The effect of transparency in ESG disclosure on firm performance: How can it influence investor perception?

(2026)

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Abstract
This Master’s thesis investigates the impact of corporate transparency, as captured by the linguistic characteristics of voluntary ESG disclosures, on short-term investor reactions, measured via cumulative abnormal returns (CAR). Using natural language processing (NLP), we extract four textual dimensions, namely linguistic clarity, temporal inconsistency, social inclusivity, and informational specificity, from a sample of 3230 observations of listed U.S. companies ESG reports between 2016 and 2023. While these attributes show no uniform directional response across the broad market due to institutional standardization, conditional industry analyses reveal significant localized pricing dynamics. Corporate narrative overhauls (inconsistency) are valued positively in the retail sector, signaling strategic agility, whereas high factual specificity is rewarded in the supply-chain- sensitive food industry. Furthermore, an analysis of absolute abnormal returns demonstrates that stakeholder inclusivity acts as a volatility-dampening mechanism, reducing market friction primarily within the services sector during the post-2020 era. Ultimately, this research demonstrates that corporate transparency should not be analyzed as a uniform market-wide construct. Instead, it must be evaluated through an industry-specific lens, since investors value narrative characteristics based on the distinct economic and operational realities of each sector.