Stock Prediction Using Artificial Neural Networks

Accurate prediction of stock price movements is highly challenging and significant topic for investors. Investors need to understand that stock price data is the most essential information which is highly volatile, non-linear, and non-parametric and are affected by many uncertainties and interrelated economic and political factors across the globe. Artificial Neural Networks (ANN) have been found to be an efficient tool in modeling stock prices and quite a large number of studies have been done on it. In this paper ANN modeling of stock prices of selected stocks under BSE is attempted to predict closing prices. The network developed consists of an input layer, one hidden layer and an output layer and the inputs being opening price, high, low, closing price and volume. Mean Absolute Percentage Error, Mean Absolute Deviation and Root Mean Square Error are used as indicators of performance of the networks. This paper is organized as follows. In the first section, the adaptability of ANN in stock prediction is discussed. In section two, we justify the using of ANNs in forecasting stock prices. Section three gives the literature review on the applications of ANNs in predicting the stock prices. Section four gives an overview of artificial neural networks. Section five presents the methodology adopted. Section six gives the simulation and performance analysis. Last section concludes with future direction of the study.