A Study on Indian Stock Market Modeling using Artificial Neural Networks

Abstract

The Indian stock exchange markets, specifically in banking, are dynamic due to diverse micro and macro-level factors. Current research aims to build a predictive model for the banking sector stock market. Statistical estimation models are tested to identify the best predictive parameters. For intelligent decision support design, artificial neural network architectures are simulated. Preliminary results suggest that market volatility has a lesser impact than fundamental and technical indicators, contrary to random walk theory. The artificial neural networks have superior accuracy for National Stock Exchange prediction. However, it requires model retraining, real-time market data, whereas time-series models suit Bombay Stock Exchange forecasting. Additionally, banking stock performance strongly correlates with technological advancements. Hence, bibliometric analysis extracts areas for the implementation of predictive information systems. An integrated framework is envisaged to adopt blockchain and fintech technologies stimulating organizational impact. Lastly, future research directions provide methodological progress along with the challenges outlined.