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 Santhosh Kumar, S.; Manjunatha Swamy, C.; Balamurugan, M. Title A name given to the resource Enhancing Stock Market Forecasting with LLMs, Sentiment Analysis and Technical Indicators 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 of 2025 IEEE International Conference on Contemporary Computing and Communications, InC4 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/InC465408.2025.11256269" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/InC465408.2025.11256269</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105030333276?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105030333276?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 Santhosh Kumar S., Christ University, Dept. of CSE, Bangalore, India; Manjunatha Swamy C., Christ University, Dept. of CSE, Bangalore, India; Balamurugan M., Christ University, Dept. of CSE, Bangalore, India Description An account of the resource Forecasting stock market trends remains a complex and demanding endeavor due to the intricate and dynamic nature of financial markets. This study explores the combination of sentiment analysis with technical indicators to improve the accuracy of stock price predictions. The research incorporates stock market and news data spanning from January 2015 to June 2023, ensuring a well-aligned and comprehensive dataset. Data was sourced using the Google News API and the Bombay Stock Exchange (BSE), followed by rigorous preprocessing, which involved handling missing values and standardizing sentiment scores for accuracy and consistency. To analyze sentiment, tools like VADER, TextBlob, and the Gemini-1.5-Flash API were employed, with sentiment scores aggregated at the stock level. Simultaneously, key technical indicators including the Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), Bollinger Bands, and Exponential Moving Averages were derived from stock price patterns. These diverse data points were integrated to predict 14-day closing stock prices, leveraging the Gemini1.5-Flash model for forecasting. The model s performance was assessed using various error metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and Symmetric Mean Absolute Percentage Error (SMAPE). The results indicated strong predictive accuracy for stable stocks while pointing out challenges in forecasting highly volatile stocks. Ultimately, the findings suggest that combining sentiment analysis with technical indicators strengthens stock market trend predictions, offering a solid foundation for future advancements in real-time financial analytics. 2025 IEEE. Subject The topic of the resource Gemini-1.5-flash; multimodal data; sentiment analysis; Stock market prediction; technical indicators Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833152118-9; 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