Optimizing Stock Price Prediction for South Asian Markets Using LSTM, GRU, CNN with Greedy Algorithm

Accurately predicting socio-economic trends, including stock market behavior, has become increasingly vital for investors, policymakers, and researchers in today's economic growth. This task is particularly challenging in South Asian nations due to the region's economic instability and the unpredictable nature of financial information. This paper aims to predict stock values in five prominent South Asian stock exchanges, namely Karachi (KSE), Nifty50 (NSE), Colombo (CSE), Dhaka (DSE), and Afghanistan, using machine learning methods and daily data from 2018 to 2023. To improve forecasting accuracy, this research used a greedy approach to optimize the window size of a Simple Moving Average (SMA) and normalized the data to train three deep learning models: Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), and Long Short-Term Memory (LSTM). The models were evaluated using performance metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), and R2 score. Our results demonstrate that GRU outperforms LSTM and CNN in all markets, with reduced MSE and elevated R² values. However, CNN exhibits the most volatility in unstable markets, such as Afghanistan and Sri Lanka. LSTM provides more dynamic forecasting patterns but is prone to overestimating abrupt fluctuations in stock values. In summary, our research provides a comprehensive evaluation of machine learning models for stock price prediction and identifies GRU as the most reliable model.