Initial stage clustering when estimating accounting quality measures with self-organizing maps

Haga, Jesper; Siekkinen, Jimi; Sundvik, Dennis

doi:10.1016/j.eswa.2015.06.049

Cited by 9 publications

(7 citation statements)

References 30 publications

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“…Furthermore, although prior studies have applied the industry cross-sectional models to examine real earnings management, Ecker et al (2013) and Haga et al (2015) show that using this grouping method is subject to sample attrition and may result in biased samples. Therefore, we decide not to use alternative groupings of accrual and real manipulation models to compare our results to prior work that predominantly use the industry grouping methodology.…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, although prior studies have applied the industry cross-sectional models to examine REM, Ecker et al. (2013) and Haga et al. (2015) show that using this grouping method is subject to sample attrition and may result in biased samples.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, we focus on the UK context rather than conduct a cross-country analysis to avoid confounding effects of financial reporting or legislative environment differences; therefore, our results may not be generalizable to other contexts. Furthermore, although prior studies have applied the industry cross-sectional models to examine REM, Ecker et al (2013) and Haga et al (2015) show that using this grouping method is subject to sample attrition and may result in biased samples. Therefore, we decide not to use alternative groupings of accrual and real manipulation models to compare our results to prior work that predominantly use the industry grouping methodology.…”

Section: Detecting Earnings Managementmentioning

confidence: 99%

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Detecting earnings management: a comparison of accrual and real earnings manipulation models

Nguyen

Ibrahim

Giannopoulos

2022

JAAR

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PurposeThe use of models for detecting earnings management in the academic literature, using accrual and real manipulation, is commonplace. The purpose of the current study is to compare the power of these models in a United Kingdom (UK) sample of 19,424 firm-year observations during the period 1991–2018. The authors include artificially-induced manipulation of revenues and expenses between zero and ten percent of total assets to random samples of 500 firm-year observations within the full sample. The authors use two alternative samples, one with no reversal of manipulation (sample 1) and one with reversal in the following year (sample 2).Design/methodology/approachThe authors include artificially induced manipulation of revenues and expenses between zero and ten percent of total assets to random samples of 500 firm-year observations within the full sample.FindingsThe authors find that real earnings manipulation models have lower power than accrual earnings manipulation models, when manipulating discretionary expenses and revenues. Furthermore, the real earnings manipulation model to detect overproduction has high misspecification, resulting in artificially inflating the power of the model. The authors examine an alternative model to detect discretionary expense manipulation that generates higher power than the Roychowdhury (2006) model. Modified real manipulation models (Srivastava, 2019) are used as robustness and the authors find these to be more misspecified in some cases but less in others. The authors extend the analysis to a setting in which earnings management is known to occur, i.e. around benchmark-beating and find consistent evidence of accrual and some forms of real manipulation in this sample using all models examined.Research limitations/implicationsThis study contributes to the literature by providing evidence of misspecification of currently used models to detect real accounts manipulation.Practical implicationsBased on the findings, the authors recommend caution in interpreting any findings when using these models in future research.Originality/valueThe findings address the earnings management literature, guided by the agency theory.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Detecting Earnings Managementmentioning

confidence: 99%

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Detecting earnings management: a comparison of accrual and real earnings manipulation models

Nguyen

Ibrahim

Giannopoulos

2022

JAAR

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“…They use cluster analysis to help the auditors focus their efforts when evaluating group life insurance claims. Haga, Siekkinen, & Sundvik (2015) are investigating the power of self-organizing map local regression-based estimation models in accounting. These models estimate the abnormal components of operating activities and financial reporting.…”

Section: Application Of Data Mining In the Accounting Domainmentioning

confidence: 99%

Assessment of the Accounting Regulations in the Republic of Serbia: Application of Cluster Analysis

2020

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The focus of the paper is on identification of hidden patterns in the opinion of accounting professionals, in order to identify the deficiencies in the legal accounting regulations and to examine their perspective on international and legal accounting regulations. The data collected through the questionnaire was divided using clustering data mining technique. We identified different groups of accountants with specific patterns regarding the expressed attitudes towards both regulations, as well as different groups of accountants with specific patterns regarding the way of obtaining information. The findings point to the prevailing negative attitude towards the legal accounting regulations quality, and differences in the habits and attitudes of certified and non-certified accountants, i.e. accountants who (don’t) use International Financial Reporting Standards. Since there are four developed models, we used them as a basis for proposing a model of information seeking that could mitigate negative views on accounting regulations in the national economy. The key points of the proposed model are the introduction of compulsory continuous professional education and certification of all accountants, as well as activities for the popularization and accessibility of the international accounting regulations.

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“…A SOM is an unsupervised neural network that maps a highly dimensional dataset into a lower-dimensional representation ( Kohonen, 2012 ). SOMs are commonly used in clustering ( Isa et al, 2009 ; Haga et al, 2015 ; Nilashi et al, 2020 ), classification ( Yorek et al, 2016 ; Jain et al, 2018 ) and oversampling ( Douzas and Bacao, 2017 ) applications. For its notable success in these applications, we propose a SOM based method for clustering and classifying bank accounts into risk levels that can be further investigated by the bank.…”

Section: Introductionmentioning

confidence: 99%

Self-Organising Map Based Framework for Investigating Accounts Suspected of Money Laundering

Alshantti

Rasheed

2021

Front. Artif. Intell.

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There has been an emerging interest by financial institutions to develop advanced systems that can help enhance their anti-money laundering (AML) programmes. In this study, we present a self-organising map (SOM) based approach to predict which bank accounts are possibly involved in money laundering cases, given their financial transaction histories. Our method takes advantage of the competitive and adaptive properties of SOM to represent the accounts in a lower-dimensional space. Subsequently, categorising the SOM and the accounts into money laundering risk levels and proposing investigative strategies enables us to measure the classification performance. Our results indicate that our framework is well capable of identifying suspicious accounts already investigated by our partner bank, using both proposed investigation strategies. We further validate our model by analysing the performance when modifying different parameters in our dataset.

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Initial stage clustering when estimating accounting quality measures with self-organizing maps

Cited by 9 publications

References 30 publications

Detecting earnings management: a comparison of accrual and real earnings manipulation models

Detecting earnings management: a comparison of accrual and real earnings manipulation models

Assessment of the Accounting Regulations in the Republic of Serbia: Application of Cluster Analysis

Self-Organising Map Based Framework for Investigating Accounts Suspected of Money Laundering

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