Forecasting Financial Distress With Machine Learning – A Review
Keywords:Bankruptcy, Credit Risk, Artificial Intelligence, Machine Learning
Purpose – Evaluate the various academic researches with multiple views on credit risk and artificial intelligence (AI) and their evolution.
Theoretical framework – The study is divided as follows: Section 1 introduces the article. Section 2 deals with credit risk and its relationship with computational models and techniques. Section 3 presents the methodology. Section 4 addresses a discussion of the results and challenges on the topic. Finally, section 5 presents the conclusions.
Design/methodology/approach – A systematic review of the literature was carried out without defining the time period and using the Web of Science and Scopus database.
Findings – The application of computational technology in the scope of credit risk analysis has drawn attention in a unique way. It was found that the demand for identification and introduction of new variables, classifiers and more assertive methods is constant. The effort to improve the interpretation of data and models is intense.
Research, Practical & Social implications – It contributes to the verification of the theory, providing information in relation to the most used methods and techniques, it brings a wide analysis to deepen the knowledge of the factors and variables on the theme. It categorizes the lines of research and provides a summary of the literature, which serves as a reference, in addition to suggesting future research.
Originality/value – Research in the area of Artificial Intelligence and Machine Learning is recent and requires attention and investigation, thus, this study contributes to the opening of new views in order to deepen the work on this topic.
Abdou, H. A. and Pointon, J. (2011), ‘Credit scoring, statistical techniques and evaluation criteria: a review of the literature’, Intelligent systems in accounting, finance and management 18(2-3), 59–88.
Abellán, J. and Castellano, J. G. (2017), ‘A comparative study on base classifiers in ensemble methods for credit scoring’, Expert Systems with Applications 73, 1–10.
Abellán, J. and Mantas, C. J. (2014), ‘Improving experimental studies about ensembles of classifiers for bankruptcy prediction and credit scoring’, Expert Systems with Applications 41(8), 3825–3830.
Addo, P. M., Guegan, D. and Hassani, B. (2018), ‘Credit risk analysis using machine and deep learning models’, Risks 6(2), 1–20.
AghaeiRad, A., Chen, N. and Ribeiro, B. (2017), ‘Improve credit scoring using transfer of learned knowledge from self-organizing map’, Neural Computing and Applications 28(6), 1329–1342.
Ahn, H. and jae Kim, K. (2009), ‘Bankruptcy prediction modeling with hybrid case-based reasoning and genetic algorithms approach’, Applied Soft Computing Journal 9(2), 599–607.
Alaka, H. A., Oyedele, L. O., Owolabi, H. A., Kumar, V., Ajayi, S. O., Akinade, O. O. and Bilal, M. (2018), ‘Systematic review of bankruptcy prediction models: Towards a framework for tool selection’, Expert Systems with Applications 94, 164–184.
Ala’raj, M. and Abbod, M. F. (2016a), ‘A new hybrid ensemble credit scoring model based on classifiers consensus system approach’, Expert Systems with Applications 64, 36–55.
Ala’Raj, M. and Abbod, M. F. (2016b), ‘Classifiers consensus system approach for credit scoring’, Knowledge-Based Systems 104, 89–105.
Alfaro, E., García, N., Gámez, M. and Elizondo, D. (2008), ‘Bankruptcy forecasting: An empirical comparison of AdaBoost and neural networks’, Decision Support Systems 45(1), 110–122.
Altman, E. I. (1968), ‘Financial ratios, discriminant analysis and the prediction of corporate bankruptcy’, The journal of finance 23(4), 589–609.
Altman, E. I. (2002), ‘Managing credit risk: A challenge for the new millennium’, Economic
Notes 31(2), 201–214.
Altman, E. I., Baidya, T. K. N. and Dias, L. M. R. (1979), ‘Assessing Potential Financial
Problems for Firms in Brazil’, Journal of International Business Studies 10(2), 9–24.
Altman, E. I., Marco, G. and Varetto, F. (1994), ‘Corporate distress diagnosis: Comparisons
using linear discriminant analysis and neural networks (the Italian experience)’, Journal of
Banking and Finance 18(3), 505–529.
Antunes, F., Ribeiro, B. and Pereira, F. (2017), ‘Probabilistic modeling and visualization for
bankruptcy prediction’, Applied Soft Computing Journal 60, 831–843.
Aziz, M. A. and Dar, H. A. (2006), ‘Predicting corporate bankruptcy: Where we stand?’, Corporate Governance 6(1), 18–33.
Bahrammirzaee, A. (2010), ‘A comparative survey of artificial intelligence applications in finance: Artificial neural networks, expert system and hybrid intelligent systems’, Neural Computing and Applications 19(8), 1165–1195.
Balcaen, S. and Ooghe, H. (2006), ‘35 years of studies on business failure: An overview of the classic statistical methodologies and their related problems’, British Accounting Review 38(1), 63–93.
Barboza, F., Kimura, H. and Altman, E. (2017), ‘Machine learning models and bankruptcy prediction’, Expert Systems with Applications 83, 405–417.
Beaver, W. H. (1966), ‘of Failure Financial Ratios as Predictors’, Journal of Accounting Research 4(1966), 71–111.
Begley, J., Ming, J. and Watts, S. (1996), ‘Bankruptcy classification errors in the 1980s: an empirical analysis of altman’s and ohlson’s models’, Review of Accounting Studies 1(4), 267–284.
Bekhet, H. A. and Eletter, S. F. K. (2014), ‘Credit risk assessment model for Jordanian commercial banks: Neural scoring approach’, Review of Development Finance 4(1), 20–28.
Bequé, A. and Lessmann, S. (2017), ‘Extreme learning machines for credit scoring: An empirical evaluation’, Expert Systems with Applications 86, 42–53.
Beynon, M. J. and Peel, M. J. (2001), ‘Variable precision rough set theory and data discretisation: An application to corporate failure prediction’, Omega 29(6), 561–576.
Bojovic, S., Mati ´ c, R., Popovi ´ c, Z., Smiljani ´ c, M., Stefanovi ´ c, M. and Vidakovi ´ c, V. (2014), ´‘An overview of forestry journals in the period 2006–2010 as basis for ascertaining research trends’, Scientometrics 98(2), 1331–1346.
Borgman, C. L. (1989), ‘Bibliometrics and scholarly communication: Editor’s introduction’, Communication Research 16(5), 583–599.
Bose, I. (2006), ‘Deciding the financial health of dot-coms using rough sets’, Information and Management 43(7), 835–846.
Boyacioglu, M. A., Kara, Y. and Baykan, Ö. K. (2009), ‘Predicting bank financial failures using neural networks, support vector machines and multivariate statistical methods: A comparative analysis in the sample of savings deposit insurance fund (SDIF) transferred banks in Turkey’, Expert Systems with Applications 36(2 PART 2), 3355–3366.
Brealey, R. A. and Myers, S. C. (1996), Principles of financial management., McGraw-Hill, New York.
Brockett, P. L., Cooper, W. W., Golden, L. L. and Pitaktong, U. (1994), ‘A Neural Network Method for Obtaining an Early Warning of Insurer Insolvency’, The Journal of Risk and Insurance 61(3), 402.
Brown, I. and Mues, C. (2012), ‘An experimental comparison of classification algorithms for imbalanced credit scoring data sets’, Expert Systems with Applications 39(3), 3446–3453.
Chandra, D. K., Ravi, V. and Bose, I. (2009), ‘Failure prediction of dotcom companies using hybrid intelligent techniques’, Expert Systems with Applications 36(3 PART 1), 4830–4837.
Chang, T. M. and Hsu, M. F. (2018), ‘Integration of incremental filter-wrapper selection strategy with artificial intelligence for enterprise risk management’, International Journal of Machine Learning and Cybernetics 9(3), 477–489.
Chaudhuri, A. and De, K. (2011), ‘Fuzzy Support Vector Machine for bankruptcy prediction’, Applied Soft Computing Journal 11(2), 2472–2486.
Chen, F. L. and Li, F. C. (2010), ‘Combination of feature selection approaches with SVM in credit scoring’, Expert Systems with Applications 37(7), 4902–4909.
Chen, M. Y. (2011), ‘Predicting corporate financial distress based on integration of decision tree classification and logistic regression’, Expert Systems with Applications 38(9), 11261–11272.
Chen, N., Ribeiro, B. and Chen, A. (2016), ‘Financial credit risk assessment: a recent review’, Artificial Intelligence Review 45(1), 1–23.
Chen, N., Ribeiro, B., Vieira, A. and Chen, A. (2013), ‘Clustering and visualization of bankruptcy trajectory using self-organizing map’, Expert Systems with Applications 40(1), 385–393.
Chen, N., Ribeiro, B., Vieira, A. S., Duarte, J. and Neves, J. C. (2011), ‘A genetic algorithmbased approach to cost-sensitive bankruptcy prediction’, Expert Systems with Applications 38(10), 12939–12945.
Chen, W. H. and Shih, J. Y. (2006), ‘A study of Taiwan’s issuer credit rating systems using support vector machines’, Expert Systems with Applications 30(3), 427–435.
Chen, W. S. and Du, Y. K. (2009), ‘Using neural networks and data mining techniques for the financial distress prediction model’, Expert Systems with Applications 36(2 PART 2), 4075–4086.
Chen, Y. S. and Cheng, C. H. (2013), ‘Hybrid models based on rough set classifiers for setting credit rating decision rules in the global banking industry’, Knowledge-Based Systems 39, 224–239.
Cheng, C., Jones, S. and Moser, W. J. (2018), ‘Abnormal trading behavior of specific types of shareholders before US firm bankruptcy and its implications for firm bankruptcy prediction’, Journal of Business Finance and Accounting 45(9-10), 1100–1138.
Cho, S., Kim, J. and Bae, J. K. (2009), ‘An integrative model with subject weight based on neural network learning for bankruptcy prediction’, Expert Systems with Applications 36(1), 403–410.
Chou, C. H., Hsieh, S. C. and Qiu, C. J. (2017), ‘Hybrid genetic algorithm and fuzzy clustering for bankruptcy prediction’, Applied Soft Computing Journal 56, 298–316.
Chuang, C. L. (2013), ‘Application of hybrid case-based reasoning for enhanced performance in bankruptcy prediction’, Information Sciences 236, 174–185.
Cielen, A., Peeters, L. and Vanhoof, K. (2004), ‘Bankruptcy prediction using a data envelopment analysis’, European Journal of Operational Research 154(2), 526–532.
Cole, F. J. and Eales, N. B. (1917), ‘The history of comparative anatomy : Part i - a statistical analysis of the literature’, Science Progress 11(44), 578–596.
Copleland, T. E. and Weston, J. F. (1988), Financial Theory and Corporate Policy, Mass.Addison-Wesley Publishing Company.
Danenas, P. and Garsva, G. (2015), ‘Selection of Support Vector Machines based classifiers for credit risk domain’, Expert Systems with Applications 42(6), 3194–3204.
De Andrés, J., Landajo, M. and Lorca, P. (2005), ‘Forecasting business profitability by using classification techniques: A comparative analysis based on a Spanish case’, European Journal of Operational Research 167(2), 518–542.
De Andrés, J., Lorca, P., De Cos Juez, F. J. and Sánchez-Lasheras, F. (2011), ‘Bankruptcy forecasting: A hybrid approach using fuzzy c-means clustering and multivariate adaptive regression splines (MARS)’, Expert Systems with Applications 38(3), 1866–1875.
de Azevedo, R. C., Ensslin, L. and Jungles, A. E. (2014), ‘A Review of Risk Management in Construction: Opportunities for Improvement’, Modern Economy 05(04), 367–383.
Doumpos, M. and Zopounidis, C. (2011), ‘A Multicriteria Outranking Modeling Approach for Credit Rating’, Decision Sciences 42(3), 721–742.
Du Jardin, P. (2016), ‘A two-stage classification technique for bankruptcy prediction’, European Journal of Operational Research 254(1), 236–252.
du Jardin, P. (2017), ‘Dynamics of firm financial evolution and bankruptcy prediction’, Expert Systems with Applications 75, 25–43.
du Jardin, P. (2018), ‘Failure pattern-based ensembles applied to bankruptcy forecasting’, Decision Support Systems 107, 64–77.
Du Jardin, P. and Séverin, E. (2012), ‘Forecasting financial failure using a Kohonen map: A comparative study to improve model stability over time’, European Journal of Operational Research 221(2), 378–396.
Ensslin, L., Ensslin, S. R., Dutra, A., Nunes, N. A. and Reis, C. (2017), ‘BPM governance: a literature analysis of performance evaluation’, Business Process Management Journal 23(1), 71–86.
Etheridge, H. L., Sriram, R. S. and Hsu, H. Y. K. (2000), ‘A Comparison of Selected Artificial Neural Networks that Help Auditors Evaluate Client Financial Viability’, Decision Sciences 31(2), 531–550.
Fedorova, E., Gilenko, E. and Dovzhenko, S. (2013), ‘Bankruptcy prediction for Russian companies: Application of combined classifiers’, Expert Systems with Applications 40(18), 7285–7293.
Feng, X., Xiao, Z., Zhong, B., Dong, Y. and Qiu, J. (2019), ‘Dynamic weighted ensemble classification for credit scoring using Markov Chain’, Applied Intelligence 49(2), 555–568.
Feng, X., Xiao, Z., Zhong, B., Qiu, J. and Dong, Y. (2018), ‘Dynamic ensemble classification for credit scoring using soft probability’, Applied Soft Computing Journal 65, 139–151.
Finlay, S. (2011), ‘Multiple classifier architectures and their application to credit risk assessment’, European Journal of Operational Research 210(2), 368–378.
Florez-Lopez, R. (2007), ‘Modelling of insurers’ rating determinants. An application of machine learning techniques and statistical models’, European Journal of Operational Research 183(3), 1488–1512.
García, V., Marqués, A. I. and Sánchez, J. S. (2014), ‘An insight into the experimental design for credit risk and corporate bankruptcy prediction systems’, Journal of Intelligent Information Systems 44(1), 159–189.
García, V., Marqués, A. I. and Sánchez, J. S. (2019), ‘Exploring the synergetic effects of sample types on the performance of ensembles for credit risk and corporate bankruptcy prediction’, Information Fusion 47, 88–101.
García, V., Marqués, A. I., Sánchez, J. S. and Ochoa-Domínguez, H. J. (2019), ‘DissimilarityBased Linear Models for Corporate Bankruptcy Prediction’, Computational Economics 53(3), 1019–1031.
Geng, R., Bose, I. and Chen, X. (2015), ‘Prediction of financial distress: An empirical study of listed Chinese companies using data mining’, European Journal of Operational Research 241(1), 236–247.
Glänzel, W. (2001a), ‘Coauthorship patterns and trends in the sciences (1980-1998): A bibliometric study with implications for database indexing and search strategies’, Library Trends 50(3), 461–473.
Glänzel, W. (2001b), ‘National characteristics in international scientific co-authorship relations’, Scientometrics 51(1), 69–115.
Gorzałczany, M. B. and Rudzinski, F. (2016), ‘A multi-objective genetic optimization for fast, ´ fuzzy rule-based credit classification with balanced accuracy and interpretability’, Applied Soft Computing Journal 40, 206–220.
Griffin, J. M. and Lemmon, M. L. (2002), ‘1540-6261.00497.Pdf’, LVII(5), 2317–2336.
Grilli, R., Tedeschi, G. and Gallegati, M. (2015), ‘Markets connectivity and financial contagion’, Journal of Economic Interaction and Coordination 10(2), 287–304.
Guo, Y., Zhou, W., Luo, C., Liu, C. and Xiong, H. (2016), ‘Instance-based credit risk assessment for investment decisions in P2P lending’, European Journal of Operational Research 249(2), 417–426.
Hajek, P. and Michalak, K. (2013), ‘Feature selection in corporate credit rating prediction’, Knowledge-Based Systems 51, 72–84.
Hajek, P., Olej, V. and Myskova, R. (2014), ‘Forecasting corporate financial performance using sentiment in annual reports for stakeholders’ decision-making’, Technological and Economic Development of Economy 20(4), 721–738.
Härdle, W., Lee, Y. J., Schäfer, D. and Yeh, Y. R. (2009), ‘Variable selection and oversampling in the use of smooth support vector machines for predicting the default risk of companies’, Journal of Forecasting 28(6), 512–534.
Heo, J. and Yang, J. Y. (2014), ‘AdaBoost based bankruptcy forecasting of Korean construction companies’, Applied Soft Computing Journal 24, 494–499.
Hillegeist, S. A., Keating, E. K., Cram, D. P. and Lundstedt, K. G. (2004), ‘Assessing the probability of bankruptcy’, Review of Accounting Studies 9(1), 5–34.
Hsieh, T. J., Hsiao, H. F. and Yeh, W. C. (2012), ‘Mining financial distress trend data using penalty guided support vector machines based on hybrid of particle swarm optimization and artificial bee colony algorithm’, Neurocomputing 82, 196–206.
Hu, Y. C. (2009), ‘Bankruptcy prediction using ELECTRE-based single-layer perceptron’, Neurocomputing 72(13-15), 3150–3157.
Hu, Y. C. and Ansell, J. (2007), ‘Measuring retail company performance using credit scoring techniques’, European Journal of Operational Research 183(3), 1595–1606.
Huang, J. J., Tzeng, G. H. and Ong, C. S. (2006), ‘Two-stage genetic programming (2SGP) for the credit scoring model’, Applied Mathematics and Computation 174(2), 1039–1053.
Huang, S. M., Tsai, C. F., Yen, D. C. and Cheng, Y. L. (2008), ‘A hybrid financial analysis model for business failure prediction’, Expert Systems with Applications 35(3), 1034–1040.
Huang, Z., Chen, H., Hsu, C. J., Chen, W. H. and Wu, S. (2004), ‘Credit rating analysis with support vector machines and neural networks: A market comparative study’, Decision Support Systems 37(4), 543–558.
Hung, C. and Chen, J. H. (2009), ‘A selective ensemble based on expected probabilities for bankruptcy prediction’, Expert Systems with Applications 36(3 PART 1), 5297–5303.
Jo, H. and Han, I. (1996), ‘Integration of case-based forecasting, neural network, and discriminant analysis for bankruptcy prediction’, Expert Systems with Applications 11(4 SPEC. ISS.), 415–422.
Jones, S. (2017), ‘Corporate bankruptcy prediction: a high dimensional analysis’, Review of Accounting Studies 22(3), 1366–1422.
Jones, S., Johnstone, D. and Wilson, R. (2015), ‘An empirical evaluation of the performance of binary classifiers in the prediction of credit ratings changes’, Journal of Banking and Finance 56, 72–85.
Jones, S., Johnstone, D. and Wilson, R. (2017), ‘Predicting Corporate Bankruptcy: An Evaluation of Alternative Statistical Frameworks’, Journal of Business Finance and Accounting 44(1-2), 3–34.
Karan, M. B., Ulucan, A. and Kaya, M. (2013), ‘Credit risk estimation using payment history data: A comparative study of Turkish retail stores’, Central European Journal of Operations Research 21(2), 479–494.
Kim, K. J. and Ahn, H. (2012), ‘A corporate credit rating model using multi-class support vector machines with an ordinal pairwise partitioning approach’, Computers and Operations Research 39(8), 1800–1811.
Kim, M. J. and Han, I. (2003), ‘The discovery of experts’ decision rules from qualitative bankruptcy data using genetic algorithms’, Expert Systems with Applications 25(4), 637–646.
Kim, M. J. and Kang, D. K. (2010), ‘Ensemble with neural networks for bankruptcy prediction’, Expert Systems with Applications 37(4), 3373–3379.
Kim, M. J. and Kang, D. K. (2012), ‘Classifiers selection in ensembles using genetic algorithms for bankruptcy prediction’, Expert Systems with Applications 39(10), 9308–9314.
Kim, M. J., Kang, D. K. and Kim, H. B. (2015), ‘Geometric mean based boosting algorithm with over-sampling to resolve data imbalance problem for bankruptcy prediction’, Expert Systems with Applications 42(3), 1074–1082.
Kim, S. Y. and Upneja, A. (2014), ‘Predicting restaurant financial distress using decision tree and AdaBoosted decision tree models’, Economic Modelling 36, 354–362.
Ko, P. C. and Lin, P. C. (2006), ‘An evolution-based approach with modularized evaluations to forecast financial distress’, Knowledge-Based Systems 19(1), 84–91.
Korol, T. (2013), ‘Early warning models against bankruptcy risk for Central European and Latin American enterprises’, Economic Modelling 31(1), 22–30.
Kou, G., Peng, Y. and Lu, C. (2014), ‘MCDM approach to evaluating bank loan default models’, Technological and Economic Development of Economy 20(2), 292–311.
Koutanaei, F. N., Sajedi, H. and Khanbabaei, M. (2015), ‘A hybrid data mining model of feature selection algorithms and ensemble learning classifiers for credit scoring’, Journal of Retailing and Consumer Services 27, 11–23.
Koyuncugil, A. S. and Ozgulbas, N. (2012), ‘Financial early warning system model and data mining application for risk detection’, Expert Systems with Applications 39(6), 6238–6253.
Kwak, W., Shi, Y., Eldridge, S. W. and Kou, G. (2006), ‘Bankruptcy prediction for Japanese firms: Using Multiple Criteria Linear Programming data mining approach’, International Journal of Business Intelligence and Data Mining 1(4), 401–416.
Lahsasna, A., Ainon, R. N. and Wah, T. Y. (2010), ‘Credit scoring models using soft computing methods: A survey’, International Arab Journal of Information Technology 7(2), 115–123.
Lee, K. C., Han, I. and Kwon, Y. (1996), ‘Hybrid neural network models for bankruptcy predictions’, Decision Support Systems 18(1 SPEC. ISS.), 63–72.
Lee, Y. C. (2007), ‘Application of support vector machines to corporate credit rating prediction’, Expert Systems with Applications 33(1), 67–74.
Lessmann, S., Baesens, B., Seow, H. V. and Thomas, L. C. (2015), ‘Benchmarking state-of-theart classification algorithms for credit scoring: An update of research’, European Journal of Operational Research 247(1), 124–136.
Li, F. and Perez-Saiz, H. (2018), ‘Measuring systemic risk across financial market infrastructures’, Journal of Financial Stability 34, 1–11.
Li, H. and Sun, J. (2009), ‘Hybridizing principles of the Electre method with case-based reasoning for data mining: Electre-CBR-I and Electre-CBR-II’, European Journal of Operational Research 197(1), 214–224.
Li, H. and Sun, J. (2010), ‘Business failure prediction using hybrid2 case-based reasoning (H2CBR)’, Computers and Operations Research 37(1), 137–151.
Li, H. and Sun, J. (2011), ‘Principal component case-based reasoning ensemble for business failure prediction’, Information and Management 48(6), 220–227.
Li, H. and Sun, J. (2012), ‘Forecasting business failure: The use of nearest-neighbour support vectors and correcting imbalanced samples - Evidence from the Chinese hotel industry’, Tourism Management 33(3), 622–634.
Li, H., Sun, J. and Sun, B. L. (2009), ‘Financial distress prediction based on OR-CBR in the principle of k-nearest neighbors’, Expert Systems with Applications 36(1), 643–659.
Liang, D., Lu, C. C., Tsai, C. F. and Shih, G. A. (2016), ‘Financial ratios and corporate governance indicators in bankruptcy prediction: A comprehensive study’, European Journal of Operational Research 252(2), 561–572.
Liang, D., Tsai, C. F., Dai, A. J. and Eberle, W. (2018), ‘A novel classifier ensemble approach for financial distress prediction’, Knowledge and Information Systems 54(2), 437–462.
Liang, D., Tsai, C. F. and Wu, H. T. (2015), ‘The effect of feature selection on financial distress prediction’, Knowledge-Based Systems 73(1), 289–297.
Lin, F., Liang, D. and Chen, E. (2011), ‘Financial ratio selection for business crisis prediction’, Expert Systems with Applications 38(12), 15094–15102.
Lin, F. Y. and McClean, S. (2001), A data mining approach to the prediction of corporate failure, in ‘Applications and Innovations in Intelligent Systems VIII’, Springer, pp. 93–106.
Lin, R. H., Wang, Y. T., Wu, C. H. and Chuang, C. L. (2009), ‘Developing a business failure prediction model via RST, GRA and CBR’, Expert Systems with Applications 36(2 PART1), 1593–1600.
Lin, S. J., Chang, C. and Hsu, M. F. (2013), ‘Multiple extreme learning machines for a two-classimbalance corporate life cycle prediction’, Knowledge-Based Systems 39, 214–223.
Lin, W. C., Lu, Y. H. and Tsai, C. F. (2019), ‘Feature selection in single and ensemble learningbased bankruptcy prediction models’, Expert Systems 36(1), 1–8.
Lin, W. Y., Hu, Y. H. and Tsai, C. F. (2012), ‘Machine learning in financial crisis prediction: A survey’, IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews 42(4), 421–436.
Liou, F. M. (2008), ‘Fraudulent financial reporting detection and business failure prediction models: A comparison’, Managerial Auditing Journal 23(7), 650–662.
Liu, W., Gu, M., Hu, G., Li, C., Liao, H., Tang, L. and Shapira, P. (2014), ‘Profile of developments in biomass-based bioenergy research: a 20-year perspective’, Scientometrics 99(2), 507–521.
Liu, Y. and Schumann, M. (2005), ‘Data mining feature selection for credit scoring models’, Journal of the Operational Research Society 56(9), 1099–1108.
Lopez, J. A. and Saidenberg, M. R. (2000), ‘Evaluating credit risk models’, Journal of Banking & Finance 24(1-2), 151–165.
Lozano, S., Calzada-Infante, L., Adenso-Díaz, B. and García, S. (2019), ‘Complex network analysis of keywords co-occurrence in the recent efficiency analysis literature’, Scientometrics 120(2), 609–629.
Marqués, A. I., García, V. and Sánchez, J. S. (2012a), ‘Exploring the behaviour of base classifiers in credit scoring ensembles’, Expert Systems with Applications 39(11), 10244–10250.
Marqués, A. I., García, V. and Sánchez, J. S. (2012b), ‘Two-level classifier ensembles for credit risk assessment’, Expert Systems with Applications 39(12), 10916–10922.
Marqués, A. I., García, V. and Sánchez, J. S. (2013a), ‘A literature review on the application of evolutionary computing to credit scoring’, Journal of the Operational Research Society 64(9), 1384–1399.
Marqués, A. I., García, V. and Sánchez, J. S. (2013b), ‘On the suitability of resampling techniques for the class imbalance problem in credit scoring’, Journal of the Operational Research Society 64(7), 1060–1070.
Martin, D. (1977), ‘Early warning of bank failure. A logit regression approach’, Journal of Banking and Finance 1(3), 249–276.
McKee, T. E. (2003), ‘Rough sets bankruptcy prediction models versus auditor signalling rates’, Journal of Forecasting 22(8), 569–586.
McKee, T. E. and Lensberg, T. (2002), ‘Genetic programming and rough sets: A hybrid approach to bankruptcy classification’, European Journal of Operational Research 138(2), 436–451.
Min, J. H. and Jeong, C. (2009), ‘A binary classification method for bankruptcy prediction’, Expert Systems with Applications 36(3 PART 1), 5256–5263.
Min, J. H. and Lee, Y. C. (2005), ‘Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters’, Expert Systems with Applications 28(4), 603–614.
Min, J. H. and Lee, Y. C. (2008), ‘A practical approach to credit scoring’, Expert Systems with Applications 35(4), 1762–1770.
Min, S. H., Lee, J. and Han, I. (2006), ‘Hybrid genetic algorithms and support vector machines for bankruptcy prediction’, Expert Systems with Applications 31(3), 652–660.
Moro, S., Cortez, P. and Rita, P. (2015), ‘Business intelligence in banking: A literature analysis from 2002 to 2013 using text mining and latent Dirichlet allocation’, Expert Systems with Applications 42(3), 1314–1324.
Nanni, L. and Lumini, A. (2009), ‘An experimental comparison of ensemble of classifiers for bankruptcy prediction and credit scoring’, Expert Systems with Applications 36(2 PART 2), 3028–3033.
Odom, M. D. and Sharda, R. (1990), ‘A neural network model for bankruptcy prediction’, IJCNN. International Joint Conference on Neural Networks pp. 163–168.
Ohlson, J. A. (1980), ‘Financial Ratios and the Probabilistic Prediction of Bankruptcy’, Journal of Accounting Research 18(1), 109.
Olson, D. L., Delen, D. and Meng, Y. (2012), ‘Comparative analysis of data mining methods for bankruptcy prediction’, Decision Support Systems 52(2), 464–473.
Ögüt, H., Do ˇ ganay, M. M., Ceylan, N. B. and Akta¸s, R. (2012), ‘Prediction of bank financial strength ratings: The case of Turkey’, Economic Modelling 29(3), 632–640.
Paleologo, G., Elisseeff, A. and Antonini, G. (2010), ‘Subagging for credit scoring models’, European Journal of Operational Research 201(2), 490–499.
Pan, W. T. (2012), ‘A new Fruit Fly Optimization Algorithm: Taking the financial distress model as an example’, Knowledge-Based Systems 26, 69–74.
Pendharkar, P. C. (2005), ‘A threshold-varying artificial neural network approach for classification and its application to bankruptcy prediction problem’, Computers and Operations Research 32(10), 2561–2582.
Polonioli, A. (2020), ‘In search of better science: on the epistemic costs of systematic reviews and the need for a pluralistic stance to literature search’, Scientometrics 122(2), 1267–1274.
Pritchard, A. (1969), ‘Pritchard1969’, Journal of documentation 25(4), 348–349.
Ribeiro, B., Chen, N. and Kovacec, A. (2019), ‘Shaping graph pattern mining for financial risk’, Neurocomputing 326-327, 123–131.
Ribeiro, B., Silva, C., Chen, N., Vieira, A. and Carvalho Das Neves, J. (2012), ‘Enhanced default risk models with SVM+’, Expert Systems with Applications 39(11), 10140–10152.
Rochon, L. P. and Rossi, S. (2010), ‘Has "It" Happened Again?’, International Journal of Political Economy 39(2), 5–9.
Salcedo-Sanz, S., Fernández-Villacañas, J. L., Segovia-Vargas, M. J. and Bousoño-Calzón, C. (2005), ‘Genetic programming for the prediction of insolvency in non-life insurance companies’, Computers and Operations Research 32(4), 749–765.
Scherr, F. C. (1989), Modern Working Capital Management, Prentice Hall.
Serrano-Cinca, C. (1996), ‘Self organizing neural networks for financial diagnosis’, Decision Support Systems 17(3), 227–238.
Serrano-Cinca, C. and Gutiérrez-Nieto, B. (2013), ‘Partial least square discriminant analysis for bankruptcy prediction’, Decision Support Systems 54(3), 1245–1255.
Shin, K. S., Lee, T. S. and Kim, H. J. (2005), ‘An application of support vector machines in bankruptcy prediction model’, Expert Systems with Applications 28(1), 127–135.
Shin, K. S. and Lee, Y. J. (2002), ‘A genetic algorithm application in bankruptcy prediction modeling’, Expert Systems with Applications 23(3), 321–328.
Sun, J., Fujita, H., Chen, P. and Li, H. (2017), ‘Dynamic financial distress prediction with concept drift based on time weighting combined with Adaboost support vector machine ensemble’, Knowledge-Based Systems 120, 4–14.
Sun, J., Jia, M. Y. and Li, H. (2011), ‘AdaBoost ensemble for financial distress prediction: An empirical comparison with data from Chinese listed companies’, Expert Systems with Applications 38(8), 9305–9312.
Sun, J., Lang, J., Fujita, H. and Li, H. (2018), ‘Imbalanced enterprise credit evaluation with DTESBD: Decision tree ensemble based on SMOTE and bagging with differentiated sampling rates’, Information Sciences 425, 76–91.
Sun, J. and Li, H. (2008), ‘Data mining method for listed companies’ financial distress prediction’, Knowledge-Based Systems 21(1), 1–5.
Sun, J., Li, H., Huang, Q. H. and He, K. Y. (2014), ‘Predicting financial distress and corporate failure: A review from the state-of-the-art definitions, modeling, sampling, and featuring approaches’, Knowledge-Based Systems 57, 41–56.
Sung, T. K., Chang, N. and Lee, G. (1999), ‘Dynamics of Modeling in Data Mining: Interpretive Approach to Bankruptcy Prediction’, Journal of Management Information Systems 16(1), 63–85.
Taffler, R. J. (1982), ‘Forecasting Company Failure in the UK Using Discriminant Analysis and Financial Ratio Data’, Journal of the Royal Statistical Society 145(3), 342–358.
Tam, K. Y. and Kiang, M. (1990), ‘Predicting bank failures: A neural network approach’, Applied Artificial Intelligence 4(4), 265–282.
Tam, K. Y. and Kiang, M. Y. (1992), ‘Managerial applications of neural networks: The case of bank failure predictions’, Management Science 38(7), 926–947.
Tang, L. (2013), ‘Does “birds of a feather flock together” matter—evidence from a longitudinal study on us–china scientific collaboration’, Journal of Informetrics 7(2), 330–344.
Tang, X., Li, S., Tan, M. and Shi, W. (n.d.), ‘Incorporating textual and management factors into financial distress prediction: A comparative study of machine learning methods’, Journal of Forecasting.
Tascón Fernández, M. T. and Castaño Gutiérrez, F. J. (2012), ‘Variables and models for the identification and prediction of business failure: Revision of recent empirical research advances’, Revista de Contabilidad-Spanish Accounting Review 15(1), 7–58.
Tian, S. and Yu, Y. (2017), ‘Financial ratios and bankruptcy predictions: An international evidence’, International Review of Economics and Finance 51, 510–526.
Tian, Y., Shi, Y. and Liu, X. (2012), ‘Recent advances on support vector machines research’, Technological and Economic Development of Economy 18(1), 5–33.
Tobback, E., Bellotti, T., Moeyersoms, J., Stankova, M. and Martens, D. (2017), ‘Bankruptcy prediction for SMEs using relational data’, Decision Support Systems 102, 69–81.
Tollefson, J. (2018), ‘China declared world’s largest producer of scientific articles’, Nature 553(7689), 390–390.
Tsai, C. F. (2008), ‘Financial decision support using neural networks and support vector machines’, Expert Systems 25(4), 380–393.
Tsai, C. F. (2009), ‘Feature selection in bankruptcy prediction’, Knowledge-Based Systems 22(2), 120–127.
Tsai, C. F. (2014), ‘Combining cluster analysis with classifier ensembles to predict financial distress’, Information Fusion 16(1), 46–58.
Tsai, C. F. and Chen, M. L. (2010), ‘Credit rating by hybrid machine learning techniques’, Applied Soft Computing Journal 10(2), 374–380.
Tsai, C. F. and Cheng, K. C. (2012), ‘Simple instance selection for bankruptcy prediction’, Knowledge-Based Systems 27, 333–342.
Tsai, C. F., Hsu, Y. F. and Yen, D. C. (2014), ‘A comparative study of classifier ensembles for bankruptcy prediction’, Applied Soft Computing Journal 24, 977–984.
Tsai, C. F. and Wu, J. W. (2008), ‘Using neural network ensembles for bankruptcy prediction and credit scoring’, Expert Systems with Applications 34(4), 2639–2649.
Tung, W. L., Quek, C. and Cheng, P. (2004), ‘GenSo-EWS: A novel neural-fuzzy based early warning system for predicting bank failures’, Neural Networks 17(4), 567–587.
Twala, B. (2010), ‘Multiple classifier application to credit risk assessment’, Expert Systems with Applications 37(4), 3326–3336.
Van Leeuwen, T. N., Visser, M. S., Moed, H. F., Nederhof, T. J. and Van Raan, A. F. (2003), ‘The holy grail of science policy: Exploring and combining bibliometric tools in search of scientific excellence’, Scientometrics 57(2), 257–280.
Varetto, F. (1998), ‘Genetic algorithms applications in the analysis of insolvency risk’, Journal of Banking and Finance 22(10-11), 1421–1439.
Verikas, A., Kalsyte, Z., Bacauskiene, M. and Gelzinis, A. (2010), ‘Hybrid and ensemble-based soft computing techniques in bankruptcy prediction: A survey’, Soft Computing 14(9), 995–1010.
Wang, G., Chen, G. and Chu, Y. (2018), ‘A new random subspace method incorporating sentiment and textual information for financial distress prediction’, Electronic Commerce Research and Applications 29, 30–49.
Wang, G., Hao, J., Ma, J. and Jiang, H. (2011), ‘A comparative assessment of ensemble learning for credit scoring’, Expert Systems with Applications 38(1), 223–230.
Wang, G. and Ma, J. (2011), ‘Study of corporate credit risk prediction based on integrating boosting and random subspace’, Expert Systems with Applications 38(11), 13871–13878.
Wang, G. and Ma, J. (2012), ‘A hybrid ensemble approach for enterprise credit risk assessment based on Support Vector Machine’, Expert Systems with Applications 39(5), 5325–5331.
Wang, G., Ma, J., Huang, L. and Xu, K. (2012), ‘Two credit scoring models based on dual strategy ensemble trees’, Knowledge-Based Systems 26, 61–68.
Wang, G., Ma, J. and Yang, S. (2014), ‘An improved boosting based on feature selection for corporate bankruptcy prediction’, Expert Systems with Applications 41(5), 2353–2361.
Wang, J., Veugelers, R. and Stephan, P. (2017), ‘Bias against novelty in science: A cautionary tale for users of bibliometric indicators’, Research Policy 46(8), 1416–1436.
Wang, Y., Wang, S. and Lai, K. K. (2005), ‘A new fuzzy support vector machine to evaluate credit risk’, IEEE Transactions on Fuzzy Systems 13(6), 820–831.
Wilson, R. L. and Sharda, R. (1994), ‘Bankruptcy prediction using neural networks’, Decision support systems 11(5), 545–557.
Wu, C. H., Tzeng, G. H., Goo, Y. J. and Fang, W. C. (2007), ‘A real-valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy’, Expert Systems with Applications 32(2), 397–408.
Wu, D. D., Olson, D. L. and Luo, C. (2014), ‘A decision support approach for accounts receivable risk management’, IEEE Transactions on Systems, Man, and Cybernetics: Systems 44(12), 1624–1632.
Yang, Z., You, W. and Ji, G. (2011), ‘Using partial least squares and support vector machines for bankruptcy prediction’, Expert Systems with Applications 38(7), 8336–8342.
Yu, L., Wang, S. and Lai, K. K. (2008), ‘Credit risk assessment with a multistage neural network ensemble learning approach’, Expert Systems with Applications 34(2), 1434–1444.
Yu, L., Yao, X., Wang, S. and Lai, K. K. (2011), ‘Credit risk evaluation using a weighted least squares SVM classifier with design of experiment for parameter selection’, Expert Systems with Applications 38(12), 15392–15399.
Yu, Q., Miche, Y., Séverin, E. and Lendasse, A. (2014), ‘Bankruptcy prediction using Extreme Learning Machine and financial expertise’, Neurocomputing 128, 296–302.
Zelenkov, Y., Fedorova, E. and Chekrizov, D. (2017), ‘Two-step classification method based on genetic algorithm for bankruptcy forecasting’, Expert Systems with Applications 88, 393–401.
Zhang, G., Hu, M. Y., Patuwo, B. E. and Indro, D. C. (1999), ‘Artificial neural networks in bankruptcy prediction: general framework and cross-validation analysis’, European Journal of Operational Research 116(1), 16–32.
Zhang, Y., Wang, S. and Ji, G. (2013), ‘A rule-based model for bankruptcy prediction based on an improved genetic ant colony algorithm’, Mathematical Problems in Engineering 2013.
Zhang, Z., Gao, G. and Shi, Y. (2014), ‘Credit risk evaluation using multi-criteria optimization classifier with kernel, fuzzification and penalty factors’, European Journal of Operational Research 237(1), 335–348.
Zhao, H., Sinha, A. P. and Ge, W. (2009), ‘Effects of feature construction on classification performance: An empirical study in bank failure prediction’, Expert Systems with Applications 36(2 PART 2), 2633–2644.
Zhong, H., Miao, C., Shen, Z. and Feng, Y. (2014), ‘Comparing the learning effectiveness of BP, ELM, I-ELM, and SVM for corporate credit ratings’, Neurocomputing 128, 285–295.
Zhou, L. (2013), ‘Performance of corporate bankruptcy prediction models on imbalanced dataset: The effect of sampling methods’, Knowledge-Based Systems 41, 16–25.
Zhu, Y., Xie, C., Wang, G. J. and Yan, X. G. (2017), ‘Comparison of individual, ensemble and integrated ensemble machine learning methods to predict China’s SME credit risk in supply chain finance’, Neural Computing and Applications 28(s1), 41–50.
Zieba, M., Tomczak, S. K. and Tomczak, J. M. (2016), ‘Ensemble boosted trees with synthetic features generation in application to bankruptcy prediction’, Expert Systems with Applications 58, 93–101.
Zmijweski, M. E. (1984), ‘Methodological Issues Related to the Estimation of Financial Distress Prediction Models’, Journal of Accounting Research 22, 59–82.
How to Cite
Authors who publish with this journal agree to the following terms:
1. Authors who publish in this journal agree to the following terms: the author(s) authorize(s) the publication of the text in the journal;
2. The author(s) ensure(s) that the contribution is original and unpublished and that it is not in the process of evaluation by another journal;
3. The journal is not responsible for the views, ideas and concepts presented in articles, and these are the sole responsibility of the author(s);
4. The publishers reserve the right to make textual adjustments and adapt texts to meet with publication standards.
5. Authors retain copyright and grant the journal the right to first publication, with the work simultaneously licensed under the Creative Commons Atribuição NãoComercial 4.0 internacional, which allows the work to be shared with recognized authorship and initial publication in this journal.
6. Authors are allowed to assume additional contracts separately, for non-exclusive distribution of the version of the work published in this journal (e.g. publish in institutional repository or as a book chapter), with recognition of authorship and initial publication in this journal.
7. Authors are allowed and are encouraged to publish and distribute their work online (e.g. in institutional repositories or on a personal web page) at any point before or during the editorial process, as this can generate positive effects, as well as increase the impact and citations of the published work (see the effect of Free Access) at http://opcit.eprints.org/oacitation-biblio.html• 8. Authors are able to use ORCID is a system of identification for authors. An ORCID identifier is unique to an individual and acts as a persistent digital identifier to ensure that authors (particularly those with relatively common names) can be distinguished and their work properly attributed.