Risk-Aware Portfolio Construction Using Transformer-Based Financial Forecasting
Abstract
The integration of attention-based neural architectures into financial decision-making represents a paradigm shift in the engineering of resilient investment systems. Traditional portfolio optimization strategies, largely predicated on the Mean-Variance framework, frequently fail to account for the long-range dependencies and non-linear structural breaks characteristic of modern global markets. This research investigates the systemic implementation of Transformer-based architectures for risk-aware portfolio construction, moving beyond simple predictive modeling to explore the socio-technical and infrastructural requirements of high-fidelity financial forecasting. We analyze the architectural trade-offs inherent in multi-head attention mechanisms when applied to high-frequency, non-stationary financial time series, emphasizing the balance between model depth and inference latency. The paper further scrutinizes the deployment requirements of these systems, including the physical high-performance computing infrastructure and the data governance frameworks necessary to maintain institutional trust. Furthermore, we address the critical dimensions of sustainability in compute-heavy financial AI, the ethical imperatives of fairness in capital allocation, and the policy implications of widespread algorithmic convergence. By synthesizing perspectives from systems engineering, information theory, and financial economics, this work provides a comprehensive roadmap for developing robust, scalable, and socially responsible investment systems. We conclude that while Transformers offer unprecedented capacity for capturing market dynamics, their successful integration requires a holistic approach to governance, infrastructure, and systemic robustness to safeguard global financial stability.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
