Enhanced Stock Market Prediction with Bigdata Analytics over the Cloud Data Using LSTM and Gated Recurrent Neural Network (LSTM - GRNN)

Stock market prediction is an essential field in finance, where proper prediction of stock prices will fetch reasonable amounts of money and improve the performance of various investment strategies. Nevertheless, there will always be weaknesses in using conventional predictors because of the nonlinear patterns of financial data. This study explores a cloud-based framework for stock market prediction using LSTM-GRNN, to capture time-series dependencies and patterns in sequential data. In the first data preprocessing stage, we scaled our data using Min-Max normalization techniques to avoid stability issues and minimize bias. The second stage employs the ALO algorithm to recognize the best features and reduce noise to improve the prediction precision of results derived from high dimensions. Last, the classification is done using the LSTM-GRNN model, which integrates LSTM and GRNN to consider short- and long-term dependencies of stock price movements. Moreover, suppose these models are deployed in a cloud environment. In that case, they can incur quick computations and be relatively integrated into the real-time data feed, making the system implementable for financial analysis and decision-making. This work suggests ways through which complex RNN architecture can be integrated with cloud resources to improve the performance of the stock market prediction models, pointing out the direction for future work in financial prediction.