Please use this identifier to cite or link to this item: https://idr.l1.nitk.ac.in/jspui/handle/123456789/6682
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dc.contributor.authorNaik, N.
dc.contributor.authorMohan, B.R.
dc.date.accessioned2020-03-30T09:45:59Z-
dc.date.available2020-03-30T09:45:59Z-
dc.date.issued2019
dc.identifier.citationCommunications in Computer and Information Science, 2019, Vol.1000, , pp.453-459en_US
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/6682-
dc.description.abstractStock price returns forecasting is challenging task for day traders to yield more returns. In the past, most of the literature was focused on machine learning algorithm to predict the stock returns. In this work, the recurrent neural network (RNN) with long short term memory (LSTM) is studied to forecast future stock returns. It has the ability to keep the memory of historical stock returns in order to forecast future stock return output. RNN with LSTM is used to store recent stock information than old related stock information. We have considered a recurrent dropout in RNN layers to avoid overfitting in the model. To accomplish the task we have calculated stock return based on stock closing prices. These stock returns are given as input to the recurrent neural network. The objective function of the prediction model is to minimize the error in the model. To conduct the experiment, data is collected from the National Stock Exchange, India (NSE). The proposed RNN with LSTM model outperforms compared to an feed forward artificial neural network. � Springer Nature Switzerland AG 2019.en_US
dc.titleStudy of stock return predictions using recurrent neural networks with LSTMen_US
dc.typeBook chapteren_US
Appears in Collections:2. Conference Papers

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