Quantifying 'Grey-Zone' Disclosure: A Multi-Construct Model for Measuring Algorithmic ESG Metric Manipulation and its Impact on Institutional Investor Capital Allocation

• Asher Noah

  covenant University

  Author

The rapid proliferation of Environmental, Social, and Governance (ESG) investing has created perverse incentives for corporate ESG metric manipulation, yet existing detection frameworks remain fragmented and lack predictive validity for institutional capital allocation outcomes. This study addresses this gap by developing and validating a multi-construct model that quantifies algorithmic ESG metric manipulation through a hybrid detection framework combining natural language processing, anomaly detection, and multi-agent verification. The study employs a quantitative, design-based research methodology analyzing 5,031 corporate ESG reports across 26 industries alongside institutional investment flow data from 2018-2025. The proposed framework achieves 89.4% accuracy in detecting manipulation patterns, outperforming traditional methods by 23.7 percentage points, and establishes a significant negative correlation (r = -0.71, p < 0.001) between manipulation scores and subsequent institutional capital allocation. The findings demonstrate that algorithmic detection of "grey-zone" disclosure practices provides institutional investors with a replicable, data-driven tool for enhancing due diligence, while offering regulators a framework for semantic supervision of ESG claims. This research contributes to corporate disclosure theory by operationalizing the construct of algorithmic ESG metric manipulation and provides practical mechanisms for restoring transparency in sustainable finance markets.

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Copyright (c) 2026 Asher Noah (Author)

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