Rebuilding a Shattered Reputation: Financial Reporting Quality and Donations Following Corporate Controversies

Vasilescu, Camelia; Wisniewski, Tomasz Piotr

doi:10.2139/ssrn.3484818

Cited by 1 publication

(1 citation statement)

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“…Despite the fact that the controversies score finds its application within other financial research [see, for example, Park (2018) and Vasilescu and Wisniewski (2019)], we still contribute to the literature as we are the first ones to consider the extreme event of an ESG-based scandal within the context of portfolio selection.…”

Section: Introductionmentioning

confidence: 99%

ESG controversies and controversial ESG: about silent saints and small sinners

2020

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Based on an extensive international dataset containing Thomson Reuters environmental, social and corporate governance (ESG) rating, as well as Thomson Reuters newest controversies and combined score of an average of 2500 companies in the years 2002-2018, this article contributes to the existing discourse of the relationship between corporate social performance and corporate financial performance (CFP) by examining the Fama and French (J Financ Econ 116(1):1-22, 2015) five-factor risk-adjusted performance of positive screened best and worst portfolios, based on a 10% cutoff, respectively, for equally, value-and rank-weighted strategies in the European, US and global market. Furthermore, the controversies score allows us to examine the mid-to-long-term effects of scandals on the CFP without having to rely on the event study methodology. Even though a value-weighted strategy does not show any significant abnormal returns, we examined a significant outperformance for equally weighted worst ESG portfolios and best controversies strategies. These results strongly indicate that this is, on the one hand, driven by low-rated smaller companies ("small sinners") and clean-coated firms with regard to controversies ("silent saints") on the other hand. The findings hold for several robustness checks such as adjusting the cutoff rates or splitting the dataset across time.

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Section: Introductionmentioning

confidence: 99%