Neural Nets for Stock Indices: Investigating Effect of Change in Hyperparameters

Abstract

Artificial neural networks have seen an outburst of interest in past decade. There has been an increasing use of ANNs in prediction based studies owing to their huge performance accuracy. They have been successfully applied across various domains like medicine, geology, finance, physics, engineering etc. The system of neural nets witnesses rise in complexity with increase in number of layers and number of neurons and possesses the capacity to solve intricate problems. The researchers, world over, consider the neural network with three layers (input, hidden and output) a universal approximator of functions as it has given outstanding results in data validation, price forecasting, sales forecasting, customer research etc. over the years. In most of the previous studies, either a standard ANN model has been taken or a default model has been tested using various softwares. But as we understand, a lot of hit and trial should be done by altering the hyperparameters to get the best performance model. In our study we attempt to prove the same point and try to find the best model for our data set wherein we predict the BSE sensex closing price of the next day using previous day data (high price, low price, open price, close price and trade volume). We use deep neural networks with backpropagation and have altered the hyperparameters: number of nodes in hidden layers, the activation function of hidden layers, Number of epochs, the batch size and hence the iterations in each epoch. The model performance was measured with the help of root mean square error on test set of the model. We are able to bring out the differences of tuning of hyperparameter and ultimately find the best predictor model for BSE sensex close value.