A review of the limitations of financial failure prediction research

Referencias bibliográficas

• Agarwal, V., & Taffler, R. (2008). Comparing the performance of market-based and accounting-based bankruptcy prediction models. Journal of Banking and Finance, 32(8), 1541-1551. https://doi.org/10.1016/j.jbankfin.2007.07.014
• Agarwal, A., & Patni, I. (2019a). Applicability of Altman Z-score in bankruptcy prediction of BSE PSUs. Journal of Commerce and Accounting Research, 8(2), 93-103.
• Agarwal A., & Patni, I. (2019b). Bankruptcy prediction models: an empirical comparison. International Journal of Innovative Technology and Exploring Engineering, 8(6S2), 131-139.
• Alaka, H. A., Oyedele, L. O., Owolabi, H. A., Ajayi, S. O., Bilal, M., & Akinade, O. O. (2016). Methodological approach of construction business failure prediction studies: a review. Construction Management and Economics, 34(11), 808-842. https://doi.org/10.1080/01446193.2016.1219037
• Alaka, H. A., Oyedele, L. O., Owolabi, H. A., Kumar, V., Ajayi, S. O., Akinade, O. O., & Bilal, M. (2018). Systematic review of bankruptcy prediction models: towards a framework for tool selection. Expert Systems with Applications, 94, 164-184. https://doi.org/10.1016/j.eswa.2017.10.040
• Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23(4), 589-609. https://www.jstor.org/stable/2978933
• Altman, E. I. (1983). Why businesses fail. Journal of Business Strategy, 3(4), 15-21. https://doi.org/10.1108/eb038985
• Altman, E. I. (1983). Why businesses fail. Journal of Business Strategy, 3(4), 15-21. https://doi.org/10.1108/eb038985
• Altman, E. I., Haldeman, R. G., & Narayanan, P. (1977). Zeta-Analysis. A new model to identify bankruptcy risk of corporations. Journal of Banking and Finance, 1(1), 29-54. https://doi.org/10.1016/0378-4266(77)90017-6
• Altman, E. I. (1984). The success of business failure prediction models: an international survey. Journal of Banking & Finance, 8(2), 171-198. https://doi.org/10.1016/0378-4266(84)90003-7
• Altman, E. I., & Narayanan, P. (1997). An international survey of business failure classification models. Financial Markets, Institutions & Instruments, 6(2), 1-57. https://doi.org/10.1111/1468-0416.00010
• Altman, E. I., & Hotchkiss, E. (2006). Corporate financial distress and bankruptcy. Third edition. Nueva Jersey, USA: John Wiley & Sons. https://doi.org/10.1002/9781118267806
• Altman, E. I., & Sabato, G. (2007). Modelling credit risk for SMEs: evidence from the U.S. market. Abacus, 43, 332-357. https://doi.org/10.1111/j.1467-6281.2007.00234.x
• Altman, E. I., Sabato, G., & Wilson, N. (2010). The value of non-financial information in small and medium-sized enterprise risk management. Journal of Credit Risk, 6(2), 1-33. https://doi.org/10.21314/JCR.2010.110
• Altman, E. I., Iwanicz-Drozdowska, M., Laitinen, E. K., & Suvas, A. (2017). Financial distress prediction in an international context: a review and empirical analysis of Altman's Z-Score model. Journal of International Financial Management & Accounting, 28(2), 131-171. https://doi.org/10.1111/jifm.12053
• Altman, E. I., Iwanicz-Drozdowska, M., Laitinen, E. K., & Suvas, A. (2020). A Race for Long Horizon Bankruptcy Prediction, Applied Economics, 52(37), 4092-4111. https://doi.org/10.1080/00036846.2020.1730762
• Amani, F. A., & Fadlalla, A. M. (2017). Data mining applications in accounting: A review of the literature and organizing framework. International Journal of Accounting Information Systems, 24, 32-58.
• Amankwah-Amoah, J. (2016). An integrative process model of organisational failure. Journal of Business Research, 69(9), 3388-3397. https://doi.org/10.1016/j.jbusres.2016.02.005
• Appiah, K. O., Chizema, A., & Arthur, J. (2015). Predicting corporate failure: a systematic literature review of methodological issues. International Journal of Law and Management, 57(5), 461-485. https://doi.org/10.1108/IJLMA-04-2014-0032
• Appiah, K. O., & Chizema, A. (2015). Remuneration committee and corporate failure. Corporate Governance, 15(5), 623-640. https://doi.org/10.1108/CG-11-2014-0129
• Åstebro, T., & Winter, J. K. (2012). More than a dummy: the probability of failure, survival and acquisition of firms in financial distress. European Management Review, 9(1), 1-17. https://doi.org/10.1111/j.1740-4762.2011.01024.x
• Aziz, A., Emanuel, D. C., & Lawson, G. H. (1988). Bankruptcy prediction - an investigation of cash flow based models. Journal of Management Studies, 25(5), 419-437. https://doi.org/10.1111/j.1467-6486.1988.tb00708.x
• Aziz, A., & Lawson, G. H. (1989). Cash flow reporting and financial distress models: testing of hypotheses. Financial Management, 18(1), 55-63. https://doi.org/10.2307/3665698
• Aziz, M. A., & Dar, H. A. (2006). Predicting corporate bankruptcy: where we stand? Corporate Governance, 6(1), 18-33. https://doi.org/10.1108/14720700610649436
• Back, P. (2005). Explaining financial difficulties based on previous payment behaviour, management background variables and financial ratios. European Accounting Review, 14(4), 839-868. https://doi.org/10.1080/09638180500141339
• 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. https://doi.org/10.1007/s00521-010-0362-z
• Balcaen, S., & Ooghe, H. (2006). 35 years of studies on business failure: an overview of the classic statistical methodologies and their related problems. The British Accounting Review, 38(1), 63-93. https://doi.org/10.1016/j.bar.2005.09.001
• Barboza, F., Kimura, H., & Altman, E. I. (2017). Machine learning models and bankruptcy prediction. Expert Systems with Applications, 83, 405-417.https://doi.org/10.1016/j.eswa.2017.04.006
• Barnes, P. (1987). The analysis and use of financial ratios. Journal of Business Finance and Accounting, 14(4), 449-461. https://doi.org/10.1111/j.1468-5957.1987.tb00106.x
• Barton, J. (2001). Does the use of financial derivatives affect earnings management decisions? The Accounting Review, 76(1), 1-26. https://doi.org/10.2308/accr.2001.76.1.1
• Beaver, W. H. (1966). Financial ratios as predictors of failure. Empirical Research in Accounting: Selected Studies, 4, Supplement, 71-111.
• Beaver, W. H., Correia, M., & McNichols, M. F. (2012). Do differences in financial reporting attributes impair the predictive ability of financial ratios for bankruptcy? Review of Accounting Studies, 17(4), 969-1010. https://doi.org/10.1007/s11142-012-9186-7
• Bellovary, J. L., Giacomino, D. E., & Akers, M. D. (2007). A review of bankruptcy prediction studies: 1930 to present. Journal of Financial Education, 1(1), 3-41. https://www.jstor.org/stable/41948574
• Bose, I. (2006). Deciding the financial health of dot-coms using rough sets. Information & Management, 43, 835-846. https://doi.org/10.1016/j.im.2006.08.001
• Boughanmi, A., & Nigam, N. (2017). A Survey of Corporate Bankruptcy Reforms: Lessons to Be Learnt for Worldwide Good Practices. European Journal of Interdisciplinary Studies, 3(3), 7-21. https://doi.org/10.26417/ejis.v3i3.p7-21
• Bulow, J., & Shoven, J. (1978). The bankruptcy decision. Bell Journal of Economics, 9(2), 437-456. https://doi.org/10.2307/3003592
• Burgstahler, D., & Dichev, I. (1997). Earnings management to avoid earnings decreases and losses. Journal of Accounting and Economics, 24(1), 99-126. https://doi.org/10.1016/S0165-4101(97)00017-7
• Camacho-Miñano, M. M., Segovia-Vargas, M. J., & Pascual-Ezama, D. (2015). Which characteristics predict the survival of insolvent firms? An SME reorganization prediction model. Journal of Small Business Management, 53(2), 340-354. https://doi.org/10.1111/jsbm.12076
• Campa, D., & Camacho-Miñano, M. M. (2014). Earnings management among bankrupt non-listed firms: evidence from Spain. Spanish Journal of Finance and Accounting, 43(1), 3-20. https://doi.org/10.1080/02102412.2014.890820
• Campa, D., & Camacho-Miñano, M. M. (2015). The impact of SME's pre-bankruptcy financial distress on earnings management tools. International Review of Financial Analysis, 42, 222-234. https://doi.org/10.1016/j.irfa.2015.07.004
• Campbell, J. Y., Hilscher, J., & Szilagyi, J. (2008). In search of distress risk. The Journal of Finance, 73(6), 2899-2939. http://www.jstor.org/stable/20487953
• Carter, R., & Van Auken, H. (2006). Small firm bankruptcy. Journal of Small Business Management, 44(4), 493-512. https://doi.org/10.1111/j.1540-627X.2006.00187.x
• Casey, C. J., & Bartczak, N. J. (1984). Cash flow - it's not the bottom line. Harvard Business Review, 62(4), 60-66.
• Casey, C. J., & Bartczak, N. J. (1985). Using operating cash flow data to predict financial distress: some extensions. Journal of Accounting Research, 23(1), 384-401. https://doi.org/10.2307/2490926
• Charitou, A., Neophytou, E., & Charalambous, C. (2004). Predicting corporate failure: empirical evidence for the UK. European Accounting Review, 13(3), 465-497. https://doi.org/10.1080/0963818042000216811
• Charitou, A., Dionysiou, D., Lambertides, N., & Trigeorgis, L. (2013). Alternative bankruptcy prediction models using option-pricing theory. Journal of Banking & Finance, 37(7), 2329-2341. https://doi.org/10.1016/j.jbankfin.2013.01.020
• Cho, S., Kim, J., & 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. https://doi.org/10.1016/j.eswa.2007.09.060
• Ciampi, F. (2015). Corporate governance characteristics and default prediction modeling for small enterprises. An empirical analysis of Italian firms. Journal of Business Research, 68(5), 1012-1025. https://doi.org/10.1016/j.jbusres.2014.10.003
• Cultrera, L., & Brédart, X. (2016). Bankruptcy prediction: the case of Belgian SMEs. Review of Accounting and Finance, 15(1), 101-119. https://doi.org/10.1108/RAF-06-2014-0059
• D'Aveni, R. A. (1989). Dependability and organizational bankruptcy: an application of agency and prospect theory. Management Science, 35(9), 1120-1138. https://doi.org/10.1287/mnsc.35.9.1120
• Davydenko, S. A., & Franks, J. R. (2008). Do bankruptcy codes matter? A study of defaults in France, Germany, and the UK. The Journal of Finance, 63(2), 565-608. https://doi.org/10.1111/j.1540-6261.2008.01325.x
• De Andrés, J., Landajo, M., & Lorca, P. (2012). Bankruptcy prediction models based on multinorm analysis: an alternative to accounting ratios. Knowledge-Based Systems, 30, 67-77. https://doi.org/10.1016/j.knosys.2011.11.005
• DeFond, M. L., & Jiambalvo, J. (1994). Debt covenant violation and manipulation of accruals. Journal of Accounting and Economics, 17(1-2), 145-176. https://doi.org/10.1016/0165-4101(94)90008-6
• Demchenko, Y., Grosso, P., De Laat, C., & Membrey, P. (2013). Addressing big data issues in scientific data infrastructure. In Collaboration Technologies and Systems (CTS), 2013 International Conference on IEEE. 48-55. Available at: http://uazone.org/demch/papers/bddac2013-bigdata-infrastructure-v06.pdf
• Dias, A., & Teixeira, A. A. (2017). The anatomy of business failure: A qualitative account of its implications for future business success. European Journal of Management and Business Economics, 26(1), 2-20. https://doi.org/10.1108/EJMBE-07-2017-001
• Dietrich, J. R. (1984). Discussion of methodological issues related to the estimation of financial distress prediction models. Journal of Accounting Research, 83-86.
• Dimitras, A.I., Zanakis, S.H., & Zopundis, C. (1996). A survey of business failures with an emphasis on prediction methods and industrial applications. European Journal of Operational Research, 90, 487-513. https://doi.org/10.1016/0377-2217(95)00070-4
• Dirickx, Y., & Van Landeghem, G. (1994). Statistical failure prevision problems. Tijdschrift voor Economie en Management, 39(4), 429-462.
• Divsalar, M., Firouzabadi, A. K., Sadeghi, M., Behrooz, A. H., & Alavi, A. H. (2011). Towards the prediction of business failure via computational intelligence techniques. Expert Systems, 28(3), 209-226. https://doi.org/10.1111/j.1468-0394.2011.00580.x
• Du Jardin, P. (2010). Predicting bankruptcy using neural networks and other classification methods: the influence of variable selection techniques on model accuracy. Neurocomputing, 73(10), 2047-2060. https://doi.org/10.1016/j.neucom.2009.11.034
• Du Jardin, P. (2015). Bankruptcy prediction using terminal failure processes. European Journal of Operational Research, 242(1), 286-303. https://doi.org/10.1016/j.ejor.2014.09.059
• Du Jardin, P. (2021a). Forecasting bankruptcy using biclustering and neural network-based ensembles. Annals of Operations Research, 299, 531-566. https://doi.org/10.1007/s10479-019-03283-2
• Du Jardin, P. (2021b). Forecasting corporate failure using ensemble of self-organizing neural networks. European Journal of Operational Research. 288(3), 869-885. https://doi.org/10.1016/j.ejor.2020.06.020.
• Edmister, R. O. (1972). An empirical test of financial ratio analysis for small business failure prediction. Journal of Financial and Quantitative analysis, 7(2), 1477-1493. https://doi.org/10.2307/2329929
• Farooq, U., & Qamar, M. A. J. (2019). Predicting multistage financial distress: Reflections on sampling, feature and model selection criteria. Journal of Forecasting, 38(7), 632-648. https://doi.org/10.1002/for.2588
• Fich, E. M., & Slezak, S. L. (2008). Can corporate governance save distressed firms from bankruptcy? An empirical analysis. Review of Quantitative Finance and Accounting, 30(2), 225-251. https://doi.org/10.1007/s11156-007-0048-5
• Fuertes-Callén, Y., Cuellar-Fernández, B., & Serrano-Cinca, C. (2022). Predicting startup survival using first years financial statements, Journal of Small Business Management, 60(6), 1314-1350. https://doi.org/10.1080/00472778.2020.1750302
• García Lara, J. M., Osma, B. G., & Neophytou, E. (2009). Earnings quality in ex-post failed firms. Accounting and Business Research, 39(2), 119-138. https://doi.org/10.1080/00014788.2009.9663353
• Gentry, J. A., Newbold, P., & Whitford, D. T. (1985). Classifying bankrupt firms with funds flow components. Journal of Accounting Research, 23(1), 146-160. https://doi.org/10.2307/2490911
• Gentry, J. A., Newbold, P., & Whitford, D. T. (1987). Funds flow components, financial ratios, and bankruptcy. Journal of Business Finance & Accounting, 14(4), 595-606.
• Giesecke, K., & Goldberg, L. R. (2004). Forecasting default in the face of uncertainty. The Journal of Derivatives, 12(1), 11-25. https://doi.org/10.3905/jod.2004.434534
• Gombola, M. J., & Ketz, J. E. (1983). A note on cash flow and classification patterns of financial ratios. The Accounting Review, 105-114. http://www.jstor.org/stable/246645
• Grice, J. S., & Dugan, M. T. (2001). The limitations of bankruptcy prediction models: some cautions for the researcher. Review of Quantitative Finance and Accounting, 17(2), 151-166. https://doi.org/10.1023/A:1017973604789
• Hannan, M. T., & Freeman, J. (1977). The population ecology of organizations. American Journal of Sociology, 82(5), 929-964. https://www.jstor.org/stable/2777807
• Hernández Tinoco, M., & Wilson, N. (2013). Financial distress and bankruptcy prediction among listed companies using accounting, market and macroeconomic variables. International Review of Financial Analysis, 30, 394-419. https://doi.org/10.1016/j.irfa.2013.02.013
• Hillegeist, S., Keating, E., Cram, D., & Lundstedt, K. (2004). Assessing the Probability of Bankruptcy. Review of Accounting Studies, 9, March, 5-34. https://doi.org/10.1023/B:RAST.0000013627.90884.b7
• Inanga, E. L., & Schneider, W. B. (2005). The failure of accounting research to improve accounting practice: a problem of theory and lack of communication. Critical Perspectives on Accounting, 16(3), 227-248. https://doi.org/10.1016/S1045-2354(03)00073-X
• Iwanicz-Drozdowska, M., Laitinen, E. K., Suvas, A., & Altman, E. I. (2016). Financial and non-financial variables as long-horizon predictors of bankruptcy. Journal of Credit Risk, 12(4), 49-78. https://doi.org/10.21314/JCR.2016.216
• Jiménez Cardoso, S. M. (1996). A critical evaluation of the empirical research developed around business solvency. Spanish Accounting Review, 26, 459-479.
• Johanson, U., Mårtensson, M., & Skoog, M. (2001). Mobilizing change through the management control of intangibles. Accounting, Organizations and Society, 26(7-8), 715-733. https://doi.org/10.1016/S0361-3682(01)00024-1
• Jones, F. (1987). Current techniques in bankruptcy predicting. Journal of Accounting Literature, 6, 131-164.
• Jones, S. (2017). Corporate bankruptcy prediction: a high dimensional analysis. Review of Accounting Studies, 22(3), 1366-1422. https://doi.org/10.1007/s11142-017-9407-1
• Jones, S., & Hensher, D. A. (2004). Predicting firm financial distress: a mixed logit model. The Accounting Review, 79(4), 1011-1038. https://www.jstor.org/stable/4093084
• Kallunki, J. P., & Pyykkö, E. (2013). Do defaulting CEOs and directors increase the likelihood of financial distress of the firm? Review of Accounting Studies, 18(1), 228-260. https://doi.org/10.1007/s11142-012-9203-x
• Karels, G., & Prakash, A. J. (1987). Multivariate normality and forecasting of business bankruptcy. Journal of Business Finance & Accounting, 14(4), 573-593. https://doi.org/10.1111/j.1468-5957.1987.tb00113.x
• Keasey, K., & Watson, R. (1986). The prediction of small company failure: some behavioural evidence for the UK. Accounting and Business Research, 17(65), 49-57. https://doi.org/10.1080/00014788.1986.9729781
• Keasey, K., & Watson, R. (1987). Non-financial symptoms and the prediction of small company failure: a test of Argenti's hypotheses. Journal of Business Finance & Accounting, 14(3), 335-354. https://doi.org/10.1111/j.1468-5957.1987.tb00099.x
• Keasey, K., & Watson, R. (1991a). The state of the art of small firm failure prediction: achievements and prognosis. International Small Business Journal, 9(4), 11-29. https://doi.org/10.1177/026624269100900401
• Keasey, K., & Watson, R. (1991b). Financial distress prediction models: a review of their usefulness. British Journal of Management, 2(2), 89-102. https://doi.org/10.1111/j.1467-8551.1991.tb00019.x
• Kirkos, E. (2015). Assessing methodologies for intelligent bankruptcy prediction. Artificial Intelligence Review, 43(1), 83-123. https://doi.org/10.1007/s10462-012-9367-6
• Kim, M. J., & Kang, D. K. (2010). Ensemble with neural networks for bankruptcy prediction. Expert Systems with Applications, 37, 3373-3379. https://doi.org/10.1016/j.eswa.2009.10.012
• Kim, H., Cho, H., & Ryu, D. (2020). Corporate default predictions using machine learning: literature review. Sustainability, 12(16), 6325. https://doi.org/10.3390/su12166325
• Kücher, A., Feldbauer-Durstmüller, B., & Duller, C. (2015). The intellectual foundations of business failure: a co-citation analysis. Journal of International Business and Economics, 15(2), 13-38. https://doi.org/10.18374/JIBE-15-2.2
• Kücher, A., Mayr, S., Mitter, C., Duller, C., & Feldbauer-Durstmüller, B. (2018). Firm age dynamics and causes of corporate bankruptcy: age dependent explanations for business failure. Review of Managerial Science, 14, 633-661. https://doi.org/10.1007/s11846-018-0303-2
• Kumar, P. R., & Ravi, V. (2007). Bankruptcy prediction in banks and firms via statistical and intelligent techniques-A review. European Journal of Operational Research, 180(1), 1-28. https://doi.org/10.1016/j.ejor.2006.08.043
• La Porta, R. , Lopez-de-Silanes, F., Shleifer, A., & Vishny, R. W. (1998). Law and finance. Journal of Political Economy, 106(6), 1113-1155. https://doi.org/10.1086/250042
• La Porta, R., Lopez-de-Silanes, F., & Shleifer, A. (2013). Law and finance after a decade of research. In George M. Constantinides, Milton Harris, & Rene M. Stulz (editors), Handbook of the Economics of Finance (vol. 2, pp. 425-491). Amsterdam, Netherlands: Elsevier. https://doi.org/10.1016/B978-0-44-453594-8.00006-9
• Laitinen, E. K. (1991). Financial ratios and different failure processes. Journal of Business Finance and Accounting, 18(5), 649-674. https://doi.org/10.1111/j.1468-5957.1991.tb00231.x
• Laitinen, E. K. (1992). Prediction of failure of a newly founded firm. Journal of Business Venturing, 7(4), 323-340. https://doi.org/10.1016/0883-9026(92)90005-C
• Laitinen, E. K. (1993). Financial predictors for different phases of the failure process. Omega, 21(2), 215-228. https://doi.org/10.1016/0305-0483(93)90054-O
• Laitinen, E. K. (1994). Traditional versus operating cash flow in failure prediction. Journal of Business Finance and Accounting, 21(2), 195-217. https://doi.org/10.1111/j.1468-5957.1994.tb00313.x
• Laitinen, E. K. (1995). The duality of bankruptcy process in Finland. European Accounting Review, 4(3), 433-454. https://doi.org/10.1080/09638189500000027
• Laitinen E. K. (1999). Predicting a corporate credit analyst's risk estimate by logistic and linear models. International Review of Financial Analysis, 8(2), 97-121. https://doi.org/10.1016/S1057-5219(99)00012-5
• Laitinen, T., & Kankaanpää, M. (1999). Comparative analysis of failure prediction methods: the Finnish Case. European Accounting Review, 8(1), 67-92. https://doi.org/10.1080/096381899336159
• Laitinen, E. K., & Suvas, A. (2013). International applicability of corporate failure risk models based on financial statement information: comparisons across European countries. Journal of Finance & Economics, 1(3), 1-26. https://doi.org/10.12735/jfe.v1i3p01
• Laitinen, E. K., & Lukason, O. (2014). Do firm failure processes differ across countries: evidence from Finland and Estonia. Journal of Business Economics and Management, 15(5), 810-832. https://doi.org/10.3846/16111699.2013.791635
• Laitinen, E. K., & Suvas, A. (2016a). The effect of national culture on financial distress prediction modelling: evidence from European countries. International Journal of Accounting & Finance, 6(4), 299-318. https://doi.org/10.1504/IJAF.2016.10003295
• Laitinen, E. K., & Suvas, A. (2016b). Financial distress prediction in an international context: Moderating effects of Hofstede's original cultural dimensions. Journal of Behavioral and Experimental Finance, 9, 98-118. https://doi.org/10.1016/j.jbef.2015.11.003
• Lau, A. H. L. (1987). A five-state financial distress prediction model. Journal of Accounting Research, 25(1), 127-138. https://doi.org/10.2307/2491262
• Lensberg, T., Eilifsen, A., & McKee, T. E. (2006). Bankruptcy theory development and classification via genetic programming. European Journal of Operational Research, 169(2), 677-697. https://doi.org/10.1016/j.ejor.2004.06.013
• Lev, B., & Zarowin, P. (1999). The boundaries of financial reporting and how to extend them. Journal of Accounting Research, 37(2), 353-385. https://doi.org/10.2307/2491413
• Liang, D., Tsai, C. F., Lu, H. Y. R., & Chang, L. S. (2020). Combining corporate governance indicators with stacking ensembles for financial distress prediction. Journal of Business Research, 120, 137-146. https://doi.org/10.1016/j.jbusres.2020.07.052
• Lukason, O. (2016). Characteristics of firm failure processes in an international context (Doctoral dissertation). Estonia: University of Tartu. Lukason, O., Laitinen, E. K., & Suvas, A. (2016). Failure processes of young manufacturing micro firms in Europe. Management Decision, 54(8), 1966-1985. https://doi.org/10.1108/MD-07-2015-0294
• Luoma, M., & Laitinen, E. K. (1991). Survival analysis as a tool for company failure prediction. Omega, 19(6), 673-678. https://doi.org/10.1016/0305-0483(91)90015-L
• Laitinen, E. K., & Laitinen, T. (2000). Bankruptcy prediction: Application of the Taylor's expansion in logistic regression. International Review of Financial Analysis, 9(4), 327-349.
• Lussier, R. N. (1995). A nonfinancial business success versus failure prediction model. Journal of Small Business Management, 33(1), 8-20.
• Makropoulos, A., Weir, C. & Zhang, X. (2020). An analysis of the determinants of failure processes in UK SMEs. Journal of Small Business and Enterprise Development, 27(3), 405-426. https://doi.org/10.1108/JSBED-07-2019-0223
• Marais, M. L., Patell, J. M., & Wolfson, M. A. (1984). The experimental design of classification models: an application of recursive partitioning and bootstrapping to commercial bank loan classifications. Journal of Accounting Research, 22, 87-114. https://doi.org/10.2307/2490861
• McDonald, R. (2002). Derivative Markets. Boston, MA. USA: Addison Wesley. Mellahi, K., & Wilkinson, A. (2004). Organizational failure: a critique of recent research and a proposed integrative framework. International Journal of Management Reviews, 5(1), 21-41. https://doi.org/10.1111/j.1460-8545.2004.00095.x
• Metaxiotis, K., & Psarras, J. (2003). Applying knowledge management in higher education: The creation of a learning organisation. Journal of Information & Knowledge Management, 2(04), 353-359. https://doi.org/10.1142/S0219649203000541
• Micha, B. (1984). Analysis of business failures in France. Journal of Banking & Finance, 8(2), 281-291. https://doi.org/10.1016/0378-4266(84)90008-6
• Min, S. H., Lee, J., & Han, I. (2006). Hybrid genetic algorithms and support vector machines for bankruptcy prediction. Expert Systems with Applications, 31(3), 652-660. https://doi.org/10.1016/j.eswa.2005.09.070
• Morris, R. (1997). Early Warning Indicators of Corporate Failure: A Critical Review of Previous Research and Further Empirical Evidence. London, UK: Routledge.
• Moynihan, G. P., Jain, V., McLeod, R. W., & Fonseca, D. J. (2006). An expert system for financial ratio analysis. International Journal of Financial Services Management, 1(2-3), 141-154. https://doi.org/10.1504/IJFSM.2006.009622
• Moses, D., & Liao, S. S. (1987). On developing models for failure prediction. Journal of Commercial Bank Lending, 69(7), 27-38.
• Muñoz-Izquierdo, N., Segovia-Vargas, M. J., Camacho-Miñano, M. M., & Pascual-Ezama, D. (2019a). Explaining the causes of business failure using audit report disclosures. Journal of Business Research, 98, 403-414. https://doi.org/10.1016/j.jbusres.2018.07.024
• Muñoz-Izquierdo, N., Camacho-Miñano, M. M., Segovia-Vargas, M. J., & Pascual-Ezama, D. (2019b). Is the external audit report useful for bankruptcy prediction? Evidence using artificial intelligence. International Journal of Financial Studies, 7(2), 1-23. https://doi.org/10.3390/ijfs7020020
• Muñoz-Izquierdo, N., Laitinen, E. K., Camacho-Miñano, M. M., & Pascual-Ezama, D. (2020). Does audit report information improve financial distress prediction over Altman's traditional Z-Score model? Journal of International Financial Management & Accounting, 31(1), 65-97. https://doi.org/10.1111/jifm.12110
• Nwogugu, M. (2007). Decision-making, risk and corporate governance: a critique of methodological issues in bankruptcy/recovery prediction models. Applied Mathematics and Computation, 185(1), 178-196. https://doi.org/10.1016/j.amc.2005.11.178
• Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109-131. https://doi.org/10.2307/2490395
• Ooghe, H., & Joos, P. (1990). Failure prediction, explanation of misclassifications and incorporation of other relevant variables: result of empirical research in Belgium. Working paper, Department of Corporate Finance, Ghent University (Belgium).
• Ooghe, H., Joos, P., & De Bourdeaudhuij, C. (1995). Financial distress models in Belgium: the results of a decade of empirical research. International Journal of Accounting, 30, 245-274.
• Ooghe, H., & Balcaen, S. (2007). Are failure prediction models widely usable? An empirical study using a Belgian dataset. Multinational Finance Journal, 11(1/2), 33-76.
• Ooghe, H., & De Prijcker, S. (2008). Failure processes and causes of company bankruptcy: a typology. Management Decision, 46(2), 223-242. https://doi.org/10.1108/00251740810854131
• Pavaloaia, V. D. (2009). Web based application for SMEs economic and financial diagnose. Communications of the IBIMA, 9, 24-30.
• Peres, C., & Antão, M. (2017). The use of multivariate discriminant analysis to predict corporate bankruptcy: A review. Aestimatio: The IEB International Journal of Finance, 14, 108-131.
• Perez, M. (2006). Artificial neural networks and bankruptcy forecasting: a state of the art. Neural Computing & Applications, 15(2), 154-163. https://doi.org/10.1007/s00521-005-0022-x
• Petpairote, W., & Chancharat, N. (2016). Corporate governance, a shield of bankruptcy. Journal of Applied Economic Sciences, 11(3), 532-537.
• Piñeiro-Sánchez, C., de Llanos-Monelos, P., & Rodríguez-López, M. (2012). La evaluación de la probabilidad de fracaso financiero. Contraste empírico del contenido informacional de la auditoría de cuentas. [Evaluation of the likelihood of financial failure. Empirical contrast of the informational content audit of accounts]. Spanish Journal of Finance and Accounting, 41(156), 565-587. https://doi.org/10.1080/02102412.2012.10779736
• Pompe, P. P. M., & Bilderbeek J. (2005), Bankruptcy prediction: The influence of the year prior to failure selected for model building and the effects in a period of economic decline. Intelligent Systems in Accounting, Finance and Management, 13, 95-112. https://doi.org/10.1002/isaf.259
• Rose, P. S., Andrews, W. T., & Giroux, G. A. (1982). Predicting business failure: a macroeconomic perspective. Journal of Accounting, Auditing and Finance, 6(1), 20-31.
• Rosner, R. L. (2003). Earnings manipulation in failing firms. Contemporary Accounting Research, 20(2), 361-408. https://doi.org/10.1506/8EVN-9KRB-3AE4-EE81
• Routledge, J., & Gadenne, D. (2000). Financial distress, reorganization and corporate performance. Accounting & Finance, 40(3), 233-259. https://doi.org/10.1111/1467-629X.00046
• Santomero, A., & Vinso, J. (1977). Estimating the probability of failure for firms in the banking system. Journal of Banking and Finance, 1(2), 185-205. https://doi.org/10.1016/0378-4266(77)90006-1
• Scapens, R. W., Ryan, R. J., & Fletcher, L. (1981). Explaining corporate failure: a catastrophe theory approach: explaining corporate failure. Journal of Business Finance & Accounting, 8(1), 1-26. https://doi.org/10.1111/j.1468-5957.1981.tb00800.x
• Scott, J. (1981). The probability of bankruptcy: a comparison of empirical predictions and theoretical models. Journal of Banking and Finance, 5(3), 317-344. https://doi.org/10.1016/0378-4266(81)90029
• Scherger, V., Vigier, H. P., Terceño-Gómez, A., & Barberà-Mariné, M. G. (2015). Goodness of aggregation operators in a diagnostic fuzzy model of business failure. In J. Gil-Aluja, A. Terceño-Gómez, J. C. Ferrer-Comalat, J. M. Merigó-Lindahl, & S. Linares-Mustarós (Eds.), Scientific Methods for the Treatment of Uncertainty in Social Sciences (pp. 141-157). Cham, Switzerland: Springer. https://doi.org/10.1007/978-3-319-19704-3_12
• Scherger, V., Terceño, A., & Vigier, H. (2019). A systematic overview of the prediction of business failure. International Journal of Technology, Policy and Management, 19(2), 196-211. https://doi.org/10.1504/IJTPM.2019.100601
• Sharma, D. (2001). The role of cash flow information in predicting corporate failure: the state of the literature. Managerial Finance, 27(4), 3-28. https://doi.org/10.1108/03074350110767114
• Shi, Y., & Li, X. (2019). An overview of bankruptcy prediction models for corporate firms: a systematic literature review. Intangible Capital, 15(2), 114-127. https://doi.org/10.3926/ic.1354
• Shin, K. S., & Lee, Y. J. (2002). A genetic algorithm application in bankruptcy prediction modelling. Expert Systems with Applications, 23(3), 321-328. https://doi.org/10.1016/S0957-4174(02)00051-9
• Shiue, W., Li, S. T., & Chen, K. J. (2008). A frame knowledge system for managing financial decision knowledge. Expert Systems with Applications, 35(3), 1068-1079. https://doi.org/10.1016/j.eswa.2007.08.035
• Stanford Encyclopedia of Philosophy (2017). Theory and Observation in Science. https://plato.stanford.edu/entries/science-theory-observation/
• Storey, D. J., Keasey, K., Watson, R., & Wynarczyk, P. (2016). The performance of small firms: profits, jobs and failures. New York, USA: Routledge.
• Subramanyam, K. R. (1996). The pricing of discretionary accruals. Journal of Accounting and Economics, 22(1-3), 249-281. https://doi.org/10.1016/S0165-4101(96)00434-X
• Sun, J., He, K. Y., & Li, H. (2011). SFFS-PC-NN optimized by genetic algorithm for dynamic prediction of financial distress with longitudinal data streams. Knowledge-Based Systems, 24(7), 1013-1023. https://doi.org/10.1016/j.knosys.2011.04.013
• Sun, J., Li, H., Huang, Q. H., & He, K. Y. (2014). Predicting financial distress and corporate failure: a review from the state-of-the-art definitions, modelling, sampling, and featuring approaches. Knowledge-Based Systems, 57, 41-56. https://doi.org/10.1016/j.knosys.2013.12.006
• Sun, L., & Shenoy, P. P. (2007). Using Bayesian networks for bankruptcy prediction: some methodological issues. European Journal of Operational Research, 180(2), 738-753. https://doi.org/10.1016/j.ejor.2006.04.019
• Sweeney, A. P. (1994). Debt-covenant violations and managers' accounting responses. Journal of Accounting and Economics, 17(3), 281-308. https://doi.org/10.1016/0165-4101(94)90030-2
• Taffler, R. J. (1982). Forecasting company failure in the UK using discriminant analysis and financial ratio data. Journal of the Royal Statistical Society. Series A (General), 342-358. https://doi.org/10.2307/2981867
• Taffler, R. J. (1983). The assessment of company solvency and performance using a statistical model, Accounting and Business Research, 13(52), 295-308. https://doi.org/10.1080/00014788.1983.9729767
• Tascón, M., & Castaño, F. J. (2012). Variables and models for the identification and prediction of business failure: revision of recent empirical research advances. Spanish Accounting Review, 15(1), 7-58. https://doi.org/10.1016/S1138-4891(12)70037-7
• Thornhill, S., & Amit, R. (2003). Learning about failure: bankruptcy, firm age, and the resource-based view. Organization Science, 14(5), 497-509. https://www.jstor.org/stable/4135145
• Tseng, F. M., & Hu, Y. C. (2010). Comparing four bankruptcy prediction models: logit, quadratic interval logit, neural and fuzzy neural networks. Expert Systems with Applications, 37(3), 1846-1853. https://doi.org/10.1016/j.eswa.2009.07.081
• Vassalou, M., & Xing, Y. (2004). Default risk on equity returns. The Journal of Finance, 69(2), 831-868. https://doi.org/10.1111/j.1540-6261.2004.00650.x
• Veganzones, D. & Severin, E. (2021). Corporate failure prediction models in the twenty-first century: a review. European Business Review, 33(2), 204-226. https://doi.org/10.1108/EBR-12-2018-0209
• Vel, R., & Zala, P. (2019). Bankruptcy prediction using multivariate discriminant analysis - Empirical evidence from cases referred to NCLT. International Journal of Innovative Technology and Exploring Engineering, 8(9), 13-17. https://doi.org/10.35940/ijitee.I7496.078919
• Waqas, H., & Md-Rus, R. (2018). Predicting financial distress: importance of accounting and firm-specific market variables for Pakistan's listed firms. Cogent Economics & Finance, 6(1), 1545739. https://doi.org/10.1080/23322039.2018.1545739
• Walter, J. (1957). Determination of technical insolvency. Journal of Business, 40, 30-43.
• White, H. C. (1981). Where do markets come from? American Journal of Sociology, 87(3), 517-547. http://www.jstor.org/stable/2778933
• White, M. J. (1983). Bankruptcy liquidation and reorganization. New York University Graduate School of Business Administration. Available at https://w4.stern.nyu.edu/finance/docs/pdfs/Outlines/2018-1/1801-b403198-Kovensky.pdf
• White, M. (1989). The corporate bankruptcy decision. Journal of Economic Perspectives, 3, 129-152. https://doi.org/10.1257/jep.3.2.129
• Wilcox, J. (1971). A simple theory of financial ratios as predictors of failure. Journal of Accounting Research, 9(2), 389-395. https://doi.org/10.2307/2489944
• Wilcox, J. (1973). A prediction of business failure using accounting data. Empirical Research in Accounting: Selected Studies, 163-190. https://doi.org/10.2307/2490035
• Wilcox, J. (1976). The gambler's ruin approach to business risk. Sloan Management Review, 18(1), 33-46.
• Wilson, N., Summers, B., & Hope, R. (2000). Using payment behaviour data for credit risk modelling. International Journal of the Economics of Business, 7(3), 333-346. https://doi.org/10.1080/13571510050197230
• Wu, W. W. (2010). Beyond business failure prediction. Expert Systems with Applications, 37(3), 2371-2376. https://doi.org/10.1016/j.eswa.2009.07.05
• Xu, W., Xiao, Z., Dang, X., Yang, D., & Yang, X. (2014). Financial ratio selection for business failure prediction using soft set theory. Knowledge-Based Systems, 63, 59-67. https://doi.org/10.1016/j.knosys.2014.03.007
• Xu, M., & Zhang, C. (2009). Bankruptcy prediction: the case of Japanese listed companies. Review of Accounting Studies, 14(4), 534-558. https://doi.org/10.1007/s11142-008-9080-5
• Zavgren, C. V. (1983). The prediction of corporate failure: the state of the art. Journal of Accounting Literature, 1, 1-38.
• Zavgren, C. V. (1985). Assessing the vulnerability to failure of American industrial firms: a logistic analysis. Journal of Business Finance and Accounting, 12(1), 19-45. https://doi.org/10.1111/j.1468-5957.1985.tb00077.x
• Zavgren, C. V., & Friedman, G. E. (1988). Are bankruptcy prediction models worthwhile? An application in securities analysis. Management International Review, 28(1), 34-44. https://www.jstor.org/stable/40227870
• Zięba, M., Tomczak, S. K., & Tomczak, J. M. (2016). Ensemble boosted trees with synthetic features generation in application to bankruptcy prediction. Expert Systems with Applications, 58, 93-101. https://doi.org/10.1016/j.eswa.2016.04.001
• Zmijewski, M. E. (1984). Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting Research,22, 59-82. https://doi.org/10.2307/2490859