The researchers have been working on determining predictable patterns to forecast stock prices. But owing to nonstationary and nonlinear patterns of the data, the detection of new models and forecasting techniques have become an extremely intricate task. Artificial Neural Network (ANN) models are highly flexible functional algorithms, developed using machine’s cognitive learning. In recent years, there has been an exponential increase in neural network applications in finance for the tasks such as pattern recognition, classification and time series forecasting. ANNs provide a considerably accurate means to examine nonlinearity in stock prices. However, large numbers of parameters need to be selected in order to develop an appropriate neural network forecasting model. The present study aims at predicting the stock prices of CNX Nifty 500 using assorted independent variables using ANNs. The variables used in the study are USD-INR exchange rate, crude oil price and major stock indices of USA (S&P 500), Euro Zone (Euro Stoxx 50), China (Shanghai Composite Index) and Japan (Nikkei 225). The paper considers daily data from January 2004 to December 2013. The series have been divided into training data and testing data to arrive at the most accurate model to be further used for predicting the stock prices for the next 10 months. The frequency of the given variables is daily, hence it would provide for a more efficient model incorporating smaller details such as daily volatility effect. The stock exchange data from foreign stock markets which open before and after the stock markets in India provide for change in market sentiments overnight. The preliminary data testing yielded encouraging results for the model. The predicted values of stock have a testing accuracy of more than 85%.

There have been innumerable techniques developed to predict stock markets as the results have been economically fruitful to its developers. These techniques rely on the quality of information used in different prediction models; however, many uncertain and interrelated factors also affect stock prices and their importance may be difficult to measure numerically. At the outset, stock markets are complicated and not entirely comprehensible. The returns of the stock market are difficult to predict. A vast amount of research has been carried out to analyze the complexity, nonlinearity, nonstationarity and chaotic nature of the stock market in order to come out with a better stock market prediction model.

The present study aims at predicting the stock prices of CNX Nifty 500 using assorted independent variables using Artificial Neural Networks (ANNs). Neural Networks (NNs) are computer programs consisting of computing nodes and interconnections between nodes (Yao et al., 1999). They are recognized as effective tools for financial forecasting (Yao and Tan, 2001) and can ‘learn’ from experience as do humans, cope with nonlinear data, and deal with partially understood application domains, such as stock market behaviors. Moreover, the fundamental stock market indicators, gross domestic product, interest rate, gold prices and exchange rates and technical indicators, including closing prices, opening prices, highest prices and lowest prices, can be incorporated into neural networks to help improve predictive outputs (Yao et al., 1999).

Applied Finance Journal, Nonlinear Model, Predict Stock Prices, India, Artificial Neural Network (ANN), Neural Networks (NNs).