The rapid digital transformation of global financial markets has fundamentally altered the dynamics of portfolio construction, risk assessment, and asset allocation. Traditional portfolio theories, although foundational, were developed in environments characterized by relatively low data velocity, limited market microstructure complexity, and minimal computational adaptability. In contrast, modern markets operate under conditions of extreme volatility, high dimensionality, and continuous feedback loops driven by algorithmic and high frequency trading. This paradigm shift has created an urgent need for adaptive, intelligent, and scalable portfolio management frameworks capable of learning from complex, nonstationary financial environments in real time. Deep reinforcement learning has emerged as a leading paradigm in this domain, offering the ability to integrate sequential decision making, nonlinear representation learning, and dynamic optimization under uncertainty. However, despite substantial progress in algorithmic trading and portfolio optimization, a persistent gap remains between theoretical deep reinforcement learning models and their practical deployment in cloud-based, risk-aware portfolio management systems.

This study addresses that gap by developing and theoretically validating an adaptive cloud-based deep reinforcement learning framework for dynamic portfolio risk prediction and asset allocation. The framework draws conceptual inspiration from recent intelligent cloud architectures that integrate reinforcement learning with scalable computational infrastructure, most notably the intelligent cloud framework for dynamic portfolio risk prediction proposed by Mirza and colleagues in a recent IEEE conference contribution (Mirza et al., 2025). Building upon this foundational work, the present study extends the conceptual scope by embedding risk-sensitive policy learning, correlation-aware state representations, and multi-temporal portfolio rebalancing within a unified cloud-native architecture. The central premise of the article is that portfolio risk is not a static property but an evolving construct shaped by market regimes, investor behavior, and structural feedback loops, and that only learning systems capable of continuous adaptation can meaningfully manage this complexity.

Deep reinforcement learning, portfolio risk prediction, cloud computing, algorithmic trading

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