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 Book Chapter Faculty Publications- Book Chapter 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 Saha, Swagatam; Chaudhary, Babita Title A name given to the resource AI Chatbots and Market Dynamics: Analyzing the Impact of AI on the Indian Stock Market 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 Behavioral Finance and AI Tools for Sustainability;pp.151-199 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.4018/979-8-3693-9684-1.ch007" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/979-8-3693-9684-1.ch007</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105021736031?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105021736031?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 Saha S., Christ University, Bangalore, India; Chaudhary B., Christ University, Bangalore, India Description An account of the resource This study examines the potential of ChatGPT v4.0 for stock market analysis, particularly within the Indian stock market. The research uses prompt engineering to assess ChatGPT's ability to recommend stocks, construct portfolios, provide fundamental analysis, and understand price movements. The study combines sentiment analysis of news data with historical price data to forecast future price movements. Results demonstrate ChatGPT's effectiveness in identifying investment opportunities and providing insights for decision- making. The study also evaluates the accuracy of ChatGPTs predictions, finding an 88.89% accuracy rate based on news sentiment analysis and a 94% accuracy rate when integrating sentiment analysis with historical price data. Despite limitations, including constraints in handling large datasets, ChatGPT exhibits promise for supporting investors in the Indian stock market. Future research should focus on refining models and addressing ethical considerations to ensure responsible AI integration in finance. 2025 by IGI Global Scientific Publishing. All rights reserved. Publisher An entity responsible for making the resource available IGI Global Relation A related resource ISBN: 979-836939686-5; 979-836939684-1; Language A language of the resource English Type The nature or genre of the resource Book chapter 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