Financial Distress Prediction in China

Chen, Jianguo; Marshall, Ben R.; Zhang, Jenny; Ganesh, Siva

doi:10.1142/s0219091506000744

Cited by 59 publications

(27 citation statements)

References 18 publications

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“…The same conclusion can be reached for the ratios EBIT/S and ∆EBIT/S. These fi ndings confi rm the results of prior studies (Theodossiou et al, 1996;Kane et al, 1998;Kahya et al, 1999;Sudarsanam et al, 2001;Molina, 2005;Chen et al, 2006;Yeh et al 2010). The size of the fi rm replicated by ln(S) showed ability for discrimination one year prior to the event of bankruptcy.…”

Section: Interpretation Of the Resultssupporting

confidence: 86%

“…EBIT/TA, EBITDA/TA, EBIT/S, NI/TA or EBT/TE) show generally higher values for solvent fi rms. It appears that effi ciency on the operational level helps to prevent fi rms from the likelihood of failure and decreases the probability of insolvency, which is in congruence with fi ndings from prior research (Theodossiou et al, 1996, p. 715;Sudarsanam et al, 2001;Molina, 2005Molina, , p. 1436Molina, -1441Chen et al, 2006;Yeh et al, 2010;Youn et al, 2010). The associated ratios could therefore be seen as a proxy for the measurement of management effectiveness (Dambolena et al, 1980(Dambolena et al, , p. 1025.…”

Section: Descriptive Statistics and Test For Normal Distributionsupporting

confidence: 75%

“…Several studies showed that the predictions made using neural networks were superior to the ones obtained by logistic regression and discriminant analysis (Coats et al, 1993;Anandarajan et al, 2001;Charitou et al, 2004;Neves et al, 2006). In many other studies, this superiority was not confi rmed as logistic regression provided overly similar results to neural networks (Fanning et al, 1993;Sen et al, 2004;Pompe et al, 2005;Chen et al, 2006;Youn et al, 2010). These three outlined methods remain the most prominent within business failure prediction research (Du Jardin, 2009, p. 44).…”

Section: Introductionmentioning

confidence: 99%

“…Besides these, many other approaches have been tried, with only some being named here for illustrative purposes: Genetic algorithms (Anandarajan et al, 2001;McKee, 2007), recursive partitioning (Frydman et al, 1985;Chen et al, 2006;Huang et al, 2008;Muller et al, 2009), rough set theory and fuzzy logic (Dimitras et al, 1999;Ahn et al, 2000;Lin et al, 2009), support vector machines (Shin et al, 2005;Li et al, 2010;Lin et al, 2011) and option pricing models (Hillegeist et al, 2004;Tsai et al, 2012). None of these approaches clearly outperformed or were demonstrably superior to discriminant analysis, logistic regression and neural networks.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

The Relevance of Trend Variables for the Prediction of Corporate Crises and Insolvencies

Situm

2015

Zagreb International Review of Economics and Business

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Abstract:This study investigated the potential of a specifi c trend, defi ned as the relative change of accounting ratios for two consecutive years, to improve the classifi cation accuracy and model performance of insolvency prediction models based on multivariate linear discriminant analysis. The results show that the respective trend can include information from both consecutive years, but this informational content could not be exploited to improve early detection of corporate crises and insolvencies.

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Section: Interpretation Of the Resultssupporting

confidence: 86%

Section: Descriptive Statistics and Test For Normal Distributionsupporting

confidence: 75%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The Relevance of Trend Variables for the Prediction of Corporate Crises and Insolvencies

Situm

2015

Zagreb International Review of Economics and Business

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“…As expected one ratio describing the capital structure of the firm appeared as potential predictor. EBIT/TA is a measure of profitability and appeared in addition to capital structure ratio (TE/TA or TD/TA) within different previous studies (Chen et al, 2006;Pervan, Pervan and Vukoja, 2011). Profitability ratios can be used as a proxy for the measurement of management efficiency (Dambolena and Khoury, 1980;Pervan and Visic, 2012) and are therefore interesting explanatory variables.…”

Section: Hypothesis and Research Questionsmentioning

confidence: 98%

Inability of Gearing-Ratio as Predictor for Early Warning Systems

Situm

2014

Business Systems Research Journal

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Background: Research in business failure and insolvency prediction provides numerous potential variables, which are in the position to differentiate between solvent and insolvent firms. Nevertheless, not all of them have the same discriminatory power, and therefore their general applicability as crisis indicators within early warning systems seems questionable. Objectives: The paper aims to demonstrate that gearing-ratio is not an appropriate predictor for firm failures/bankruptcies. Methods/Approach: The first and the second order derivatives for the gearing-ratio formula were computed and mathematically analysed. Based on these results an interpretation was given and the suitability of gearing-ratio as a discriminator within business failure prediction models was discussed. These theoretical findings were then empirically tested using financial figures from financial statements of Austrian companies for the observation period between 2008 and 2010. Results: The theoretical assumptions showed that gearing-ratio is not a suitable predictor for early warning systems. This finding was confirmed with empirical data. Conclusions:The inclusion of gearing-ratio within business failure prediction models is not able to provide early warning signals and should therefore be ignored in future model building attempts.

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Corporate financial distress prediction in a transition economy

Nguyen,

Liêu

2024

Journal of Forecasting

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Forecasting financial distress of corporations is a difficult task in economies undergoing transition, as data are scarce and are highly imbalanced. This research tackles these difficulties by gathering reliable financial distress data in the context of a transition economy and employing the synthetic minority oversampling technique (SMOTE). The study employs seven different models, including linear discriminant analysis (LDA), logistic regression (LR), support vector machines (SVMs), neural networks (NNs), decision trees (DTs), random forests (RFs), and the Merton model, to predict financial distress among publicly traded companies in Vietnam between 2011 and 2021. The first six models use accounting‐based variables, while the Merton model utilizes market‐based variables. The findings indicate that while all models perform fairly well in predicting results for nondelisted firms, they perform somewhat poorly in predicting results for delisted firms in terms of various measures including balanced accuracy, Matthews correlation coefficient (MCC), precision, recall, and score. The study shows that the models that incorporate both Altman's and Ohlson's variables consistently outperform those that only use Altman's or Ohlson's variables in terms of balanced accuracy. Additionally, the study finds that NNs are generally the most effective models in terms of both balanced accuracy and MCC. The most important variable in Altman's variables as well as the combination of Altman's and Ohlson's variables is “reat” (retained earnings on total assets), whereas “ltat” (total liabilities on total assets) and “wcapat” (working capital on total assets) are the most important variables in Ohlson's variables. The study also reveals that in most cases, the models perform better in predicting results for big firms than for small firms and typically better than in good years than for bad years.

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Financial Distress Prediction in China

Cited by 59 publications

References 18 publications

The Relevance of Trend Variables for the Prediction of Corporate Crises and Insolvencies

The Relevance of Trend Variables for the Prediction of Corporate Crises and Insolvencies

Inability of Gearing-Ratio as Predictor for Early Warning Systems

Corporate financial distress prediction in a transition economy

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