A Hybrid LSTM–Attention Network for Stock Market Trend Prediction and Risk Analysis
Keywords:
The increasing complexity of global financial markets has necessitated the development of advanced computational frameworks capable of navigating the stochastic and non-linear nature of asset price dynamics. Traditional linear econometric models and basic recurrent architectures often struggle to capture the multi-scale temporal dependencies and sudden structural shifts characteristic of modern equities markets. This research proposes a system-level investigation of a hybrid architecture integrating Long Short-Term Memory (LSTM) units with a multi-head attention mechanism to enhance stock market trend prediction and systemic risk analysis. By leveraging the sequential memory retention of LSTM layers alongside the selective weighting capabilities of the attention mechanism, the framework achieves a more nuanced interpretation of both historical price trajectories and transient volatility signals. This paper moves beyond mere predictive performance to examine the broader socio-technical implications of such systems. We conduct a thorough analysis of architectural trade-offs, particularly the tension between model depth and inference latency in high-frequency environments. Furthermore, the discussion addresses critical dimensions of algorithmic governance, the physical and environmental infrastructure required to sustain high-compute financial AI, and the policy challenges associated with model-driven market convergence. By situating the hybrid LSTM–Attention network within the context of global financial stability, this research offers a comprehensive roadmap for the deployment of robust, fair, and sustainable predictive systems in the financial sector.Abstract
The increasing complexity of global financial markets has necessitated the development of advanced computational frameworks capable of navigating the stochastic and non-linear nature of asset price dynamics. Traditional linear econometric models and basic recurrent architectures often struggle to capture the multi-scale temporal dependencies and sudden structural shifts characteristic of modern equities markets. This research proposes a system-level investigation of a hybrid architecture integrating Long Short-Term Memory (LSTM) units with a multi-head attention mechanism to enhance stock market trend prediction and systemic risk analysis. By leveraging the sequential memory retention of LSTM layers alongside the selective weighting capabilities of the attention mechanism, the framework achieves a more nuanced interpretation of both historical price trajectories and transient volatility signals. This paper moves beyond mere predictive performance to examine the broader socio-technical implications of such systems. We conduct a thorough analysis of architectural trade-offs, particularly the tension between model depth and inference latency in high-frequency environments. Furthermore, the discussion addresses critical dimensions of algorithmic governance, the physical and environmental infrastructure required to sustain high-compute financial AI, and the policy challenges associated with model-driven market convergence. By situating the hybrid LSTM–Attention network within the context of global financial stability, this research offers a comprehensive roadmap for the deployment of robust, fair, and sustainable predictive systems in the financial sector.
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