Comparative Predictive Modeling on CNX Nifty with Artificial Neural Network


  • Christ University, Bangalore, India


CNX Nifty being an important barometer to indicate country's growth has always been followed with lots of interest from both academia and industry. Now, CNX Nifty could be predicted or not on a random basis gives rise to many a questions. This sounds redolent with any predictive modeling, though with a certain degree of accuracy inbuilt into the system. The major point of consideration is that predictive modeling could be done by various measures and mechanisms. In predictive modeling Multiple Adaptive Regression (MARS), Classification and Regression Trees (CART), Logistic OLS or Non Linear OLS could be used. Here in this study, the researcher has utilized Neural Network as a "Predictive Modeler" to predict CNX Nifty closing on certain definite time zones under consideration, because it is closer to the functioning of the human brain in comparison to the other models. As Indian markets are a clear case of the weak form of efficiency, so, Neural Network will be an ideal tool for detection or prediction in this market.


Neural Network, CNX Nifty, Predictive Modeling.

Subject Discipline


Full Text:


Burrascano, P. (1991). Learning vector quantization for the probabilistic neural network. IEEE Transactions on Neural Networks, 458–461.

Fabio Ancona, A. M. (1998). Implementing probabilistic neural networks. Neural Comput & Applic, 37–51.

Ghosh, B. (2013). Shariah Investment in India - An Unexplored Opportunity. International Journal of Innovative Research & Development ISSN 2278 7631 (Print), 33–37.

Ghosh, B. (2015, June 5). Detection of Sentiment in CNX Nifty – An Investigative Attempt Using Probabilistic Neural Network. . International Journal of Business Quantitative Economics & applied management research. 1–11.

Ghosh, B. A. (2014, April- May). BSE 100 Market Capitalization follows Sentiment of Investors or Technical Methods-An analytical study. SRJIS, 400–404.

Natarajan, P. (2012). Shariah Compliant Stocks in India- A viable & ethical Investment Vehicle. Arabian Journal of Business & Management Review ISSN, 50–62.

Majumdar, S. (2008, Jan 12). Islamic Banking in India – What is the Future potential. CRISIL young thought leader 2008, 1–12.

Specht, D. F. (1990). “Probabilistic neural networks”. Neural Networks 3, 109–118.

Stern, H. (1996). Neural Networks in Applied Statistics. Technimetrics, 205–214.

Tkacz, G. (2001). Neural Network Forecasting of Canadian GDP Growth. International Journal of Forecasting, 57–69.


  • There are currently no refbacks.