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 An Intelligent Portfolio Management Scheme Based On Hybrid Deep Reinforcement Learning and Cumulative Prospective Approach Subject The topic of the resource Assets; Deep reinforcement learning; Optimal Return; Optimization; Portfolio selection; Stock price prediction Description An account of the resource Stock markets retain an extensive role towards economic growth of diverse countries and it is a place where investors invest assured amount to earn more profit and the issuers pursue the investors for project investing. However, it is deliberated as a challenging task to buy and sell because of its explosive and complex nature. The existing portfolio optimization models are primarily focused on just improving the returns whereas, the selection of optimal assets is least focused. Hence, the proposed research article focuses on the integration of stock prediction with the portfolio optimization model (SPPO). Initially, the stock prices for the next period are predicted using the hybrid deep reinforcement learning (DRL) model. Within this prediction model, the gated recurrent unit network (GRUN) model is utilized to simulate the interactions of the agent with the environment. The best actions in the prediction model are determined throughout the prediction process using the quantum differential evolution algorithm (Q-DEA). After the prediction of best assets, the optimal portfolio with the best assets is selected using the cumulative prospect theory (CPT) model. The work will be implemented in python and evaluated using the NIFTY-50 Stock Market Data (2000 -2021) dataset. Minimal error rates of 0.130, 0.114, 0.148 and 0.153 is obtained by the proposed model in case of MSE, MAE, RMSE and MAPE. 2024 IEEE. Creator An entity primarily responsible for making the resource Ramaiah K.; Soundarabai P.B. Source A related resource from which the described resource is derived Proceedings of ICWITE 2024: IEEE International Conference for Women in Innovation, Technology and Entrepreneurship, pp. 117-123. 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 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICWITE59797.2024.10503521" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICWITE59797.2024.10503521</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192562600&amp;doi=10.1109%2FICWITE59797.2024.10503521&amp;partnerID=40&amp;md5=4955509a44cd3910fb93bea3a7ac2ab9" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192562600&amp;doi=10.1109%2fICWITE59797.2024.10503521&amp;partnerID=40&amp;md5=4955509a44cd3910fb93bea3a7ac2ab9</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835038328-7 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 Ramaiah K., Christ (Deemed to Be University), Department of Computer Science, Bengaluru, 560029, India; Soundarabai P.B., Christ (Deemed to Be University), Department of Computer Science, Bengaluru, 560029, India