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 Rai, Shashank Kumar; Awasthi, Yashmita; Singh, Shanu; Ahuja, Shivam Title A name given to the resource Investigating Risk Factors for Enhanced Portfolio Performance: An AI Approach for Indian Midcap Market 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 Lecture Notes in Networks and Systems;Volume;1294 LNNS;pp.173-195 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-3253-4_13" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-3253-4_13</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105010648861?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105010648861?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 Rai S.K., Christ University, Bengaluru, India; Awasthi Y., Christ University, Bengaluru, India; Singh S., Christ University, Bengaluru, India; Ahuja S., Christ University, Bengaluru, India Description An account of the resource This research investigates the potential of machine learning (ML) for constructing portfolios that outperform human-based management, specifically focusing on the Indian midcap market. The study compares AI-based portfolio compositions, optimised using various risk measures, to the holdings of top midcap mutual funds. In this research, the top five midcap mutual funds sectoral distributions, portfolio compositions, and AI-generated portfolios are examined. According to the research, there is significant performance potential in the AI-generated portfolio, particularly when taking shorter investment horizons into account. Portfolios that maximise the Sharpe ratio produced the best returns throughout the course of the test period for four out of the six sectors, according to the research statistics. Additionally, in order to shed light on the effectiveness and possible advantages of our strategy, our study compares the suggested technique to existing investing strategies that concentrate on particular corporations as well as well-established market benchmarks. The research shows that, particularly when taking shorter investment horizons into account, the AI-generated portfolio has great performance potential. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource Machine learning; Nifty midcap mutual funds; Nifty midcap100; Portfolio optimisation; Risk-return dynamics; Sharpe ratio Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981963252-7; 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