Predicting Earnings Management from Qualitative Disclosures

Munich Risk and Insurance Center Working Paper 40

67 Pages Posted: 24 Jan 2021 Last revised: 25 Jan 2021

See all articles by Johannes Gerd Jaspersen

Johannes Gerd Jaspersen

Ludwig Maximilian University of Munich (LMU) - Faculty of Business Administration (Munich School of Management)

Andreas Richter

Ludwig Maximilian University of Munich (LMU) - Faculty of Business Administration (Munich School of Management)

Sandra Zoller

Ludwig Maximilian University of Munich (LMU) - Faculty of Business Administration (Munich School of Management)

Date Written: January 1, 2021

Abstract

While analysts, customers, and lenders rely on financial disclosures to make decisions regarding a company, executives often manage the disclosed earnings. Detecting such practices is thus a concern for company stakeholders and regulators. Qualitative disclosures are an additional source of information about a company's financial situation, but executives likely attempt to hide their earnings management activity in these disclosures, as well. We use supervised machine learning models to predict earnings management by property and casualty insurers from the Management’s Discussion and Analysis filings. For this, we utilize a new algorithm that interprets textual data conditional on the reported financial situation of the company. We show that the qualitative disclosures can predict earnings management, revealing that executives are unable to remove all subliminal messages from them. The results demonstrate that qualitative disclosures can be useful for learning about the accounting choices of companies.

Keywords: Machine learning, Earnings management, Reserve error, Disclosure

JEL Classification: C38, C53, G22, M41

Suggested Citation

Jaspersen, Johannes Gerd and Richter, Andreas and Zoller, Sandra, Predicting Earnings Management from Qualitative Disclosures (January 1, 2021). Munich Risk and Insurance Center Working Paper 40, Available at SSRN: https://ssrn.com/abstract=3732203 or http://dx.doi.org/10.2139/ssrn.3732203

Johannes Gerd Jaspersen

Ludwig Maximilian University of Munich (LMU) - Faculty of Business Administration (Munich School of Management) ( email )

Schackstr. 4
Munich, DE 80539
Germany

Andreas Richter

Ludwig Maximilian University of Munich (LMU) - Faculty of Business Administration (Munich School of Management) ( email )

Schackstraße 4
Munich, 80539
Germany

Sandra Zoller (Contact Author)

Ludwig Maximilian University of Munich (LMU) - Faculty of Business Administration (Munich School of Management) ( email )

Schackstr. 4
Munich, DE Bavaria 80539
Germany

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