A Tone Analysis of the Non-Financial Disclosure in the Automotive Industry

Valentina Beretta · Chiara Demartini · Laura Lico · Sara Trucco

33 citations0/0 files storedSustainability

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Corporate Social Responsibility Reporting Auditing, Earnings Management, Governance Financial Reporting and XBRL

Abstract

This study’s purpose is twofold. On the one hand, it analyzes the relationship between the profitability of firms and the tone of nonfinancial disclosures; on the other hand, it tests the relationship between the environmental, social, and governing (ESG) performance of firms and the tone of nonfinancial disclosures on the automotive sector under two different and competing approaches, which are incremental information and impression management. The sample is composed of 68 nonfinancial reports issued by 17 automotive organizations between the years 2016 and 2020. Data analysis proceeded in two stages. First, a content analysis was performed to assess the linguistic attributes of the nonfinancial disclosure. Second, an inferential regression analysis was performed to test the hypothesized associations between firms’ performance and tone of their disclosures. The results of this study are aimed at providing evidence of the determinants of the verbal tone in the corporate nonfinancial reporting in a specific industry.

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References

2011When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10‐Ks 2010Voluntary Nonfinancial Disclosure and the Cost of Equity Capital: The Initiation of Corporate Social Responsibility Reporting 2008Annual report readability, current earnings, and earnings persistence 1994The Politics of Stakeholder Theory: Some Future Directions 2001Essays on disclosure 2003The relations among environmental disclosure, environmental performance, and economic performance: a simultaneous equations approach 2013Determinants of sustainability reporting: a review of results, trends, theory, and opportunities in an expanding field of research 2010The Information Content of Forward‐Looking Statements in Corporate Filings—A Naïve Bayesian Machine Learning Approach

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