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 framework for stock price prediction using long short-term memory Subject The topic of the resource Artificial intelligence; Deep learning neural network; Long short-term memory; Mean absolute percentage error; Stock prediction; Technical indicators Description An account of the resource Forecasting stock prices is always considered as complicated process due to the dynamic and noisy characteristics of stock data influenced by external factors. For predicting the stock market, several approaches have been put forward. Many academics have successfully forecasted stock prices using soft computing models. Recently, there has been growing interest in applying deep learning techniques in combination with technical indicators to forecast stock prices, attracting attention from both investors and researchers. This paper focuses on developing a reliable model for anticipating future stock prices in one day advance using Long Short-Term Memory (LSTM). Three steps make up the suggested model. The approach begins with ten technical indicators computed from previous data as feature vectors. The second phase involves data normalization to scale the feature vectors. Finally, in the third phase, the LSTM model analyzes the closing price for the next day using the normalized characteristics as input. Two stock markets, NASDAQ and NYSE are chosen to evaluate the efficacy of the developed model. To demonstrate how effective the new model is in making predictions, its performance is compared to earlier models. Comparing the suggested model to other models, the findings revealed that it had a high level of prediction accuracy. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Creator An entity primarily responsible for making the resource Chandar S.K. Source A related resource from which the described resource is derived Soft Computing, Vol-28, No. 17-18, pp. 10557-10567. 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 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/s00500-024-09836-3" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s00500-024-09836-3</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198502005&amp;doi=10.1007%2Fs00500-024-09836-3&amp;partnerID=40&amp;md5=102b635ab0ef7ec3725454c066bb2eb3" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198502005&amp;doi=10.1007%2fs00500-024-09836-3&amp;partnerID=40&amp;md5=102b635ab0ef7ec3725454c066bb2eb3</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 Chandar S.K., School of Business and Management, CHRIST (Deemed to be University), Bangalore, India