articles. As a result, the integration of technical and sentiment analyses greatly enhances the accuracy of stock market predictions.