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Modeling Hybrid Indicators for Stock Index Prediction

  • R. ArjunEmail author
  • K. R. Suprabha
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 940)

Abstract

The study aims to assess the major predictors of stock index closing using select set of technical and fundamental indicators from market data. Here two of major service sector specific indices of Bombay stock exchange (BSE) and National stock exchange (NSE) with historical data from 2004 up to 2016 are considered. By experimental simulation, the predictive estimates of index closing using automatic linear modeling, time-series based forecasting, and also artificial neural network models are analyzed. While linear models show better performance for BSE, artificial neural network based models exhibit higher predictive modeling accuracy for NSE. The design aspects are outlined for augmenting intelligent market prediction systems.

Keywords

Stock index forecasting Artificial neural networks 

Notes

Acknowledgments

The first author acknowledges National Institute of Technology Karnataka, Surathkal for permitting the usage of resources and providing research scholarship support. The authors thank reviewers for helpful comments.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.School of ManagementNational Institute of Technology KarnatakaMangaloreIndia

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