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 Conference Papers 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/. Title A name given to the resource Time Series Forecasting of Stock Market Volatility Using LSTM Networks Subject The topic of the resource Deep learning; Financial modeling; GARCH models; LSTM networks; Risk management; Stock market; Time series; Trading strategies; Volatility forecasting Description An account of the resource Forecasting stock market volatility is a pivotal concern for investors and financial institutions alike. This research paper employs Long Short-Term Memory (LSTM) networks, a potent class of recurrent neural networks, to predict stock market volatility. LSTM networks have proven adept at capturing intricate temporal dependencies, rendering them a fitting choice for time series data analysis. We commence by elucidating the notion of stock market volatility and its profound significance in financial decision-making. Traditional methodologies, such as GARCH models, exhibit shortcomings in deciphering the convoluted dynamics inherent in financial time series data. LSTM networks, with their capacity to model extended temporal relationships, present an encouraging alternative. In this study, we assemble historical stock price and trading volume data for a diverse array of assets, diligently preprocessing it to ensure its aptness for LSTM modeling. We systematically explore various network architectures, hyperparameter configurations, and input features to optimize the efficacy of our models. Our empirical investigations decisively underscore the supremacy of LSTM networks in capturing the subtleties of stock market volatility compared to conventional techniques. As the study progresses, we delve deeper into the complexities of LSTM network training, leveraging advanced techniques such as batch normalization and dropout to fortify model resilience. Moreover, we delve into the interpretability of LSTM models within the context of stock market forecasting. 2023 IEEE. Creator An entity primarily responsible for making the resource Praveenraj D.D.W.; Pandey M.; Victor M. Source A related resource from which the described resource is derived 2023 4th International Conference on Computation, Automation and Knowledge Management, ICCAKM 2023 Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICCAKM58659.2023.10449596" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICCAKM58659.2023.10449596</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187554066&amp;doi=10.1109%2FICCAKM58659.2023.10449596&amp;partnerID=40&amp;md5=1aacde6e1e659aa4c01f9f3c66386d3d" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187554066&amp;doi=10.1109%2fICCAKM58659.2023.10449596&amp;partnerID=40&amp;md5=1aacde6e1e659aa4c01f9f3c66386d3d</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835039324-8 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 Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Praveenraj D.D.W., School of Business and Management, Christ (Deemed to Be University), Bangalore, India; Pandey M., School of Business and Management, Christ (Deemed to Be University), Bangalore, India; Victor M., School of Business and Management, Christ (Deemed to Be University), Bangalore, India