Please use this identifier to cite or link to this item: http://idr.nitk.ac.in/jspui/handle/123456789/11742
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dc.contributor.authorNaik, N.
dc.contributor.authorMohan, B.R.
dc.date.accessioned2020-03-31T08:35:32Z-
dc.date.available2020-03-31T08:35:32Z-
dc.date.issued2019
dc.identifier.citationNational Academy Science Letters, 2019, Vol., , pp.-en_US
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/11742-
dc.description.abstractPredicting stock price movements is difficult due to the speculative nature of the stock market.Accurate predictions of stock prices allow traders to increase their profits. Stock prices react when receiving new information.During the trading day, it is difficult to understand the up and down movements signaled by stock prices. This paper addresses the problem of fluctuations in stock prices. We proposed the method to identify stock movement trend in data, and this method considered the combination of candlestick data and technical indicator values. The outcome of this method is given as inputs to a deep neural network (DNN) to classify a stock price s up and down movements. National Stock Exchange, India, datasets are considered for an experiment from the years 2008 to 2018. The work is carried out using H2O deep learning on an RStudio platform. Experimental results are compared with a three-layer artificial neural network (ANN) model. The proposed five-layer DNN model outperforms state-of-the-art methods by 8 11% in predicting up and down movements of a given stock. 2019, The National Academy of Sciences, India.en_US
dc.titleIntraday Stock Prediction Based on Deep Neural Networken_US
dc.typeArticleen_US
Appears in Collections:1. Journal Articles

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