Predicting Stock Market Price Using Neural Network Model

Abstract

The present article predicts the movement of daily Indian stock market (S&P CNX Nifty) price by using Feedforward Neural Network Model over a period of eight years from January 1st 2008 to April 8th 2016. The prediction accuracy of the model is accessed by normalized mean square error (NMSE) and sign correctness percentage (SCP) measure. The study indicates that the predicted output is very close to actual data since the normalized error of one-day lag is 0.02. The analysis further shows that 60 percent accuracy found in the prediction of the direction of daily movement of Indian stock market price after the financial crises period 2008. The study indicates that the predictive power of the feedforward neural network models reasonably influenced by one-day lag stock market price. Hence, the validity of an efficient market hypothesis does not hold in practice in the Indian stock market. This article is quite useful to the investors, professional traders and regulators for understanding the effectiveness of Indian stock market to take appropriate investment decision in the stock market.

Optimization of Virtual Network Mapping Based on Improved Ant Colony Algorithm in Complex Network Environments

“…For the Japanese stock market, the authors of [13], a hybrid of Genetic Algorithm (GA) and ANN, are made to predict the next day's price for the stocks. e authors of [14] utilise a simple feed-forward neural network model with two measures, normalized mean square error (NMSE) [15] and sign correctness percentage (SCP) [15], for improved performance results.…”