Using Deep Learning Models and FinBERT to Predict the Stock Price of Top Banks in the Dhaka Stock Exchange

Bangladesh's economy, like many other contemporary nations, is fundamental to its overall structure. The stock market is one crucial and well-known component of this. Therefore, it is essential to anticipate it earlier to better prepare for market swings. The price of stocks is influenced by a wide range of factors, including geopolitical and economic ones. However, stock data and news sentiment are two that we can leverage ourselves in stock forecasting. Since news strongly influences the stock market, regular and institutional investors often base their decisions on news analysis. Integrating news sentiment analysis into a predictive model holds immense potential when building stock price forecasters. In our research, we have built a web application that reads data from the Dhaka Stock Exchange (DSE) and then relays the output prediction to the user. We use LSTM and GRU for the stock forecasting models on seven banks-four private and three public. For the sentiment analysis task for each of these seven datasets, we employ FinBERT. React, Flask, and MongoDB power our web application, which serves as the medium for all communication. Furthermore, we can confidently state that our research outcomes were precise and valuable regarding the future prices of Bangladeshi stocks. This paper will demonstrate and explain this endeavor's process and outcome trajectory.