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 Hari Krishnan, B.; Kumar, Dalvin Vinoth; Kavitha, R. Title A name given to the resource An Enhanced A3C-LSTM Framework with Attention for Dynamic Portfolio Allocation in Equity Markets 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 IC-DECON 2025 - 2025 International Conference on Data, Energy and Communication Network, Proceedings; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/DECoN67170.2025.11448012" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/DECoN67170.2025.11448012</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105037337169?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105037337169?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 Hari Krishnan B., Christ University, Dept. of Statistics and Data Science, Bengaluru, India; Kumar D.V., Christ University, Dept. of Statistics and Data Science, Bengaluru, India; Kavitha R., Christ University, Dept. of Statistics and Data Science, Bengaluru, India Description An account of the resource Portfolio optimization in dynamic financial markets presents a significant challenge for traditional models. This paper introduces an advanced deep reinforcement learning framework for portfolio management based on an enhanced Asynchronous Advantage Actor-Critic (A3C) algorithm. This paper integrate's a Long Short-Term Memory layer and a multi-head attention mechanism into the actor-critic architecture to more effectively capture temporal dependencies and feature importance within financial time-series data. The model's novelty lies in its enriched state representation, which includes a comprehensive set of technical indicators, inter-asset correlation matrices, and market regime analysis. Furthermore, we employ a sophisticated riskadjusted reward function, incorporating penalties for drawdown and volatility alongside a bonus based on the Sortino ratio. The agent was trained and tested in a simulated environment using historical daily price data from five major S&amp;P 500 stocks. Experimental results demonstrate that our agent successfully learns a robust and adaptive allocation strategy, significantly outperforming an equal-weight benchmark in terms of overall return, Sharpe ratio, and maximum drawdown. This study underscores the potential of sophisticated DRL architectures to navigate complex market dynamics and optimize for riskadjusted performance. 2025 IEEE. Subject The topic of the resource A3C; Attention Mechanism; Computational Finance; Deep Reinforcement Learning; LSTM; Portfolio Management; Stock Selection Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833159442-8; 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