Explainable Machine Learning for Predicting Stock Price Volatility from Corporate Risk Disclosures
Keywords:
explainable machine learning; volatility forecasting; corporate risk disclosures; financial text analysis; model governance; algorithmic accountabilityAbstract
Predicting stock price volatility from corporate risk disclosures presents a complex socio-technical problem that sits at the intersection of financial economics, natural language processing, systems engineering, and regulatory governance. This paper develops a systems-level examination of explainable machine learning architectures designed to forecast volatility using qualitative risk factor narratives in corporate filings. Rather than proposing a single algorithmic solution, the analysis focuses on structural trade-offs across data ingestion, textual representation, volatility target selection, predictive modeling, explanation generation, and audit infrastructure. The discussion emphasizes that explainability in financial machine learning must operate across multiple institutional audiences, including model developers, risk managers, regulators, and investors. It further examines how semantic anomaly detection in risk disclosures can function as a governance safeguard to flag structural deviations that may undermine model confidence. The paper addresses robustness under concept drift, sustainability of large language model pipelines, fairness across firms and sectors, and policy implications for algorithmic accountability. By integrating perspectives from accounting, finance, machine learning, and regulatory science, the paper offers a forward-looking framework for designing, deploying, and governing explainable volatility prediction systems that balance predictive performance with institutional legitimacy, auditability, and operational resilience.
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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.
