000164005 001__ 164005 000164005 005__ 20251121161350.0 000164005 0247_ $$2doi$$a10.1016/j.emj.2018.10.006 000164005 0248_ $$2sideral$$a116668 000164005 037__ $$aART-2019-116668 000164005 041__ $$aeng 000164005 100__ $$0(orcid)0000-0003-2596-9638$$aSerrano-Cinca, Carlos$$uUniversidad de Zaragoza 000164005 245__ $$aThe use of accounting anomalies indicators to predict business failure 000164005 260__ $$c2019 000164005 5060_ $$aAccess copy available to the general public$$fUnrestricted 000164005 5203_ $$aMost of the studies that try to predict business failure assume that accounts give a true and fair view of the financial position of a company, without considering that managers can discretionarily apply accounting rules or even perform accounting fraud. This paper takes a set of financial ratios especially designed to detect accounting anomalies as bankruptcy predictors. These ratios are not very common in bankruptcy prediction studies, but they come from creative accounting studies. The ratios try to identify abnormal depreciation figures, exaggerated receivables or deteriorating financial conditions preceding aggressive accounting practices. The empirical study has been performed from a sample of 51, 337 public and private European companies, during the period 2012–2016. The analysis techniques applied were logistic regression and decision trees, allowing to obtain rules to predict the status of failed or non-failed. It is found that several indicators proposed in the literature as earnings management indicators present statistically significant differences between failed and non-failed firms, but they do not have enough predictive power to incorporate them into prediction models. However, an index developed to measure accounting anomalies exhibits high discriminatory power, similar to that of the classical financial ratios. The construction of the index and its application to private firm sample provide the main contribution of the paper, as the results suggest slightly better forecast accuracy only for the private firm sample. The inclusion of indicators to detect accounting anomalies should be considered when developing new models to predict bankruptcy, especially in private companies. 000164005 536__ $$9info:eu-repo/grantAgreement/ES/DGA/S38-17R$$9info:eu-repo/grantAgreement/ES/MINECO/ECO2016-74920-C2-1-R 000164005 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.es 000164005 590__ $$a2.369$$b2019 000164005 591__ $$aMANAGEMENT$$b118 / 226 = 0.522$$c2019$$dQ3$$eT2 000164005 591__ $$aBUSINESS$$b85 / 152 = 0.559$$c2019$$dQ3$$eT2 000164005 592__ $$a1.308$$b2019 000164005 593__ $$aStrategy and Management$$c2019$$dQ1 000164005 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion 000164005 700__ $$0(orcid)0000-0003-3044-4983$$aGutiérrez-Nieto, Begoña$$uUniversidad de Zaragoza 000164005 700__ $$aBernate-Valbuena, Martha 000164005 7102_ $$14002$$2230$$aUniversidad de Zaragoza$$bDpto. Contabilidad y Finanzas$$cÁrea Economía Finan. y Contab. 000164005 773__ $$g37, 3 (2019), 353-375$$pEUROPEAN MANAGEMENT JOURNAL$$tEUROPEAN MANAGEMENT JOURNAL$$x0263-2373 000164005 8564_ $$s3860254$$uhttps://zaguan.unizar.es/record/164005/files/texto_completo.pdf$$yPostprint 000164005 8564_ $$s926861$$uhttps://zaguan.unizar.es/record/164005/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint 000164005 909CO $$ooai:zaguan.unizar.es:164005$$particulos$$pdriver 000164005 951__ $$a2025-11-21-14:24:34 000164005 980__ $$aARTICLE