Applying a Hybrid Deep Learning Framework to Efficient Stock Price Prediction

It is very difficult to make reliable financial predictions using stock market data due to its inherent volatility and non-linearity, especially in the period following a pandemic. In response to these difficulties, this study builds a hybrid framework using deep learning to forecast technology stock prices, financial trends, and risk quantification. An increasing number of individuals have started investing in the stock market due to the fast growth of the economy in the past few years. A novel stock market prediction based on deep learning (SMP-DL) is presented in this research as a method for predicting stock market values. This study forecasted the stock market closing price using a bidirectional gated recurrent unit (BiGRU) in conjunction with long short-term memory (LSTM). When tested against other systems currently in use, the results demonstrated that the suggested system performed admirably. Since the values of R2, RMSE, MDA, MAE, and 0.8, respectively, are 3.4, 6.7, and 3.8.