Stock Market Trend Prediction Using Deep Learning Approach

Since the dawn of financial market trading, traders have continually sought methods to enhance their predictive capabilities for future price movements. This pursuit is driven by the significant daily trading volumes observed in financial markets worldwide. While traditional econometric and statistical methods have historically dominated in forecasting the behaviors of stock exchanges such as the Pakistan Stock Exchange, there remains a relatively limited exploration into the realm of artificial intelligence (AI) and machine learning (ML) techniques for addressing the inherent unpredictability of these markets. This study aims to improve the accuracy of forecasting the closing index of the Pakistan Stock Exchange by leveraging AI-based models, particularly employing the Deep Learning (DL) Long Short-Term Memory (LSTM) recurrent neural network. These DL models are anticipated to outperform traditional time series methods in predicting market indices. The primary objective of this work is to empower short-term investors with more precise index forecasts, enabling them to make informed and strategic trading decisions through the application of AI-based models.