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 Grey Wolf optimization-Elman neural network model for stock price prediction Subject The topic of the resource Average relative variance; Bio-inspired algorithm; Elman neural network; Grey Wolf optimization; Stock prediction Description An account of the resource Over the past two decades, assessing future price of stock market has been a very active area of research in financial world. Stock price always fluctuates due to many variables. Thus, an accurate prediction of stock price can be considered as a tough task. This study intends to design an efficient model for predicting future price of stock market using technical indicators derived from historical data and natural inspired algorithm. The model adopts Elman neural network (ENN) because of its ability to memorize the past information, which is suitable for solving stock problems. Trial and error-based method is widely used to determine the parameters of ENN. It is a time-consuming task. To address such an issue, this study employs Grey Wolf optimization (GWO) algorithm to optimize the parameters of ENN. Optimized ENN is utilized to predict the future price of stock data in 1day advance. To evaluate the prediction efficiency, proposed model is tested on NYSE and NASDAQ stock data. The efficacy of the proposed model is compared with other benchmark models such as FPA-ELM, PSO-MLP, PSOElman,CSO-ARMA and GA-LSTM to prove its superiority. Results demonstrated that the GWO-ENN model provides accurate prediction for 1day ahead prediction and outperforms the benchmark models taken for comparison. 2020, Springer-Verlag GmbH Germany, part of Springer Nature. Creator An entity primarily responsible for making the resource Kumar Chandar S. Source A related resource from which the described resource is derived Soft Computing, Vol-25, No. 1, pp. 649-658. Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH 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/10.1007/s00500-020-05174-2" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s00500-020-05174-2</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087739609&amp;doi=10.1007%2Fs00500-020-05174-2&amp;partnerID=40&amp;md5=5c337d073235478f7fb9de9af48d928b" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087739609&amp;doi=10.1007%2fs00500-020-05174-2&amp;partnerID=40&amp;md5=5c337d073235478f7fb9de9af48d928b</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 14327643 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 Kumar Chandar S., School of Business and Management, CHRIST (Deemed to be University), Bangalore, India