Automated Model Discovery for Forecasting Stock Market Using Artificial Neural Network

An artificial neural network (ANN) is a framework for explaining the interaction between data entering and exiting that may be defined as linear and nonlinear. It has been extensively used in market time series forecasting and prediction. However, ANN models like the straightforward multilayer perceptron model have been linked to low bias and high variance problems. We create an ensemble artificial neural network that allows automatic model discovery by combining more than a century’s worth of multilayer perception with cutting-edge machine learning. The objective of this study was to develop an efficient forecasting model using Ensemble ANN to unravel market conundrums and make wise financial judgments. In order to address the significant changes in the stock market training dataset that were discovered while employing a straightforward multilayer perceptron model, which is based on the principle of ensemble averaging, this research used the ensemble ANN modeling technique for the period of 495 days. The agile technique has been used to complete this work. The suggested predictive model beat four conventional neural network multilayer perceptron algorithms in tests comparing their output with that of the proposed predictive model. When compared to the other four conventional models used for automated model discovery, the suggested model had the best predicted error on average for every given day.