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 Gupta, Vansh; George, Jossy; Chanti, S. Title A name given to the resource Volatility Prediction in the Indian Share Market Using Sentiment Analysis Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Proceedings - 2025 International Conference on Transformative Computing Technologies, ICTCT 2025;pp.382-390 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICTCT69201.2025.00076" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICTCT69201.2025.00076</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105037459775?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105037459775?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 Gupta V., CHRIST (Deemed to be University), Bengaluru, India; George J., CHRIST (Deemed to be University), Bengaluru, India; Chanti S., CHRIST (Deemed to be University), Bengaluru, India Description An account of the resource In addressing the challenge of accurate volatility prediction in the Indian share market, the study explores the performance of deep learning-based models using sentimentdriven features. A demo model was deployed using data from five major Nifty 50 stocks-RELIANCE, HDFCBANK, INFOSYS, ITC, and MARUTI-for the financial years 2020 to 2023. We compared the results of traditional ARIMA model and standalone LSTM and its hybrid variants: LSTM + CNN and LSTM+RNN. Sentiment scores were gathered from financial news using FinBERT and NLTK, and combined with stock price data to generate time-series features. While all models demonstrated promising results, the LSTM+RNN hybrid model consistently achieved the lowest MAE and RMSE, indicating improved learning of temporal dependencies. The standalone LSTM and LSTM+RNN models also showed positive results for Sharpe ratio and Maximum drawdown indicating strong economic significance of the models. The study emphasizes the potential of hybrid LSTM architectures in modeling market volatility driven by investor sentiment. Limitations included limited dataset size, exclusion of other volatility factors, and overfitting in early hybrid GARCH trials. Future work aims to expand data coverage, integrate hybrid GARCH models more effectively, and explore additional market indicators. This research highlights a scalable and effective approach for sentiment-informed volatility forecasting in financial domains. 2025 IEEE. Subject The topic of the resource ARIMA; Deep learning; FinBERT; Hybrid LSTM + RNN and LSTM + CNN; Indian share market; LSTM; MAE; RMSE; Sentiment analysis; Stock market; Volatility prediction Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833159195-3; 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