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 Conference Papers Conference Paper Faculty Publications- Conference Papers 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 Financial Market Forecasting using Macro-Economic Variables and RNN Subject The topic of the resource GRU; LSTM; machine learning; macroeconomic variables; RNN; stock price; time series Description An account of the resource Stock market forecasting is widely recognized as one of the most important and difficult business challenges in time series forecasting. This is mainly due to its noise. The use of RNN algorithms for funding has attracted interest from traders and scientists. The best technique for learning long-term memory sequences is to use long and short networks. Based on the literature, it is acknowledged that LSTM neural networks outperform all other models. Macroeconomics is a discipline of economics that studies the behavior of the economy as a whole. Macroeconomic factors are economic, natural, geopolitical, or other variables that influence the economy of a country. This study studies and test several macroeconomic variables and their significance on stock market forecasting. In macroeconomics, we have series that are updated once a month or even once a quarter, with data that is rarely more than a few hundred characters long. The amount of data given can sometimes be insufficient for algorithms to uncover hidden patterns and generate meaningful results. Depending on the prediction needs, we proposed a feasible LSTM design and training algorithm. According to the findings of this study, the inclusion of macroeconomic variable has a significant impact on stock price prediction. 2022 IEEE. Creator An entity primarily responsible for making the resource Binoy S.J.; Jos J. Source A related resource from which the described resource is derived 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2022, pp. 1366-1371. Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. 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/10.1109/ICACITE53722.2022.9823905" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICACITE53722.2022.9823905</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135443114&amp;doi=10.1109%2FICACITE53722.2022.9823905&amp;partnerID=40&amp;md5=adaf8146b5424cabd53deb4fc2759c6a" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135443114&amp;doi=10.1109%2fICACITE53722.2022.9823905&amp;partnerID=40&amp;md5=adaf8146b5424cabd53deb4fc2759c6a</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-166543789-9 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 Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Binoy S.J., Christ University, Data Science Department, Pune, India; Jos J., Christ University, Data Science Department, Pune, India