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 Faculty Publications 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/. Creator An entity primarily responsible for making the resource Shashidhar Yadav, J.; Kulkarni, Shrinivas; Rajath, B.S.; Pradeep Kumar, S.V.; Priyadarshini, N.; Devananda, H.M. Title A name given to the resource Forecasting Market Turbulence: A Multi-model Study Using GARCH, Random Forest, and LSTM in the Indian Stock Market Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Communications in Computer and Information Science;Volume;2863 CCIS;pp.334-347 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-032-17184-9_25" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-032-17184-9_25</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105040391077?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105040391077?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Shashidhar Yadav J., School of Business and Management, Christ University, Karnataka, Bengaluru, India; Kulkarni S., School of Business and Management, Christ University, Karnataka, Bengaluru, India; Rajath B.S., School of Business and Management, Christ University, Karnataka, Bengaluru, India; Pradeep Kumar S.V., School of Business and Management, Christ University, Karnataka, Bengaluru, India; Priyadarshini N., Post Graduate Department of Business Administration, Seshadripuram College, Karnataka, Bengaluru, India; Devananda H.M., Department of Management, Adichunchanagiri Institute of Technology, Karnataka, Chikkamagaluru, India Description An account of the resource The dynamic and unpredictable nature of the Indian stock market presents significant challenges in forecasting return behavior and managing financial risk. This study explores market turbulence through a comparative analysis of three distinct modeling approaches: the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, Random Forest, and Long Short-Term Memory (LSTM) networks. By analyzing historical return data from Indian Nifty indices, the research captures both linear dependencies and complex nonlinear patterns associated with market volatility. The results highlight the GARCH models strength in modeling conditional volatility, while the machine learning and deep learning techniquesRandom Forest and LSTMexhibit enhanced predictive power in capturing intricate fluctuations in stock returns. The findings suggest that integrating traditional econometric methods with data-driven approaches offers a more comprehensive and accurate understanding of market dynamics. This multi-model framework is valuable for investors, financial analysts, and policymakers aiming to anticipate and navigate periods of heightened market uncertainty. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. Subject The topic of the resource GARCH; Indian stock market; Market volatility; Random forest; Return prediction Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 18650929; ISBN: 978-303217183-2; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online