Predicting Stock Prices in the Banking Sector of Bangladesh: A Machine Learning Approach

Abstract

Predicting stock prices is a complex challenge, par-ticularly in volatile and low-liquidity markets like the Bangladesh Stock Market, where economic, political, and market-specific factors significantly influence outcomes. This study compares state-of-the-art machine learning models for stock price predic-tion, utilizing historical market data to forecast short-term and long-term price movements. Algorithms including Long Short-Term Memory (LSTM) networks, Support Vector Regression (SVR), Prophet, and Autoregressive Integrated Moving Average (ARIMA) were employed. The dataset comprised historical price and trading volume data to enhance prediction accuracy. Model performance was evaluated using prediction accuracy, Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Error (MAE). Results indicate that the ARIMA model outperformed the other models considered. This research contributes to the field of stock market forecasting and provides a framework for improving investment strategies through advanced AI techniques