A Multi-modal Deep Learning Approach for Predicting Dhaka Stock Exchange

This study proposes a reliable and accurate approach for forecasting future stock price movements on the Dhaka Stock Exchange (DSE). Despite some people's beliefs that it is difficult to create a predictive framework that can properly anticipate stock prices, there is a substantial body of literature that shows that seemingly random movement patterns in stock prices can be forcasted with a highly accurate result. The framework described in this study combines LSTM, Transformer, and the GRU model. Performance metrics including mean squared error (MSE) and R-squared (R2) are used to gauge the suggested DeepDse model's accuracy. The results of the evaluation indicate that the model is highly accurate and can be used to provide reliable predictions of stock prices. This is of great importance, as accurate predictions of stock prices can assist investors in determining the best timing to buy and sell their investments. This can help investors minimize the risk of losing money and maximize their returns. The study suggests that the proposed model could be particularly valuable for investors in the Dhaka Stock Exchange, as it can provide them with valuable information to make informed investment decisions.