Evaluation of the going‐concern status for companies: An ensemble framework‐based model

Hsu, Yu Feng; Lee, Wei‐Po

doi:10.1002/for.2653

Cited by 5 publications

(3 citation statements)

References 57 publications

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“…Early studies in the going-concern opinion literature used discriminant analysis to model the decision (e.g., Altman & McGough, 1974;McKee, 1976). However, because going-concern prediction cannot rely on the contradictory assumptions of a multivariate normal distribution of explanatory variables and equal covariance metrics between two groups (Carson et al, 2013), models for predicting goingconcern opinions shifted from discriminant analysis to logit analysis (e.g., Menon & Schwartz, 1987;Harris & Harris, 1990), probit analysis (e.g., Koh & Brown, 1991), neural networks (e.g., Serrano-Cinca, 1996), decision tree (Koh & Low, 2004), support vector machine (Martens et al, 2008), and also a machine-learning random forest model (e.g., Hsu & Lee, 2020). For instance, Bellovary et al (2007) identified twenty-seven statistical models developed to predict issuance of a goingconcern opinion, including multivariate discriminant analysis (MDA).…”

Section: Prediction Modelsmentioning

confidence: 99%

Decision Tree Tool for Auditors’ Going Concern Assessment in Spain

Custodio¹,

Gu²,

González³

2022

IJDAR

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The COVID-19 pandemic increased uncertainty about the financial future of many organizations, and regulators alerted auditors to be increasingly skeptical in assessing an entity’s ability to continue as a going concern. An auditor’s assessment of an entity’s ability to continue as a going concern is a matter of significant judgment. This paper proposes to use machine learning to construct a Decision Tree Automated Tool, based on both quantitative financial indicators (e.g., Z-scores) and qualitative factors (e.g., partners’ judgment and assessment of industry risk given the pandemic). Considering both quantitative and qualitative factors results in a model that provides additional audit evidence for auditors in their going-concern assessment. An auditing firm in Spain used the model as a supplemental guide, and the model’s suggested results were compared to auditors’ reports to evaluate its effectiveness and accuracy. The model’s predictions were significantly similar to the auditors’ assessments, indicating a high level of accuracy, and differences between the model’s proposed outcomes and auditors’ final conclusions were investigated. This paper also provides insights for regulators on both the use of machine-learning predictive models and additional factors to be considered in future going-concern assessment research.

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Section: Prediction Modelsmentioning

confidence: 99%

Decision Tree Tool for Auditors’ Going Concern Assessment in Spain

Custodio¹,

Gu²,

González³

2022

IJDAR

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show abstract

“…While it is comforting that auditors appear to be sensitive to indicators of distress when making going concern judgments, the effectiveness with which these factors are used and whether there are other useful predictors of distress have also been the focus of research attention. Research has developed and tested models to assist auditors when assessing an entity's viability (e.g., Koh 1991;Hsu and Lee 2020), and other studies find associations between new variables and future viability, for example, social media sentiment (Condie and Moon 2020).…”

Section: (A) Should the Auditor Have Enhanced Or More Requirements With Regard To Going Concern In An Audit Of Financial Statements? If Ymentioning

confidence: 99%

Comments by the Auditing Standards Committee of the Auditing Section of the American Accounting Association on the Discussion Paper:Fraud and Going Concern in an Audit of Financial Statements: Exploring the Differences between Public Perceptions about the Role of the Auditor and the Auditor's Responsibilities in a Financial Statement Audit

Schaefer¹,

Brown²,

Ege³

et al. 2021

Current Issues in Auditing

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We commend the International Auditing and Assurance Standards Board (the Board) on its efforts to determine “whether the auditing standards related to fraud and going concern need to be updated to reflect the rapidly evolving external reporting landscape.” We especially commend the Board on the timeliness of the call for input into these issues in that there is sufficient time to conduct research to further inform questions raised in the Discussion Paper and the questions that will inevitably arise as deliberations continue and progress is made on the project. We note below insights from the extant research literature as they relate to the questions posed in the Discussion Paper, but there remain many unanswered questions. We believe that many members of the Auditing Section of the American Accounting Association stand ready to work with the Board and other stakeholders to inform deliberations in this area.

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“…The size of a company with respect to its evaluation is addressed by Yang et al [54]; the methodology of analysis of dynamic network data packages in order to provide a comprehensive assessment (including the inclusion of CSR factors) for the insurance sector is provided by Kuo et al [55]. Hsu and Lee [56] test the Random Forest method for comprehensive audits. The same method, with declared excellent results, is chosen by Petropoulos et al [57] for assessing the economic health of financial institutions.…”

Section: Company Specific Modelmentioning

confidence: 99%

Creating a Comprehensive Method for the Evaluation of a Company

et al. 2020

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For investment purposes, the evaluation of a company is not only a matter for a company itself, but also for shareholders and external persons. There are many methods for evaluating a company. This contribution therefore focuses on the creation of a comprehensive method for the evaluation of an industrial enterprise, one that can be used to predict potential future bankruptcies, using a dataset of financial statements of active companies and those in liquidation in the period 2015–2019. Artificial neural networks were used to process the data, specifically logistic regressions from the data processed in the Statistica and Mathematica software programmes. The results showed that the models created using the Mathematica software are not applicable in practice due to the parameters of the obtained results. In contrast, the artificial neural structures obtained using the neural network model in the Statistica software were prospective due to their performance, which is almost always above 0.8, and the logical economic interpretation of the relevant variables. All the generated and retained networks show excellent performance and few errors. However, one of the artificial structures, network no. 4 (MLP 16-16-2), produces better results than the others. Overall, accuracy is almost 81%. In the case of the classification of companies capable of surviving financial distress, accuracy is almost 90%, with that for the classification of companies at risk of going into bankruptcy at nearly 55%.

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Evaluation of the going‐concern status for companies: An ensemble framework‐based model

Cited by 5 publications

References 57 publications

Decision Tree Tool for Auditors’ Going Concern Assessment in Spain

Decision Tree Tool for Auditors’ Going Concern Assessment in Spain

Creating a Comprehensive Method for the Evaluation of a Company

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