Forecasting banking sectors in Indian stock markets using machine intelligence

Article type: Research Article

Authors: Arjun, R.^* | Suprabha, K.R.

Affiliations: School of Management, National Institute of Technology Karnataka, Surathkal, India

Correspondence: [*] Corresponding author: R. Arjun, School of Management, National Institute of Technology Karnataka, Surathkal 575025, Mangalore, India. E-mail: [email protected].

Abstract: The study analyze stock index closing from myriad set of technical and fundamental analysis variables extracted from real market data to assist forecast of market closing. For this, major service sector indices of Bombay stock exchange (BSE) and National stock exchange (NSE) with historical data were taken from banking industry. The predictive model performance of index closing using statistical procedures like automatic linear modeling, time-series based econometric forecasting, vector auto regression as with artificial neural network based models were simulated and analyzed. The results indicate that BSE had higher forecast accuracy using autoregressive models and market volatility factor had major influence. Whereas, NSE was impacted by quarterly performance that can be modeled using neural networks. The empirical results were contrasted with latest state-of-art research theories to provide agenda and future research challenges of market forecast systems.

Keywords: Stock index prediction, hybrid models, artificial neural networks, time-series forecast

DOI: 10.3233/HIS-190266

Journal: International Journal of Hybrid Intelligent Systems, vol. 15, no. 3, pp. 129-142, 2019