Revisión de las limitaciones de la investigación sobre predicción de quiebras financieras

A review of the limitations of financial failure prediction research

Autores/as

DOI: https://doi.org/10.6018/rcsar.453041
Palabras clave: Dificultades financieras, Quiebra financiera, Bancarrota, Predicción, Limitaciones

Agencias de apoyo

  • This work was supported by the Complutense University of Madrid and Banco Santander under Grant REF PR87/19-22586.

Resumen

El objetivo de este artículo es evaluar críticamente los principales puntos débiles asociados a las limitaciones de los estudios de investigación sobre predicción de quiebras financieras. Durante más de 80 años, los investigadores han estudiado sin éxito la forma de crear una teoría general del fracaso financiero que sea útil para la predicción. En este artículo, revisamos los principales límites de la investigación sobre predicción de quiebras mediante una evaluación crítica de trabajos anteriores y nuestro propio enfoque a partir de la experiencia investigadora. Nuestras conclusiones corroboran que estos estudios adolecen de una falta de investigación teórica y dinámica, una definición poco clara del fracaso, deficiencias con la calidad de los datos de los estados financieros y un déficit en los análisis de diagnóstico del fracaso. También se esbozan las implicaciones más relevantes para futuras investigaciones en este ámbito. Se trata del primer estudio que analiza en profundidad las salvedades de los estudios de predicción de la quiebra financiera, un tema crucial en la actualidad debido a los atisbos de crisis económica provocados por la pandemia del Covid-19.

Descargas

Los datos de descargas todavía no están disponibles.

Citas

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., 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. https://doi.org/10.1016/j.accinf.2016.12.004

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

Publicado
01-07-2023
Cómo citar
Laitinen, E. K., Camacho-Miñano, M.- del-M., & Muñoz-Izquierdo, N. (2023). Revisión de las limitaciones de la investigación sobre predicción de quiebras financieras: A review of the limitations of financial failure prediction research. Revista de Contabilidad - Spanish Accounting Review, 26(2), 255–273. https://doi.org/10.6018/rcsar.453041
Número
Sección
Artículos