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 Stock market prediction employing ensemble methods: the Nifty50 index Subject The topic of the resource Bagging; Boosting; Fundamental analysis; Machine learning; National stock exchange fifty; Stacking; Stock market forecasting Description An account of the resource Accurately forecasting stock fluctuations can yield high investment returns while minimizing risk. However, market volatility makes these projections unlikely. As a result, stock market data analysis is significant for research. Analysts and researchers have developed various stock price prediction systems to help investors make informed judgments. Extensive studies show that machine learning can anticipate markets by examining stock data. This article proposed and evaluated different ensemble learning techniques such as max voting, bagging, boosting, and stacking to forecast the Nifty50 index efficiently. In addition, an embedded feature selection is performed to choose an optimal set of fundamental indicators as input to the model, and extensive hyperparameter tuning is applied using grid search to each base regressor to enhance performance. Our findings suggest the bagging and stacking ensemble models with random forest (RF) feature selection offer lower error rates. The bagging and stacking regressor model 2 outperformed all other models with the lowest root mean square error (RMSE) of 0.0084 and 0.0085, respectively, showing a better fit of ensemble regressors. Finally, the findings show that machine learning algorithms can help fundamental analyses make stock investment decisions. 2024, Institute of Advanced Engineering and Science. 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 IAES International Journal of Artificial Intelligence, Vol-13, No. 2, pp. 2047-2057. Publisher An entity responsible for making the resource available Institute of Advanced Engineering and Science 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/10.11591/ijai.v13.i2.pp2049-2059" target="_blank" rel="noreferrer noopener">https://doi.org/10.11591/ijai.v13.i2.pp2049-2059</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192996983&amp;doi=10.11591%2Fijai.v13.i2.pp2049-2059&amp;partnerID=40&amp;md5=defcf1993f4e9872d40f39c2da5ffd84" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192996983&amp;doi=10.11591%2fijai.v13.i2.pp2049-2059&amp;partnerID=40&amp;md5=defcf1993f4e9872d40f39c2da5ffd84</a> Rights Information about rights held in and over the resource All Open Access; Hybrid Gold Open Access Relation A related resource ISSN: 20894872 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, School of Engineering and Technology, Christ University, Bengaluru, India; Marimuthu B., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Bengaluru, India; Ghosh B., Symbiosis Institute of Business Management, Symbiosis International (Deemed University), Bengaluru, India