Predicting Risk of Corporate Bankruptcy: History
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Contributor:
Martina Mokrišová
,
Jarmila Horváthová

Predicting the risk of corporate bankruptcy is one of the most important challenges for researchers dealing with the issue of financial health evaluation. The risk of corporate bankruptcy is most often assessed with the use of early warning models. The results of these models are significantly influenced by the financial features entering them. 

  • data envelopment analysis
  • domain knowledge
  • feature
  • LASSO

1. Introduction

Research shows that no company can be sure of its future even in times of peace and prosperity. The problem of companies’ risk of bankruptcy is highly relevant today and is being addressed by many researchers. The acceleration in interest in its solution was caused by the events of the last few years (COVID-19, war in Ukraine), especially in Europe. It is necessary to catch earlier signals of bankruptcy, to which business managers should pay increased attention in order to prevent bankruptcy. For this purpose, various methods of selecting bankruptcy prediction features, as well as various bankruptcy prediction models, are suitable. It is proven that domain knowledge plays a significant role in the given process and, when combined with a suitable prediction method, can provide significant results. This is confirmed by the studies of several authors. It is possible to mention the studies of Veganzones and Severin (2021), who selected features based on their popularity in the prior literature, the study of Min and Lee (2008), who used expert opinion, or the study of Zhou et al. (2015), who applied domain knowledge approach. Often used features in bankruptcy prediction are Altman’s (1968) features. They were used in the study of Hu (2009) and that of De Andrés et al. (2011). Barboza et al. (2017) combined the features of Altman (1968) with the features of Carton and Hofer (2006), which have a greater impact on financial performance models in the short term. Similarly, Du Jardin (2015) applied financial ratios traditionally used in the literature since Altman (1968). These ratios were chosen based on the main financial dimensions which govern bankruptcy. Tseng and Hu (2010) used features inspired by the research of Lin (1999) and Lin and Piesse (2004).
Several studies (Kirkos 2015; Zvarikova et al. 2017; Kovacova et al. 2019) were published in which the authors examined the occurrence of individual features in bankruptcy prediction models. Researchers followed up the results of the study of Kovacova et al. (2019), who made a review of the most often used bankruptcy prediction features in Visegrad-group countries.
Based on the above mentioned, the research question was as follows: Which way of selecting financial features for DEA model ensures higher performance of the model: the domain knowledge approach or one of data mining techniques—LASSO regression?

2. Predicting Risk of Corporate Bankruptcy

Determining corporate bankruptcy risk is one of the main challenges of economic and financial research as well as one the most important issues for investors and decision-makers (Korol 2019). Predicting, measuring and assessing the risk of bankruptcy of a company is of particular interest to investors before investing their capital, as the optimization of risk is a prerequisite for the maximum capital profit of the investment, which will ensure payment of dividends. However, value maximization can only occur if capital providers selectively choose a profitable and sustainable business from which they can obtain the maximum share of business income (Agustia et al. 2020). The risk of bankruptcy is an important topic in many scientific articles, which is primarily reflected in the implications for the stakeholders’ decisions (Lukason and Camacho-Miñano 2019). Bankruptcy risk (insolvency) can be understood as “the company’s inability to meet maturing obligations resulting either from current operations, whose achievement conditions the continuation of activity, or from compulsory levies” (Bordeianu et al. 2011, p. 250). According to Achim et al. (2012), the risk of business bankruptcy is closely related to economic and financial risk. While financial risk is determined by the level of indebtedness, economic risk is dependent on the ratio of fixed and variable costs. It can be said that, in general, knowledge of these risks makes it possible to quantify the risk of bankruptcy of the company. Bankruptcy risk is the risk of a company no longer being able to meet its debt obligations. This risk is also referred to as the risk of failure or insolvency (Campbell 2011).
Bankruptcy risk represents a constant threat to businesses, which determines how long they will survive (Khan et al. 2020). If a business goes bankrupt, in fact, the probability of bankruptcy in connected businesses increases (Battiston et al. 2007), which can have a negative effect on the entire economy. Therefore, predicting the risk of bankruptcy is the subject of many research studies dealing with the search for the most suitable bankruptcy prediction model as well as the features describing bankruptcy the best.
Research on bankruptcy prediction dates back to Fitzpatrick (1932), who was the first to examine the financial conditions of bankrupt and non-bankrupt firms by comparing the values of their financial ratios. He found that there are significant differences between bankrupt and non-bankrupt companies, especially between liquidity, debt and turnover indicators (Fejér-Király 2015). In the early days of the development of bankruptcy prediction models, discriminant analysis (DA) was very popular. Beaver (1966) applied univariate discriminant analysis to investigate the predictive ability of 30 financial ratios. The best discriminating factor was identified as the working capital/debt ratio. The second one was the net income/total assets ratio (Gameel and El-Geziry 2016). Despite the criticism, this method was a starting point for the development of other models. The most famous bankruptcy-risk-scoring model, known as Z-score, was published by Altman in 1968 (Voda et al. 2021). This model was developed with the use of multiple discriminant analysis. Since the introduction of Altman’s model, many other authors (Deakin 1972; Altman et al. 1977; Norton and Smith 1979; Taffler 1983) developed their models based on multiple discriminant analysis. In the 1980s, logistic regression analysis was developed, followed by probit analysis. The first logistic regression model intended to predict the financial situation of businesses was developed by Ohlson (1980). In the next period, many authors (Kim and Gu 2006; Mihalovic 2016; Barreda et al. 2017; Khan 2018; Affes and Hentati-Kaffel 2019) compared the accuracy of the multiple discriminant analysis model and the logistic regression model. These two models were the most used parametric models in bankruptcy prediction (Fejér-Király 2015). Probit analysis has not been as widely used as logistic regression. The first probit model was developed by Zmijewski (1984), followed by Zavgren (1985). Since the 1990s, the development of computer science has enabled the use of more computationally demanding methods in bankruptcy prediction. These methods are mainly non-parametric. Within them, Mousavi et al. (2023) identifies two main groups: machine learning and artificial intelligence, and operation research. Most used methods within the machine learning and artificial intelligence group include artificial neural networks, such as those used by Messier and Hansen (1988), Odom and Sharda (1990), Atiya (2001) and Abid and Zouari (2002), decision trees (Frydman et al. 1985; Chen et al. 2011; Stankova and Hampel 2018), the Bayesian models (Sarkar and Sriram 2001; Aghaie and Saeedi 2009; Cao et al. 2022), genetic algorithms (Kingdom and Feldman 1995; Alfaro-Cid et al. 2007; Bateni and Asghari 2020), modeling based on rough sets (Ahn et al. 2000; Wang and Wu 2017) and support vector machines (Huang et al. 2004; Olson et al. 2012).
The main method within operation research is Data Envelopment Analysis. This method by Simak (1997) was firstly used when predicting corporate failure. In his master thesis, he compared the results of DEA with the results of Altman’s Z-score. In recent years, numerous models based on Data Envelopment Analysis have been developed to predict bankruptcy and their results were compared with the results achieved based on other techniques. Cielen et al. (2004) found that DEA outperformed a discriminant analysis model and a rule induction (C5.0) model in terms of their classification accuracy. Ouenniche and Tone (2017) proposed the out-of-sample evaluation of decision-making units by applying DEA. Out-of-sample framework was based on an instance of case-based reasoning methodology. They found that “DEA as a classifier is a real contender to Discriminant Analysis, which is one of the most commonly used classifiers by practitioners” (Ouenniche and Tone 2017, p. 249). Premachandra et al. (2009) compared the results of an additive DEA model with the results of a Logit model. They found that DEA outperformed the Logit model in evaluating bankruptcy out of sample. Condello et al. (2017, p. 2186) found that DEA has “a greater capacity for bankruptcy prediction, while Logit Regression and Discriminant Analysis perform better in non-bankruptcy and overall prediction in the short term”. Janova et al. (2012) achieved similar results. They found that the additive DEA model seems to perform well in correctly identifying bankrupt agricultural businesses. On the other hand, it is less powerful when identifying non-bankrupt agricultural businesses. The performance of DEA models is assessed mainly with the use of sensitivity, specificity, or overall accuracy. In this regard, Premachandra et al. (2011) pointed out that the cut-off point of 0.5 traditionally used to classify bankrupt and non-bankrupt businesses may not be appropriate for the DEA model. According to these authors “depending on the precision with which predictions for bankrupt and non-bankrupt businesses need to be done, the decision maker has to determine an appropriate cut-off point”, Premachandra et al. (2011, p. 623). Stefko et al. (2020) determined the optimal cut-off of the additive DEA model at a point in which the sum of sensitivity and specificity is the highest. Stankova and Hampel (2023) selected an optimal threshold by applying the Youden index and distance from the corner. They found that “selecting a suitable threshold improves specificity visibly with only a small reduction in the total accuracy” (Stankova and Hampel 2023, p. 129).
In the development of the above-mentioned models, the variables included in the model are as important as the method applied (Nurcan and Köksal 2021). In order to select appropriate variables from high-dimensional datasets, various dimensionality reduction methods can be applied. Depending on whether the original features are transformed into new features or not, feature extraction methods and feature selection methods are differentiated (Wang et al. 2016; Li et al. 2020). Feature extraction methods transform existing features into a lower-dimensional space (new set of features) while preserving the original relative distance between the features (Subasi and Gursoy 2010; Li et al. 2020). Well-known feature extraction methods often used in current research include Principal Component Analysis (Adisa et al. 2019; Karas and Reznakova 2020), Multidimensional Scaling (Tang et al. 2020) and Isometric Mapping (Gao et al. 2020). Since the new set of features is different from the original ones, it may be difficult to interpret them (Wang et al. 2016). When using feature selection methods, the original features are sorted according to specific criteria and features with the highest ranking are selected to form a subset (Li et al. 2020). Among the feature selection methods, researchers can differentiate between filter, wrapper, embedded and combined methods (Liu et al. 2018). Filter methods examine each feature independently while ignoring the individual performance of the feature in the relation to the group. Within filter methods, researchers frequently use t-test (Chandra et al. 2009; Xiao et al. 2012), correlation analysis (Zhou et al. 2012) and stepwise methods (Lin et al. 2010). Wrapper methods use machine learning algorithms to evaluate the performance of selected feature subsets. Within them, decision trees (Ratanamahatana and Gunopulos 2003), Naive Bayes (Chen et al. 2009), artificial neural networks (Ledesma et al. 2008) and genetic algorithms (Amini and Hu 2021) are often used. The results of wrapper methods are often superior to the results of filter methods; however, the computational cost of wrapper methods is high. Embedded methods integrate feature selection and learning procedures. Important embedded techniques are regularization approaches which have recently become more and more interesting, for example, LASSO (Fonti and Belitser 2017; Cao et al. 2022; Paraschiv et al. 2021), and Elastic net (Jones et al. 2016; Amini and Hu 2021). Combined methods include different types of feature selection measures, such as filter and wrapper.
Various methodologies have been applied to select features for DEA models. Cielen et al. (2004) used variables according to their efficiency to predict bankruptcy in prior research. Similarly, Psillaki et al. (2010) focused on financial ratios which appeared to be most successful in previous studies. Premachandra et al. (2009) approached this issue in the same way. When creating DEA models, they used ratios which were applied in past bankruptcy literature, and some of them were the same as the ratios used by Altman (1968) and Cielen et al. (2004). The ratios selected by Premachandra et al. (2009) were later applied in the study of Condello et al. (2017) and other studies. Min and Lee (2008) combined expert opinion and factor analysis when selecting features for DEA models. The resulting set of indicators contained the most relevant financial classification dimensions, while taking into account the mathematical relationships among ratios as well. Sueyoshi and Goto (2009) applied Principal Component Analysis to reduce the number of financial factors in order to reduce the computational burden of the DEA-DA model. Stefko et al. (2021) used Principal Component Analysis and Multidimensional Scaling when selecting inputs and outputs for DEA models. Huang et al. (2015) selected variables for DEA models based on gray relational analysis. They proved this method to be an effective technique for obtaining variables for DEA models. Gray relational analysis was later used in this way by Nurcan and Köksal (2021) as well. Lee and Cai (2020) were dealing with the curse of dimensionality in DEA. They proposed the LASSO variable selection technique and combined it in a sign-constrained convex nonparametric least squares (SCNLS) to support estimating the production function using DEA for small datasets. They also proved that this approach provides useful guidelines for DEA with small datasets. Chen et al. (2021) were inspired by their approach and proposed a simplified two-step LASSO+DEA approach to handle the dimensionality of data entering the DEA models via LASSO. They used standard cross-validation LASSO to select an optimal number of regressors. These regressors were used in the DEA model. As an important advantage of this approach against the study of Lee and Cai (2020), Chen et al. (2021) state that tuning parameter λ was not chosen manually, but it was determined based on optimizing the classical cross-validation criterion to optimally select the relevant variables.

This entry is adapted from the peer-reviewed paper 10.3390/risks11110199

References

  1. Veganzones, David, and Eric Severin. 2021. Corporate failure prediction models in the twenty-first century: A review. European Business Review 33: 204–26.
  2. Min, Joe H., and Young-Chan Lee. 2008. A practical approach to credit scoring. Expert Systems with Applications 35: 1762–70.
  3. Zhou, Ligang, Dong Lu, and Hamido Fujita. 2015. The performance of corporate financial distress prediction models with features selection guided by domain knowledge and data mining approaches. Knowledge-Based Systems 85: 52–61.
  4. Altman, Edward I. 1968. Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. Journal of Finance 23: 589–609.
  5. Hu, Yi-Chung. 2009. Bankruptcy prediction using ELECTRE-based single-layer perceptron. Neurocomputing 72: 3150–7.
  6. De Andrés, Javier, Pedro Lorca, Francisco Javier de Cos Juez, and Fernando Sánchez-Lasheras. 2011. Bankruptcy forecasting: A hybrid approach using Fuzzy c-means clustering and Multivariate Adaptive Regression Splines (MARS). Expert Systems with Applications 38: 1866–75.
  7. Barboza, Flavio, Herbert Kimura, and Edward Altman. 2017. Machine learning models and bankruptcy prediction. Expert Systems with Applications 83: 405–17.
  8. Carton, Robert B., and Charles W. Hofer. 2006. Measuring Organizational Performance. Cheltenham: Edward Elgar Publishing.
  9. Du Jardin, Philippe. 2015. Bankruptcy prediction using terminal failure processes. European Journal of Operational Research 242: 286–303.
  10. Tseng, Fang-Mei, and Yi-Chung Hu. 2010. Comparing four bankruptcy prediction models: Logit, quadratic interval logit, neural and fuzzy neural networks. Expert Systems with Applications 37: 1846–53.
  11. Lin, Lin. 1999. Does Takeover Help Distressed Acquirers to Escape from Bankruptcy? Some Lessons from the UK Industrial Sector. Ph.D. thesis, University of London, London, UK.
  12. Lin, Lin, and Jenifer Piesse. 2004. Identification of corporate distress in UK industrials: A conditional probability analysis approach. Applied Financial Economics 14: 73–82.
  13. Kirkos, Efstathios. 2015. Assessing methodologies for intelligent bankruptcy prediction. Artificial Intelligence Review 43: 83–123.
  14. Zvarikova, Katarina, Erika Spuchlakova, and Gabriela Sopkova. 2017. International comparison of the relevant variables in the chosen bankruptcy models used in the risk management. Oeconomia Copernicana 8: 145–57.
  15. Kovacova, Maria, Tomas Kliestik, Katarina Valaskova, Pavol Durana, and Zuzana Juhaszova. 2019. Systematic review of variables applied in bankruptcy prediction models of Visegrad group countries. Oeconomia Copernicana 10: 743–72.
  16. Korol, Tomasz. 2019. Dynamic bankruptcy prediction models for European enterprises. Journal of Risk and Financial Management 12: 185.
  17. Agustia, Dian, Nur Pratama A. Muhammad, and Yani Permatasari. 2020. Earnings management, business strategy, and bankruptcy risk: Evidence from Indonesia. Heliyon 6: E03317.
  18. Lukason, Oliver, and María-del-Mar Camacho-Miñano. 2019. Bankruptcy Risk, Its Financial Determinants and Reporting Delays: Do Managers Have Anything to Hide? Risks 7: 77.
  19. Bordeianu, Gabriela-Daniela, Florin Radu, Marius Dumitru Paraschivescu, and Willi Pӑvӑloaia. 2011. Analysis models of the bankruptcy risk. Economy Transdisciplinary Cognition 14: 248–59. Available online: https://www.ugb.ro/etc/etc2011no1/FIN-1-full.pdf (accessed on 20 July 2023).
  20. Achim, Monica Violeta, Codruta Mare, and Sorin Nicolae Borlea. 2012. A statistical model of financial risk bankruptcy applied for Romanian manufacturing industry. Procedia Economics and Finance 3: 132–37.
  21. Campbell, R. Harvey. 2011. Bankruptcy Risk. Available online: https://financial-dictionary.thefreedictionary.com/Bankruptcy+risk (accessed on 1 June 2023).
  22. Khan, Khurram A., Robert Dankiewicz, Yana Kliuchnikava, and Judit Oláh. 2020. How do entrepreneurs feel bankruptcy? International Journal of Entrepreneurial Knowledge 1: 89–101.
  23. Battiston, Stefano, Domenico Delli Gatti, Mauro Gallegati, Bruce Greenwald, and Joseph E. Stiglitz. 2007. Credit chains and bankruptcy propagation in production networks. Journal of Economic Dynamics & Control 31: 2061–84.
  24. Fitzpatrick, Paul J. 1932. A Comparison of the Ratios of Successful Industrial Enterprises with Those of Failed Companies. Washington, DC: The Accountants’ Publishing Company.
  25. Fejér-Király, Gergely. 2015. Bankruptcy Prediction: A Survey on Evolution, Critiques, and Solutions. Acta Universitatis Sapientiae, Economics and Business 3: 93–108.
  26. Beaver, William H. 1966. Financial ratios as predictors of failure. Journal of Accounting Research 4: 71–111.
  27. Gameel, Mohamed, and Khairy El-Geziry. 2016. Predicting Financial Distress: Multi Scenarios Modeling Using Neural Network. International Journal of Economics and Finance 8: 159–66.
  28. Voda, Alina D., Gabriela Dobrotă, Diana Mihaela Țîrcă, Dănuț Dumitru Dumitrașcu, and Dan Dobrotă. 2021. Corporate bankruptcy and insolvency prediction model. Technological and Economic Development of Economy 27: 1039–56.
  29. Deakin, Edward B. 1972. A Discriminant Analysis of Predictors of Business Failure. Journal of Accounting Research 10: 167–79.
  30. Altman, Edward I., Robert G. Haldeman, and Paul Narayanan. 1977. ZETA ANALYSIS, a new model to identify bankruptcy risk of corporations. Journal of Banking and Finance 1: 29–54.
  31. Norton, Curtis L., and Ralph E. Smith. 1979. A comparison of general price level and historical cost financial statements in the prediction of bankruptcy. The Accounting Review 54: 72–87. Available online: https://www.jstor.org/stable/246235 (accessed on 15 July 2023).
  32. Taffler, Richard J. 1983. The assessment of company solvency and performance using a statistical model. Accounting and Business Research 13: 295–308.
  33. Ohlson, James A. 1980. Financial Ratios and the Probabilistic Prediction of Bankruptcy. Journal of Accounting Research 18: 109–31.
  34. Kim, Hyunjoon, and Zheng Gu. 2006. Predicting Restaurant Bankruptcy: A Logit Model in Comparison with a Discriminant Model. Journal of Hospitality & Tourism Research 30: 474–93.
  35. Mihalovic, Matus. 2016. Performance comparison of multiple discriminant analysis and logit models in bankruptcy prediction. Economics & Sociology 9: 101–18.
  36. Barreda, Albert A., Yoshimasa Kageyama, Dipendra Singh, and Sandra Zubieta. 2017. Hospitality Bankruptcy in United States of America: A Multiple Discriminant Analysis-Logit Model Comparison. Journal of Quality Assurance in Hospitality and Tourism 18: 86–106.
  37. Khan, Usama E. 2018. Bankruptcy Prediction for Financial Sector of Pakistan: Evaluation of Logit and Discriminant Analysis Approaches. Pakistan Journal of Engineering Technology and Science 6: 210–20.
  38. Affes, Zeineb, and Rania Hentati-Kaffel. 2019. Predicting US Banks Bankruptcy: Logit Versus Canonical Discriminant Analysis. Computational Economics 54: 199–244.
  39. Zmijewski, Mark E. 1984. Methodological Issues Related to the Estimation of Financial Distress Prediction Models. Journal of Accounting Research 22: 59–82.
  40. Zavgren, Christine V. 1985. Assessing the Vulnerability to Failure of American Industrial Firms: A logistic Analysis. Journal of Business Finance & Accounting 12: 19–45.
  41. Mousavi, Muhammad M., Jamal Ouenniche, and Kaoru Tone. 2023. A dynamic performance evaluation of distress prediction models. Journal of Forecasting 42: 756–84.
  42. Messier, William F., Jr., and James V. Hansen. 1988. Inducing rules for expert system development: An example using default and bankruptcy data. Management Science 34: 1412–5.
  43. Odom, Marcus D., and Ramesh Sharda. 1990. A neural network model for bankruptcy prediction. Paper presented at 1990 IJCNN International Joint Conference on Neural Networks, San Diego, CA, USA, June 17–21.
  44. Atiya, Amir F. 2001. Bankruptcy prediction for credit risk using neural networks: A survey and new results. IEEE Transactions on Neural Networks 12: 929–35.
  45. Abid, Fathi, and Anis Zouari. 2002. Predicting corporate financial distress: A neural networks approach. Finance India 16: 601–12. Available online: https://ssrn.com/abstract=1300290 (accessed on 1 June 2023).
  46. Frydman, Halina, Edward I. Altman, and Duen-Li Kao. 1985. Introducing recursive portioning for financial classification: The case of financial distress. Journal of Finance 40: 269–91.
  47. Chen, Hui-Ling, Bo Yang, Gang Wang, Jie Liu, Xin Xu, Su-Jing Wang, and Da-You Liu. 2011. A novel bankruptcy prediction model based on an adaptive fuzzy k-nearest neighbor method. Knowledge-Based Systems 24: 1348–59.
  48. Stankova, Michaela, and David Hampel. 2018. Bankruptcy Prediction of Engineering Companies in the EU Using Classification Methods. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 66: 1347–56.
  49. Sarkar, Sumit, and Ram S. Sriram. 2001. Bayesian models for early warning of bank failures. Management Science 47: 1457–75.
  50. Aghaie, Arezoo, and Ali Saeedi. 2009. Using Bayesian Networks for Bankruptcy Prediction: Empirical Evidence from Iranian Companies. Paper presented at 2009 International Conference on Information Management and Engineering, Kuala Lumpur, Malaysia, April 3–5.
  51. Cao, Yi, Xiaoquan Liu, Jia Zhai, and Shan Hua. 2022. A two-stage Bayesian network model for corporate bankruptcy prediction. International Journal of Finance & Economics 27: 455–72.
  52. Kingdom, J., and K. Feldman. 1995. Genetic Algorithms for Bankruptcy Prediction. Search Space Research Report No. 01-95. London: Search Space Ltd.
  53. Alfaro-Cid, Eva, Ken Sharman, and Anna I. Esparcia-Alcazar. 2007. A genetic programming approach for bankruptcy prediction using a highly unbalanced database. In Workshops on Applications of Evolutionary Computation. Edited by Mario Giacobini. Berlin: Springer, pp. 169–78.
  54. Bateni, Leila, and Farshid Asghari. 2020. Bankruptcy Prediction Using Logit and Genetic Algorithm Models: A Comparative Analysis. Computational Economics 55: 335–48.
  55. Ahn, Byeong S., Sung Sik Cho, and Chang Yun Kim. 2000. The integrated methodology of rough set theory and artificial neural network for business failure prediction. Expert Systems with Applications 18: 65–74.
  56. Wang, Lu, and Chong Wu. 2017. Business failure prediction based on two-stage selective ensemble with manifold learning algorithm and kernel-based fuzzy self-organizing map. Knowledge-Based Systems 121: 99–110.
  57. Huang, Zan, Hsinchun Chen, Chia-Jung Hsu, Wun-Hwa Chen, and Saushan Wu. 2004. Credit rating analysis with support vector machines and neural networks: A market comparative study. Decision Support Systems 37: 543–58.
  58. Olson, David L., Dursun Delen, and Yanyan Meng. 2012. Comparative analysis of data mining methods for bankruptcy prediction. Decision Support Systems 52: 464–73.
  59. Simak, Paul C. 1997. DEA Based Analysis of Corporate Failure. Master’s thesis, University of Toronto, Toronto, ON, Canada.
  60. Cielen, Anja, Ludo Peeters, and Koen Vanhoof. 2004. Bankruptcy prediction using a data envelopment analysis. European Journal of Operational Research 154: 526–32.
  61. Ouenniche, Jamal, and Kaoru Tone. 2017. An out-of-sample evaluation framework for DEA with application in bankruptcy prediction. Annals of Operations Research 254: 235–50.
  62. Premachandra, I. M., Gurmeet S. Bhabra, and Toshiyuki Sueyoshi. 2009. DEA as a tool for bankruptcy assessment: A comparative study with logistic regression technique. European Journal of Operational Research 193: 412–24.
  63. Condello, Silva, Antonio Del Pozzo, Salvatore Loprevite, and Bruno Ricca. 2017. Potential and limitations of D.E.A. s a bankruptcy prediction tool in the light of a study on Italian listed companies. Applied Mathematical Sciences 11: 2185–207.
  64. Janova, Jitka, Jan Vavrina, and David Hampel. 2012. DEA as a tool for bankruptcy assessment: The agribusiness case study. Paper presented at 30th International Conference Mathematical Methods in Economics 2012, Karviná, Czech Republic, September 11–13; Available online: http://mme2012.opf.slu.cz/proceedings/pdf/065_Janova.pdf (accessed on 1 July 2023).
  65. Premachandra, I. M., Yao Chen, and Jon Watson. 2011. DEA as a tool for predicting corporate failure and success: A case of bankruptcy assessment. Omega 3: 620–6.
  66. Stefko, Robert, Jarmila Horvathova, and Martina Mokrisova. 2020. Bankruptcy prediction with the use of data envelopment analysis: An empirical study of Slovak businesses. Journal of Risk and Financial Management 13: 212.
  67. Stankova, Michaela, and David Hampel. 2023. Optimal threshold of data envelopment analysis in bankruptcy prediction. SORT-Statistics and Operations Research Transactions 47: 129–50.
  68. Nurcan, Ebru, and Can Deniz Köksal. 2021. Determination of Financial Failure Indicators by Gray Relational Analysis and Application of Data Envelopment Analysis and Logistic Regression Analysis in BIST 100 Index. Iranian Journal of Management Studies 14: 163–87.
  69. Wang, Lipo, Yaoli Wang, and Qing Chang. 2016. Feature selection methods for big data bioinformatics: A survey from the search perspective. Methods 111: 21–31.
  70. Li, Mengmeng, Haofeng Wang, Lifang Yang, You Liang, Zhigang Shang, and Hong Wan. 2020. Fast hybrid dimensionality reduction method for classification based on feature selection and grouped feature extraction. Expert Systems with Applications 150: 113277.
  71. Subasi, Abdulhamit, and M. Ismail Gursoy. 2010. EEG signal classification using PCA, ICA, LDA and support vector machines. Expert Systems with Applications 37: 8659–66.
  72. Adisa, Juliana Adeola, Samuel Olusegun Ojo, Pius Adewale Owolawi, and Agnieta Beatrijs Pretorius. 2019. Financial Distress Prediction: Principle Component Analysis and Artificial Neural Networks. Paper presented at 2019 International Multidisciplinary Information Technology and Engineering Conference (IMITEC), Vanderbijlpark, South Africa, November 21–22.
  73. Karas, Michal, and Mária Reznakova. 2020. Cash flow indicators in the prediction of financial distress. Engineering Economics 31: 525–35.
  74. Tang, Xueying, Zhi Wang, Qiwei He, Jingchen Liu, and Zhiliang Ying. 2020. Latent Feature Extraction for Process Data via Multidimensional Scaling. Psychometrika 85: 378–97.
  75. Gao, Shuzhi, Sixuan Zhang, Yimin Zhang, and Yue Gao. 2020. Operational reliability evaluation and prediction of rolling bearing based on isometric mapping and NoCuSa-LSSVM. Reliability Engineering and System Safety 201: 106968.
  76. Liu, Xiao-Ying, Yong Liang, Sai Wang, Zi-Yi Yang, and Han-Shuo Ye. 2018. A Hybrid Genetic Algorithm With Wrapper-Embedded Approaches for Feature Selection. IEEE Access 6: 22863–74.
  77. Chandra, D. Karthik, Vadlamani Ravi, and Indranil Bose. 2009. Failure prediction of dotcom companies using hybrid intelligent techniques. Expert Systems with Applications 36: 4830–7.
  78. Xiao, Zhi, Xianglei Yang, Ying Pang, and Xin Dang. 2012. The prediction for listed companies’ financial distress by using multiple prediction methods with rough set and Dempster–Shafer evidence theory. Knowledge-Based Systems 26: 196–206.
  79. Zhou, Ligang, Kin K. Lai, and Jerome Yen. 2012. Empirical models based on features ranking techniques for corporate financial distress prediction. Computers & Mathematics with Applications 64: 2484–96.
  80. Lin, Fengyi, Deron Liang, and Wing-Sang Chu. 2010. The role of non-financial features related to corporate governance in business crisis prediction. Journal of Marine Science and Technology 18: 504–13.
  81. Ratanamahatana, Chotirat A., and Dimitrios Gunopulos. 2003. Feature selection for the naive bayesian classifier using decision trees. Applied Artificial Intelligence 17: 475–87.
  82. Chen, Jingnian, Houkuan Huang, Shengfeng Tian, and Youli Qu. 2009. Feature selection for text classification with Naïve Bayes. Expert Systems with Applications 36: 5432–35.
  83. Ledesma, Sergio, Gustavo Cerda, Gabriel Aviña, Donato Hernández, and Miguel Torres. 2008. Feature Selection Using Artificial Neural Networks. In Advances in Artificial Intelligence. Edited by Alexander Gelbukh and Eduardo F. Morales. Paper presented at Micai 2008, October 27–31. Berlin/Heidelberg: Springer, vol. 5317.
  84. Amini, Fatemeh, and Guiping Hu. 2021. A two-layer feature selection method using Genetic Algorithm and Elastic Net. Expert Systems with Applications 166: 114072.
  85. Fonti, Valeria, and Eduard Belitser. 2017. Feature Selection Using LASSO. VU Amsterdam Research Paper in Business Analytics. Amsterdam: VRIJE Universiteit Amsterdam, pp. 1–25. Available online: https://www.semanticscholar.org/paper/Paper-in-Business-Analytics-Feature-Selection-using-Fonti-Belitser/24acd159910658223209433cf4cbe3414264de39 (accessed on 5 July 2023).
  86. Paraschiv, Florentina, Markus Schmid, and Ranik Raaen Wahlstrøm. 2021. Bankruptcy Prediction of Privately Held SMEs Using Feature Selection Methods. SSRN Electronic Journal, 1–64.
  87. Jones, Stewart, David Johnstone, and Roy Wilson. 2016. Predicting Corporate Bankruptcy: An Evaluation of Alternative Statistical Frameworks. Journal of Business Finance & Accounting 44: 3–34.
  88. Psillaki, Maria, Ioannis E. Tsolas, and Dimitris Margaritis. 2010. Evaluation of credit risk based on firm performance. European Journal of Operational Research 201: 873–81.
  89. Sueyoshi, Toshiyuki, and Mika Goto. 2009. DEA-DA for bankruptcy-based performance assessment: Misclassification analysis of Japanese construction industry. European Journal of Operational Research 199: 576–94.
  90. Stefko, Robert, Jarmila Horvathova, and Martina Mokrisova. 2021. The Application of Graphic Methods and the DEA in Predicting the Risk of Bankruptcy. Journal of Risk and Financial Management 14: 220.
  91. Huang, Chao, Chong Dai, and Miao Guo. 2015. A hybrid approach using two-level DEA for financial failure prediction and integrated SE-DEA and GCA for indicators selection. Applied Mathematics and Computation 251: 431–41.
  92. Lee, Chia-Yen, and Jia-Ying Cai. 2020. LASSO variable selection in data envelopment analysis with small datasets. Omega 91: 102019.
  93. Chen, Ya, Mike G. Tsionas, and Valentin Zelenyuk. 2021. LASSO+DEA for small and big wide data. Omega 102: 120419.
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