Abstract: Prediction of Stock Market movements has always been a challenging and complex task to accomplish. In this context, Artificial Neural Networks (ANNs) is eyed as one of the potential research fields to design and develop a solution to this problem. Previous research works show that Multi Layer Perceptron (MLP) based Artificial Neural Network (ANN) models trained with Backpropagation algorithm deliver the highest accuracies for the selected use case. This paper presents the design of an Artificial Neural Network trained using Levenberg-Marquardt Backpropagation algorithm as well as Bayesian Regularization Backpropagation algorithm. The Artificial Neural Network model has been developed in MATLAB 2019 software and has currently been trained using a single stock's data from the National Stock Exchange (NSE). The Artificial Neural Network (ANN) model is also tested for different parameters defining the accuracy in stock movement prediction of the model. The results achieved tell that Bayesian Regularization Backpropagation algorithm deliver better results as compared to Levenberg-Marquardt algorithm.
Keywords: Artificial Neural Networks; Stock Market Forecasting; Backpropagation training; Levenberg-Marquardt algorithm; Bayesian Regularization algorithm.
Title: Artificial Neural Network Model for Stock Movement Forecasting
Author: Ashutosh Chandra, Akanksha Kulshreshtha, Nitin Kaliraman
International Journal of Computer Science and Information Technology Research
ISSN 2348-1196 (print), ISSN 2348-120X (online)
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