Please use this identifier to cite or link to this item: https://idr.l2.nitk.ac.in/jspui/handle/123456789/17067
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dc.contributor.advisorR, Suprabha K.-
dc.contributor.authorR, Arjun.-
dc.date.accessioned2022-01-31T11:30:52Z-
dc.date.available2022-01-31T11:30:52Z-
dc.date.issued2021-
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/17067-
dc.description.abstractThe 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.en_US
dc.language.isoenen_US
dc.publisherNational Institute of Technology Karnataka, Surathkalen_US
dc.subjectSchool of Managementen_US
dc.subjectPredictive information systemsen_US
dc.subjectStock market forecastingen_US
dc.subjectNeural networksen_US
dc.subjectBankingen_US
dc.subjectBusiness intelligenceen_US
dc.titleA Study on Indian Stock Market Modeling using Artificial Neural Networksen_US
dc.typeThesisen_US
Appears in Collections:1. Ph.D Theses

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