Large Language Model-Based Analysis of Corporate Risk Narratives for Investment Decision-Making
Keywords:
large language models; corporate risk narratives; investment decision-making; financial text analysis; governance; explainable artificial intelligence; systemic riskAbstract
The increasing availability of unstructured corporate disclosures has created both opportunities and challenges for investment decision-making. This paper presents a system-level examination of large language model-based analysis of corporate risk narratives, focusing on architectural trade-offs, governance mechanisms, deployment infrastructure, robustness, fairness, and policy implications. Traditional textual analysis methods rely on lexicons and shallow classifiers, but recent advances in transfer learning and generative language models enable more context-sensitive interpretation of risk factors, management discussion, and forward-looking statements. The paper argues that effective deployment requires more than predictive accuracy: it demands careful corpus construction, continuous monitoring, explainable decision support, and alignment with regulatory expectations. It discusses how these models can be integrated into investment workflows without displacing human judgment, and it identifies structural risks including distributional drift, semantic manipulation, opacity, and systemic homogeneity. Through cross-domain comparisons with credit risk, regulatory supervision, and medical decision support, the paper highlights the need for institutional governance, auditability, and sustainability. The discussion extends to emerging policy questions such as disclosure integrity, model assurance, and market efficiency. The paper concludes that large language models should be treated as socio-technical infrastructural components whose value depends on organizational oversight, incentive alignment, and the capacity to detect anomalies in narrative structures rather than on isolated performance benchmarks.
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