Tax avoidance and earnings management: a neural network approach for the largest European economies

Francisco J. Delgado (), Elena Fernández-Rodríguez (), Roberto García-Fernández, Manuel Landajo () and Antonio Martínez-Arias ()
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Francisco J. Delgado: University of Oviedo
Elena Fernández-Rodríguez: University of Oviedo
Manuel Landajo: University of Oviedo
Antonio Martínez-Arias: University of Oviedo

Financial Innovation, 2023, vol. 9, issue 1, 1-25

Abstract: Abstract In this study, we investigate the relationship between tax avoidance and earnings management in the largest five European Union economies by using artificial neural network regressions. This methodology allows us to deal with nonlinearities detected in the data, which is the principal contribution to the previous literature. We analyzed Compustat data for Germany, the United Kingdom, France, Italy, and Spain for the 2006–2015 period, focusing on discretionary accruals. We considered three tax avoidance measures, two based on the effective tax rate (ETR) and one on book-tax differences (BTD). Our results indicate the presence of nonlinear patterns and a positive, statistically significant relationship between discretionary accruals and both ETR indicators implying that when companies resort to earnings management, a larger taxable income—and thus higher ETR and lesser tax avoidance– would ensue. Hence, as also highlighted by the fact that discretionary accruals do not appear to affect BTD, our evidence does not suggest that companies are exploiting tax manipulation to reduce their tax payments; thus, the gap between accounting and taxation seems largely unaffected by earnings management.

Keywords: Tax avoidance; Earnings management; Artificial neural networks; European Union (search for similar items in EconPapers)
JEL-codes: C45 H25 H26 M41 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (3)

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DOI: 10.1186/s40854-022-00424-8

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