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Detecting Earnings Management Using Neural Networks

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dc.contributor Svenska handelshögskolan, institutionen för redovisning och handelsrätt, redovisning sv
dc.contributor Hanken School of Economics, Department of Accounting and Commercial Law, Accounting en
dc.contributor.author Höglund, Henrik
dc.date.accessioned 2010-11-29T09:49:01Z fi
dc.date.accessioned 2011-03-02T13:18:57Z
dc.date.available 2010-11-29T09:49:01Z fi
dc.date.available 2011-03-02T13:18:57Z
dc.date.issued 2010-11-29
dc.identifier.isbn 978-952-232-109-1
dc.identifier.issn 0424-7256
dc.identifier.uri http://hdl.handle.net/10227/742
dc.identifier.uri URN:ISBN:978-952-232-109-1
dc.description.abstract Detecting Earnings Management Using Neural Networks. Trying to balance between relevant and reliable accounting data, generally accepted accounting principles (GAAP) allow, to some extent, the company management to use their judgment and to make subjective assessments when preparing financial statements. The opportunistic use of the discretion in financial reporting is called earnings management. There have been a considerable number of suggestions of methods for detecting accrual based earnings management. A majority of these methods are based on linear regression. The problem with using linear regression is that a linear relationship between the dependent variable and the independent variables must be assumed. However, previous research has shown that the relationship between accruals and some of the explanatory variables, such as company performance, is non-linear. An alternative to linear regression, which can handle non-linear relationships, is neural networks. The type of neural network used in this study is the feed-forward back-propagation neural network. Three neural network-based models are compared with four commonly used linear regression-based earnings management detection models. All seven models are based on the earnings management detection model presented by Jones (1991). The performance of the models is assessed in three steps. First, a random data set of companies is used. Second, the discretionary accruals from the random data set are ranked according to six different variables. The discretionary accruals in the highest and lowest quartiles for these six variables are then compared. Third, a data set containing simulated earnings management is used. Both expense and revenue manipulation ranging between -5% and 5% of lagged total assets is simulated. Furthermore, two neural network-based models and two linear regression-based models are used with a data set containing financial statement data from 110 failed companies. Overall, the results show that the linear regression-based models, except for the model using a piecewise linear approach, produce biased estimates of discretionary accruals. The neural network-based model with the original Jones model variables and the neural network-based model augmented with ROA as an independent variable, however, perform well in all three steps. Especially in the second step, where the highest and lowest quartiles of ranked discretionary accruals are examined, the neural network-based model augmented with ROA as an independent variable outperforms the other models. fi
dc.language.iso en
dc.publisher Svenska handelshögskolan sv
dc.publisher Swedish School of Economics and Business Administration en
dc.relation.ispartofseries Economics and Society
dc.relation.ispartofseries 221
dc.rights Publikationen är skyddad av upphovsrätten. Den får läsas och skrivas ut för personligt bruk. Användning i kommersiellt syfte är förbjuden. sv
dc.rights This publication is copyrighted. You may download, display and print it for Your own personal use. Commercial use is prohibited. en
dc.rights Julkaisu on tekijänoikeussäännösten alainen. Teosta voi lukea ja tulostaa henkilökohtaista käyttöä varten. Käyttö kaupallisiin tarkoituksiin on kielletty. fi
dc.subject earnings management fi
dc.subject neural networks fi
dc.subject discretionary accruals fi
dc.subject.other Accounting fi
dc.title Detecting Earnings Management Using Neural Networks fi
dc.type.ontasot Doctoral thesis en
dc.type.ontasot Väitöskirja fi
dc.type.ontasot Doktorsavhandling sv
dc.type.dcmitype Text
dc.date.accepted 2010-12-16

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