Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Modeling hybrid indicators for stock index prediction(Springer Verlag service@springer.de, 2020) Arjun, R.; Suprabha, K.R.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. © Springer Nature Switzerland AG 2020.Item Deep Learning for Stock Index Tracking: Bank Sector Case(Springer Science and Business Media Deutschland GmbH info@springer-sbm.com, 2021) Arjun, R.; Suprabha, K.R.; Majhi, R.The current study explores the efficacy of deep learning models in stock market prediction specific to banking sector. The secondary data of major fundamental indicators and technical variables during 2004–2019 periods of two banking indices, BSE BANKEX and NIFTY Bank of Bombay stock exchange and National stock exchange, respectively, are collected. The factors impacting market index prices were analyzed using nonlinear autoregressive neural network. Preliminary findings contradict the general random walk hypothesis theory and model improvement over previous studies. The implications from practical and theoretical perspective for stakeholders are discussed. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.