Predicting the Karachi Stock Price index with an Enhanced multi-layered Sequential Stacked Long-Short-Term Memory Model

Abstract

The study proposes the use of a stacked Long-Short-Term Memory (LSTM) model to predict the KSE-100 stock exchange trend and provides a comprehensive review of the literature on deep learning models and time series forecasting in the stock market. The study's findings suggest that the stacked LSTM model outperforms other models in terms of prediction accuracy. The study's contribution lies in its approach to improving the accuracy of stock price prediction using deep learning models. The stacked LSTM model architecture is a novel approach that provides better results than other traditional time series forecasting models. Furthermore, the study's use of hyper-parameter optimization techniques demonstrates the importance of model tuning for improving performance intended for accurate time series forecasting in the financial market. The study's results have practical implications for investors, who can use the stacked LSTM model to make informed decisions about buying or selling stocks in the KSE-100. The model's ability to predict stock prices accurately can help investors maximize their profits and minimize their losses. Hence, the proposed stacked LSTM model can effectively predict stock prices in the KSE-100 and can assist investors in making informed decisions in the stock market.