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 Enhancing Stock Market Trend Prediction Using Explainable Artificial Intelligence and Multi-source Data Subject The topic of the resource Explainable artificial intelligence; Machine learning; Multi-source data; Sentiment analysis; Stock market prediction; Technical indicators Description An account of the resource Determining the trend of the stock market is a complex task influenced by numerous factors like fundamental variables, company performance, investor behavior, sentiments expressed in social media, etc. Although machine learning models support predicting stock market trends using historical or social media data, reliance on a single data source poses a serious challenge. This study introduces a novel Explainable artificial intelligence (XAI) to address a binary classification problem wherein the objective is to predict the trend of the stock market, utilizing an integration of multiple data sources. The dataset includes trading data, news and Twitter sentiment, and technical indicators. Sentiment analysis and the Natural Language Toolkit are utilized to extract the qualitative information from social media data. Technical indicators, or quantitative characteristics, are therefore generated from trade data. The technical indicators are fused with the stock sentiment features to predict the future stock market trend. Finally, a machine learning model is employed for upward or downward stock trend predictions. The proposed model in this study incorporates XAI to interpret the results. The presented model is evaluated using five bank stocks, and the results are promising, outperforming other models by reporting a mean accuracy of 90.14%. Additionally, the proposed model is explainable, exposing the rationale behind the classifier and furnishing a complete set of interpretations for the attained outcomes. 2024, American Scientific Publishing Group (ASPG). All rights reserved. Creator An entity primarily responsible for making the resource Ranjith J.; Chandar S.K. Source A related resource from which the described resource is derived Fusion: Practice and Applications, Vol-16, No. 2, pp. 178-189. Publisher An entity responsible for making the resource available American Scientific Publishing Group (ASPG) 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/10.54216/FPA.160211" target="_blank" rel="noreferrer noopener">https://doi.org/10.54216/FPA.160211</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201629045&amp;doi=10.54216%2FFPA.160211&amp;partnerID=40&amp;md5=0c98cd96375a5917d38668bb4cb6923c" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201629045&amp;doi=10.54216%2fFPA.160211&amp;partnerID=40&amp;md5=0c98cd96375a5917d38668bb4cb6923c</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 27700070 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 Ranjith J., School of Business and Management, CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Chandar S.K., School of Business and Management, CHRIST (Deemed to be University), Karnataka, Bengaluru, India