An effective deep learning model with reduced error rate for accurate forecast of stock market direction

Abstract

Prediction using ML models is not well adapted in many portions of business decision-making due to a lack of clarity and flexibility. In order to provide a positive risk-adjusted price for stocks by evaluating historical transaction data and retaining more accuracy with a reduced error rate, the suggested framework aims to use deep learning method. The deep learning methodology, which can handle time-series data, is applied in this work. The measurements of MSE and RMSE error rates, which indicate how far the measured values are from the regression line, are used to produce the findings. The dispersion of these residuals is evaluated by RMSE. It demonstrates how densely the data is clustered around the line of best fit. In this work, a novel deep learning approach is compared to deep LSTM, GA, and Harris Hawk optimization. Outcomes were obtained and exhibited for the various firm stocks dataset as part of this investigation, which amply demonstrates the usefulness of the proposed strategy with a lower error rate.