Using accounting‐based information on young firms to predict bankruptcy

Lohmann, Christian; Ohliger, Thorsten

doi:10.1002/for.2586

Cited by 4 publications

(2 citation statements)

References 63 publications

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“…Most corporate failure models are designed with financial ratios, in the form of static (i.e., calculated for a particular year) (Farooq & Qamar, 2019;Lohmann & Ohliger, 2019) or dynamic (i.e., variation over several years) (Heo & Yang, 2014;Shin, Lee, & Kim, 2005) indicators. These variables are objective measures based on the publicly available information (Micha, 1984) and have achieved a dominant position as predictors.…”

Section: Literature Reviewmentioning

confidence: 99%

Human resources and corporate failure prediction modeling: Evidence from Belgium

Brédart

Séverin

Veganzones³

2021

Journal of Forecasting

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This paper analyzes the prediction performance of human resources (HR) variables in corporate failure modeling. We define corporate failure as a two-phase process from financial distress to bankruptcy, so that we can determine the prediction power of HR variables along a firm's phase in the financial deterioration process. We demonstrate the use of HR variables and their application to a two-phase corporate failure model, providing first evidence for the predictive power of HR variables. The experimental results, based on realworld datasets from Belgium, show that HR variables used in conjugation with accounting-based information improve the accuracy of prediction modeling. However, the predictive power of HR variables varies in different phases of corporate failure with better prediction accuracy during the initial symptoms of corporate failure (i.e., financial distress). Findings show that our proposed model predicted financial distress with 84.1%, whereas the accuracy decreased to 83.3% when predicting bankruptcy. Besides, they also show that, on average, the inclusion of HR variables improves the global accuracy of the prediction models of 3.8% and allows to decrease Type I error of 5%.

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Section: Literature Reviewmentioning

confidence: 99%

Human resources and corporate failure prediction modeling: Evidence from Belgium

Brédart

Séverin

Veganzones³

2021

Journal of Forecasting

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“…Accordingly, much attention has been paid to financial distress on both academic and practical ends. A vast majority of financial distress prediction (FDP) literature can be classified into statistical and machine learning models that use accounting and market ratios as predictors (Altman, 1968; Barboza et al , 2017; Campbell et al , 2008; Hillegeist et al , 2004; Lohmann and Ohliger, 2019; Ohlson, 1980; Shumway, 2001; Taffler, 1983; Wang, 2017; Zmijweski, 1984). Several studies have explained the relationship between corporate governance and financial distress (Abdullah, 2006; Chaganti et al , 1985; Daily and Dalton, 1994; Darrat et al , 2014; Elloumi and Gueyié, 2001; Fich and Slezak, 2008; Lajili and Zéghal, 2010; Lee and Yeh, 2004; Li et al , 2008; Muranda, 2006; Parker et al , 2002; Shahwan, 2015; Udin et al , 2017).…”

Section: Introductionmentioning

confidence: 99%

Can board diversity predict the risk of financial distress?

Yousaf

Jebran

Wang

2021

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Purpose The purpose of this study is to explore whether different board diversity attributes (corporate governance aspect) can be used to predict financial distress. This study also aims to identify what type of prediction models are more applicable to capture board diversity along with conventional predictors. Design/methodology/approach This study used Chinese A-listed companies during 2007–2016. Board diversity dimensions of gender, age, education, expertise and independence are categorized into three broad categories; relation-oriented diversity (age and gender), task-oriented diversity (expertise and education) and structural diversity (independence). The data is divided into test and validation sets. Six statistical and machine learning models that included logistic regression, dynamic hazard, K-nearest neighbor, random forest (RF), bagging and boosting were compared on Type I errors, Type II errors, accuracy and area under the curve. Findings The results indicate that board diversity attributes can significantly predict the financial distress of firms. Overall, the machine learning models perform better and the best model in terms of Type I error and accuracy is RF. Practical implications This study not only highlights symptoms but also causes of financial distress, which are deeply rooted in weak corporate governance. The result of the study can be used in future credit risk assessment by incorporating board diversity attributes. The study has implications for academicians, practitioners and nomination committees. Originality/value To the best of the authors’ knowledge, this study is the first to comprehensively investigate how different attributes of diversity can predict financial distress in Chinese firms. Further, this study also explores, which financial distress prediction models can show better predictive power.

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