Please use this identifier to cite or link to this item: https://idr.l4.nitk.ac.in/jspui/handle/123456789/8980
Title: Modeling hybrid indicators for stock index prediction
Authors: Arjun, R.
Suprabha, K.R.
Issue Date: 2020
Citation: Advances in Intelligent Systems and Computing, 2020, Vol.940, , pp.193-202
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. � Springer Nature Switzerland AG 2020.
URI: http://idr.nitk.ac.in/jspui/handle/123456789/8980
Appears in Collections:2. Conference Papers

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