Welc 2017 EBITDA vs. Cash Flows in Bankruptcy Prediction on the Polish Capital Market.
The study evaluated the accuracy of bankruptcy predictions generated by EBITDA-based and cash flow-based liabilities-coverage ratios on a sample of firms firms listed on the Warsaw Stock Exchange, from the Polish market.
The study found that the logit models with only one ratio used as an explanatory variable is capable of identifying bankrupt firms (with one-period-ahead forecast horizon) in about 66-76%
of cases.
Babatunde, Akeju, and Malomo
2017 The effectiveness of Altman‟s z-score in predicting bankruptcy of quoted manufacturing companies in Nigeria.
Evaluated the effectiveness of Altman‟s z-score in predicting bankruptcy of quoted manufacturing companies in Nigeria. The sample comprised 10 manufacturing companies quoted on the Nigeria Stock Exchange (NSE) for 2015 financial year.
The study proves the effectiveness of the Z-score model in identifying companies with deteriorating performance in Nigeria.
Mihalovič 2016 Performance Comparison of Multiple Discriminant Analysis and Logit Models in Bankruptcy Prediction.
Compared performance of multiple discriminant analysis and logit models in bankruptcy prediction in Slovak Republic.
The results showed that the logit model outperformed the classification accuracy of the discriminant model. The discriminant analysis had a total accuracy of 64.41% on the test data and the logit analysis had a total of 68.64% on the test data.
Slefendorfas 2016 Bankruptcy prediction model for private limited
companies of Lithuania.
Developed a bankruptcy prediction model for private limited companies of Lithuania. 145 companies (73 already bankrupt and 72 still operating) were chosen as sample and by using multivariate discriminant analysis stepwise method a linear function was created. 156 different financial ratios were selected as a primary input data by using correlation calculation between bankruptcy and still operating companies and Mann – Whitney U test techniques.
The results showed that 89% of companies were classified correctly, which states that the model is strong enough to predict bankruptcy probability for private limited companies operating in Lithuania in a sufficient accuracy.
Situm 2015 The Relevance of Trend Variables for the Prediction of Corporate Crises and Insolvencies.
Investigated the potential of a specific trend, to improve the classification accuracy and model performance of insolvency prediction models based on multivariate linear discriminant analysis.
The results showed 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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Adeyeye and Migiro
2015 An investigation on Nigerian banks‟ status using early-warning signal.
Developed an integrated early warning signal which utilises the PCA with three statistical models DA, logit and probit models to determine the health status of Nigerian banks. The sample comprised 21 banks out of the total 24 Deposit Money Banks (DMBs) quoted on the Nigerian Stock Exchange over a 23 year-period from 1993 to 2010.
The results show that discriminant analysis (95.2), logit (90.24) and probit (89.02) models are credible predictors of a bank‟s financial status.
Brédart 2014a Bankruptcy prediction model: The case of the United States.
The study used logit regression to forecast corporate bankruptcy among US companies. The sample used in the study consisted of 870 firms originally quoted on the Amex, the Nasdaq and the NYSE from January 2000 to December 2012. The study used a matched-pair sample of US quoted firms with half of the sample filing for chapter 11 (reorganization procedure) of the United States Bankruptcy Code and conducted logit regression analysis.
The results showed that profitability had a negative impact on financial distress probability, a negative relationship between liquidity and the probability to file for a bankruptcy law, and solvency had a
negative impact on financial distress probability. The overall prediction accuracy of the model is 83.82%.
Mosionek-Schweda (2014)
2014 The Use of Discriminant Analysis to Predict the Bankruptcy of Companies Listed on the NewConnect Market.
The analysis was based on three models: Altman's model for emerging markets, and two other models based on P.
Antonowicz's research, Z7 INEPAN model developed in the Polish Academy of Sciences and E. Mączyńska's model, developed by Polish scientists and adapted to the Polish economy.
The results confirmed that these models are a valuable tool in assessing the financial condition of enterprises and allow for bankruptcy forecasting.
Bartual, Garcia, Guijarro, and Moya
2013 Default pre diction of Spanish companies. A logistic analysis.
Developed a model to predict corporate default for Spanish manufacturing companies applying logistic regression. They selected 2,783 companies, of which 736 were identified as insolvent (26.5% of the sample). The variables employed in the study were obtained from the balance sheets and the income statements of the companies.
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Lundqvist and Strand
2013 Bankruptcy Prediction with Financial Ratios-Examining Differences across Industries and Time.
Bankruptcy prediction models were estimated using logistic regression for each year, with and without interaction terms accounting for industry effects.
The study shows that the bankruptcy-prediction ability of different financial ratios varies between years. However, only in some cases, significant differences between industries were found.
Adeyeye and Oloyede
2014 Forecasting Bank Failure in Nigeria: An
Application of Enhanced Discriminant Model.
They combined principal component analysis (CPA) and discriminant analysis (DA) to estimate bankruptcy. The data set of the analysis contains 11 bank-specific variables of 21 banks out of the 24 banks operating as deposit money banks in Nigeria between 2007 and 2009.
The discriminant model correctly predicted the financial status of about 20 banks out of 21 sampled banks respectively. The model accurately predicted the status of 6 banks out of 7 failed banks included in the model. Even the one not correctly predicted was appropriately identified as misclassified.
Unegbu and Adefila
2013 Efficacy Assessments of Z-Score and Operating Cash Flow Insolvency Predictive Models.
They examined and compared the efficacy of Z-Score and operating cash flow as corporate insolvency prediction models. The tools of analyses employed are ANOVA, Loglinear Analysis, Fredman ANOVA and Percentages.
Z-Score predictive ability across Services and Merchandising sectors is found to be very poor but very strong on Manufacturing and Oil Services, while Operating Cash Flow model is found to be more effective in predicting accurately Service and
Merchandising Sectors. The predictive efficacy of the two models significantly varies as the year becomes closer to the year of corporate failure.
Pam 2013 Discriminant Analysis and the Prediction of Corporate Bankruptcy in the Banking Sector of Nigeria.
Investigated the potency of Multiple Discriminant Analysis Model (propounded by Altman, 1968) in ascertaining the state of health of banks in Nigeria.
The sample of the study comprised two „failed‟ and two non-failed banks within a five year period (1999-2003).
The study found that the Z Scores of the two non-failed banks were found to be below 1.80 indicating ill-health.
Serrano-Cinca and
GutiéRrez-Nieto
2013 Partial least square discriminant analysis for bankruptcy prediction.
Used Partial Least Square Discriminant Analysis (PLS-DA) for the prediction of the 2008 USA banking crisis. They compared the performance of this technique to the performance of 8 algorithms widely used in bankruptcy prediction.
The PLS-DA results obtained were very close to those obtained by Linear Discriminant Analysis and Support Vector Machine. In terms of accuracy, precision, F-score, Type I error and Type II error, results are similar; no algorithm outperforms the others.
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Islam, Semeen, and Farah
2013 The Effects of Financial Ratios on Bankruptcy.
They studied 24 ratios listed under the title of liquidity, financial solvency, activity and profitability. The data were subjected to analysis using discriminant analysis.
The results of the study demonstrate that each ratio (variable) has a significant effect on the financial positions of enterprises with differing amounts Zaghdoudi 2013 Bank failure prediction with
logistic regression.
Developed a predictive model of Tunisian bank failures using binary logistic regression method. The specificity of our prediction model is that it takes into account microeconomic indicators of bank failures.
The results showed that a bank's ability to repay its debt, the coefficient of banking operations, bank profitability per employee and leverage financial ratio has a negative impact on the probability of failure.
Ani and
Ugwunta
2012 Predicting Corporate Business Failure in the Nigerian
Manufacturing Industry.
Employed discriminant analysis in predicting business failure in Nigeria. They employed secondary data for a five year period from eleven firms in the manufacturing, oil marketing and conglomerate sector of the Nigerian Stock Exchange.
The result revealed that multi discriminant analysis is a veritable tool for assessing the financial health of firms in Nigeria.
Ahmadi, Soleimani, Vaghfi, and Salimi
2012 Corporate bankruptcy prediction using a logit model: Evidence from listed companies of Iran.
Attempt to predict corporate bankruptcy prediction using Logit model. They used 19 financial ratios.
The results showed that the Logit model with variables of net profit to total assets ratio, the ratio of retained earnings to total assets and debt ratio have more power to predict corporate bankruptcy in Iran.
Han, Kang, Kim, and Yi
2012 Logit regression based bankruptcy prediction of Korean firms.
They developed a bankruptcy prediction model for Korean firms using logit regression. They also include equity market inputs and macro-economic variables.
They compared the model with a Merton-type structural model and find that the model demonstrates a higher prediction power in distinguishing distressed firms from healthy firms.
Hassani and Parsadmehr
2012 The presentation of financial crisis forecast pattern (Evidence from Tehran Stock Exchange).
Used logit model on firms in Tehran Stock Exchange.
Variables were obtained from literature and Article 141 of the Commerce Law.
The results indicated that using the test data, the forecast strength of the model is 81.49%, its degree of sensitivity is 96.12% and its degree of
identification is 67.48%.
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Hauser and Booth
2011 Predicting bankruptcy with robust logistic regression.
They used data from 2006 and 2007, and a three-fold cross validation scheme to compare the classification and prediction of bankrupt firms using the Bianco and Yohai (BY) estimator versus maximum likelihood (ML) logistic regression.
The provide evidence to support the BY robust logistic regression for improved classification of bankrupt firms.
Wang and Campbell
2010 Business failure prediction for publicly listed
companies in China.
Studied data from Chinese publicly listed companies for the period of September 2000-September 2008. They tested the accuracy of the Altman‟s Z-score model in predicting failure of the companies. They studied a total of 42 delisted firms (16 manufacturing companies) along with 42 (16 manufacturing companies) matching nondelisted firms.
All three models were found to have significant predictive ability. The re-estimated model had higher prediction accuracy for predicting non-failed firms, but Altman‟s model has higher prediction accuracy for predicting failed firms. The revised Z-score model has a higher prediction accuracy compared with both the reestimated model and Altman‟s original model.
Zhou and Elhag
2007 Apply logit analysis in bankruptcy prediction.
They employed Logit analysis with forward stepwise regression to construct predictive models. A total of 23 variables were chosen from financial statement of each sample firm in four groups.
The Logit model predicted bankruptcy, with an overall prediction accuracy of the model was 81% with cut-off point 0.7, while type I error is 92% and type II error is 70%. The t test showed that the bankrupt group had lower profit generation ability before failure, and there is a significant difference in operating efficiency ratio.
Kim and Gu 2006 A logistic regression analysis for predicting bankruptcy in the hospitality industry.
They estimated logit models for predicting bankruptcy up to 2 years in advance using financial data of 16 U.S.
hospitality firms that went bankrupt between 1999 and 2004 and 16 non-bankrupt matching firms.
The logit models, resulting from forward stepwise
selection procedures, could correctly predict 91% and 84%
of bankruptcy cases 1 and 2 years earlier, respectively.
Darayseh, Waples, and Tsoukalas
2003 Corporate failure for manufacturing industries using firms specifics and economic environment with logit analysis.
They developed a logit model for bankruptcy prediction using economic variables in combination with firm-wise financial ratios.
Their estimated model could make correct predictions for 87.82% and 89.50% of the in-sample and holdout samples for 1 year prior to bankruptcy.
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Gu 2002 Analyzing bankruptcy in the restaurant industry: A multiple discriminant model.
Developed a multiple discriminant model for analyzing US restaurant firm bankruptcy. The model achieved a 92-percent accuracy rate in classifying the in-sample firms into bankrupt and non-bankrupt groups. The jackknife cross-validation accuracy rate was 89 percent.
The ex-post classification of out-of-sample restaurants, mainly non-bankrupt firms, was 80 percent correct. The model suggests that restaurant firms with low earnings before interests and taxes and high total liabilities are more likely to be bankruptcy candidates.
Low, Nor, and Yatim
2001 Predicting corporate financial distress using the logit model: The case of Malaysia.
They developed a model using an estimation sample consisting of both distressed and non-distressed companies. They selected 11 financial ratios from prior studies.
They tested the predictive ability of the model on a holdout sample, and showed that the overall accuracy rate for the estimation and holdout samples are 82.4% and 90%
respectively.
Lennox 1999 Identifying failing companies: A re-evaluation of the logit, probit, and DA approaches.
The study compared the performance of the three models in predicting bankruptcy.
The results showed that the probit and logit models outperformed the discriminant model.
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