Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Forecasting NIFTY 50 in Volatile Markets Using RNNLSTM: A Study on the Performance of Neural Network Models During the COVID-19 Pandemic Subject The topic of the resource forecasting; LSTM; neural network; NIFTY50; RNN Description An account of the resource The COVID-19 pandemic has shown us how the market can be highly uncertain and volatile at certain times. This brings a new level of challenges to all the investors and active traders in the market, as they have not seen such a movement in the past. However, as technology is evolving, highly sophisticated tools and techniques are being used by hedge funds and other investment banks to track down these movements and turn this into an opportunity. In this paper, we try to analyse how recurrent neural network (RNN) with long- and short-term memory architecture performs under volatile market conditions. For this study, we tried to perform a comparative analysis between two models within two successive time periods, where one is trained in a volatile market condition and the other in a relatively low volatile market condition. The results showed that the RNN model is less accurate in predicting the prices in a volatile market compared to a relatively low volatile market. We also compared these two models to a separate model where we trained using the combined data from the two successive time periods. Even though the addition in data points for the neural network produced a better result compared to the model trained under volatile conditions, it did not significantly perform better than the model, which was trained in the low volatile period. 2022 Management Development Institute. Creator An entity primarily responsible for making the resource Gnanendra M.; Sainath A.R.; Jijo P. Source A related resource from which the described resource is derived Vision Publisher An entity responsible for making the resource available Sage Publications India Pvt. Ltd Date A point or period of time associated with an event in the lifecycle of the resource 2022-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1177/09722629221106251" target="_blank" rel="noreferrer noopener">https://doi.org/10.1177/09722629221106251</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132909080&amp;doi=10.1177%2F09722629221106251&amp;partnerID=40&amp;md5=f643b0d94845590951de418c01b876c3" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132909080&amp;doi=10.1177%2f09722629221106251&amp;partnerID=40&amp;md5=f643b0d94845590951de418c01b876c3</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 9722629 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Gnanendra M., Department of Management Studies and Centre for Research, New Horizon College of Engineering, Karnataka, Bangalore, India; Sainath A.R., Department of Management Studies and Centre for Research, New Horizon College of Engineering, Karnataka, Bangalore, India; Jijo P., School of Business and Management, CHRIST (Deemed to Be University), Karnataka, Bengaluru, India