ItemConference Paper

An Enhanced A3C-LSTM Framework with Attention for Dynamic Portfolio Allocation in Equity Markets

Title
An Enhanced A3C-LSTM Framework with Attention for Dynamic Portfolio Allocation in Equity Markets
Creator
Hari Krishnan, B.; Kumar, Dalvin Vinoth; Kavitha, R.
Description
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&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.
Source
IC-DECON 2025 - 2025 International Conference on Data, Energy and Communication Network, Proceedings;
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
A3C; Attention Mechanism; Computational Finance; Deep Reinforcement Learning; LSTM; Portfolio Management; Stock Selection
Coverage
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
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833159442-8;
Format
online
Language
English
Type
Conference paper

Citation

Hari Krishnan, B.; Kumar, Dalvin Vinoth; Kavitha, R., “An Enhanced A3C-LSTM Framework with Attention for Dynamic Portfolio Allocation in Equity Markets,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/25821.

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