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 Deep Learning for Stock Market Index Price Movement Forecasting Using Improved Technical Analysis Subject The topic of the resource Deep learning method; Ease of movement; Gated recurrent unit; Market forecasting; Relative volatility index; Reynolds number Description An account of the resource Equity market forecasting is difficult due to the high explosive nature of stock data and its impact on investor's stock investment and finance. The stock market serves as an indicator for forecasting the growth of the economy. Because of the nonlinear nature, it becomes a difficult job to predict the equity market. But the use of different methods of deep learning has become a vital source of prediction. These approaches employ time-series stock data for deep learning algorithm training and help to predict their future behavior. In this research, deep learning methods are evaluated on the India NIFTY 50 index, a benchmark Indian equity market, by performing a technical data augmentation approach. This paper presents a Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), and the three variants of Gated Recurrent Unit (GRU) to analyze the model results. The proposed three GRU variants technique is evaluated on two sets of technical indicator datasets of the NIFTY 50 index (namely TA1 and TA2) and compared to the RNN and LSTM models. The experimental outcomes show that the GRU variant1 (GRU1) with TA1 provided the lowest value of Mean Square Error (MSE=0.023) and Root Mean Square Error (RMSE= 0.152) compared with existing methods. In conclusion, the NIFTY 50 index experiments with technical indicator datasetTA1 were more efficient by GRU. Hence, TA1 can be used to construct a robust predictive model in forecasting the stock index movements. 2021. All Rights Reserved. Creator An entity primarily responsible for making the resource Manjunath C.; Marimuthu B.; Ghosh B. Source A related resource from which the described resource is derived International Journal of Intelligent Engineering and Systems, Vol-14, No. 5, pp. 129-141. Publisher An entity responsible for making the resource available Intelligent Network and Systems Society Date A point or period of time associated with an event in the lifecycle of the resource 2021-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.22266/ijies2021.1031.13" target="_blank" rel="noreferrer noopener">https://doi.org/10.22266/ijies2021.1031.13</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114719990&amp;doi=10.22266%2Fijies2021.1031.13&amp;partnerID=40&amp;md5=976027ccb955dbff97d1c4592d6f7adc" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114719990&amp;doi=10.22266%2fijies2021.1031.13&amp;partnerID=40&amp;md5=976027ccb955dbff97d1c4592d6f7adc</a> Rights Information about rights held in and over the resource All Open Access; Bronze Open Access Relation A related resource ISSN: 2185310X 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 Manjunath C., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bengaluru, India; Marimuthu B., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bengaluru, India; Ghosh B., Department of Finance, RV Institute of Management, Bengaluru, India