Investigation of financial distress with a dynamic logit based on the linkage between liquidity and profitability status of listed firms

References

• Alfaro, E., García, N., Gámez, M., & Elizondo, D. (2008). Bankruptcy forecasting: An empirical comparison of AdaBoost and neural networks. Decisions Support Systems, 45, 110–122.10.1016/j.dss.2007.12.002
   Web of Science ®Google Scholar
• Altman, E. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589–609.10.1111/j.1540-6261.1968.tb00843.x
   Web of Science ®Google Scholar
• Altman, E. I. (1977). ZETA analysis a new model to identify bankruptcy risk of corporations. Journal of Banking & Finance, 1(1), 29–54.10.1016/0378-4266(77)90017-6
   Google Scholar
• Altman, E., & Hotchkiss, E. (2006). Corporate financial distress and bankruptcy: predict and avoid bankruptcy, analyze and invest in distressed debt (3rd ed.). Hoboken, NJ: Wiley Finance.
   Google Scholar
• Amemiya, Y. (1985). Instrumental variable estimator for the nonlinear errors-in-variables model. Journal of Econometrics, 28(3), 273–289.
   Web of Science ®Google Scholar
• Baranoff, E. G., Sager, T. W., & Shively, T. S. (2000). A semiparametric stochastic spline model as a managerial tool for potential insolvency. Journal of Risk and Insurance, 67(3), 369–396.10.2307/253834
   Web of Science ®Google Scholar
• Bauer, J., & Agarwal, V. (2014). Are hazard models superior to traditional bankruptcy prediction approaches? A comprehensive test. Journal of Banking & Finance, 40, 432–442.10.1016/j.jbankfin.2013.12.013
   Web of Science ®Google Scholar
• Beaver, W. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 4, 71–111.10.2307/2490171
   Web of Science ®Google Scholar
• Beaver, W. H., McNichols, M. F., & Rhie, J.-W. (2005). Have financial statements become less informative? Evidence from the ability of financial ratios to predict bankruptcy. Review of Accounting Studies, 10, 93–122.10.1007/s11142-004-6341-9
   Web of Science ®Google Scholar
• Beynon, M. J., & Peel, M. J. (2001). Variable precision rough set theory and data discretisation: An application to corporate failure prediction. Omega, 29, 561–576.10.1016/S0305-0483(01)00045-7
   Web of Science ®Google Scholar
• Bose, I. (2006). Deciding the financial health of dot-coms using roughsets. Journal Information and Management, 43(7), 835–846.10.1016/j.im.2006.08.001
   Web of Science ®Google Scholar
• Bose, I., & Pal, R. (2006). Predicting the survival or failure of click-and-mortar corporations: A knowledge discovery approach. European Journal of Operational Research, 174(2), 959–982.10.1016/j.ejor.2005.05.009
   Web of Science ®Google Scholar
• Campbell, J. Y., Hilscher, J., & Szilagyi, J. (2008). In search of distress risk. The Journal of Finance, 63(6), 2899–2939.10.1111/j.1540-6261.2008.01416.x
   Web of Science ®Google Scholar
• Campbell, J. Y., Hilscher, J., & Szilagyi, J. (2011). Predicting financial distress and the performance of distressed stocks. Journal of Investment Management, 9(2), 14–34.
   Google Scholar
• Charitou, A., Neophytou, E., & Charalambous, C. (2004). Predicting corporate failure: Empirical evidence for the UK. European Accounting Review, 13(3), 465–497.10.1080/0963818042000216811
   Google Scholar
• Chava, S., & Jarrow, R. A. (2004). Bankruptcy prediction with industry effects. Review of Finance, 8, 537–569.
   Google Scholar
• Chen, P., & Wu, C. (2014). Default prediction with dynamic sectoral and macroeconomic frailties. Journal of Banking & Finance, 40, 211–226.10.1016/j.jbankfin.2013.11.036
   Web of Science ®Google Scholar
• Chiang Yeh, C., Jang Chi, D., & Fu Hsu, M. (2010). A hybrid approach of DEA, rough set and support vector machines for business failure prediction. Expert Systems with Applications, 37, 1535–1541.
   Web of Science ®Google Scholar
• Christidis, A.-Y., & Gregory, A. (2010). Some new models for financial distress prediction in the UK. Xfi Centre for Finance and Investment, Discussion Paper No. 10/04.
   Google Scholar
• Cielen, A., Peeters, L., & Vanhoof, K. (2004). Bankruptcy prediction using a data envelopment analysis. European Journal of Operational Research, 154, 526–532.10.1016/S0377-2217(03)00186-3
   Web of Science ®Google Scholar
• Courtis, J. K. (1978). Modelling a financial ratios categoric framework. Journal of Business Finance and Accounting, 5(4), 371–386.10.1111/jbfa.1978.5.issue-4
   Google Scholar
• Derni, A., & Crucifix, F. (1992). Symptômes de défaillance et stratégies pour le redressement d’entreprise [Symptoms of failure and strategies for business recovery]. Louvain-La-Neuve: Académia.
   Google Scholar
• Dimitras, A., Zanakis, S., & Zopounidis, C. (1996). A survey of business failures with an emphasis on prediction methods and industrial applications. European Journal of Operational Research, 90(3), 487–513.10.1016/0377-2217(95)00070-4
   Web of Science ®Google Scholar
• Dimitras, A. I., Slowinski, R., Susmaga, R., & Zopounidis, C. (1999). Business failure prediction using rough sets. European Journal of Operational Research, 114, 263–280.10.1016/S0377-2217(98)00255-0
   Web of Science ®Google Scholar
• Doumpos, M., & Zopounidis, C. (1999). A multicriteria discrimination method for the prediction of financial distress: The case of Greece. Multinational Finance Journal, 3(2), 71–101.10.17578/3
   Google Scholar
• Eljelly, Α. M. A. (2004). Liquidity – profitability tradeoff: An empirical investigation in an emerging market. International Journal of Commerce and Management, 14(2), 48–61.10.1108/10569210480000179
   Google Scholar
• Foster, G. (1986). Financial statements analysis. London: Prentice Hall International Editions.
   Google Scholar
• Frydman, H., Altman, E. I., Kao, D.-L. (1985). Introducing recursive partitioning for financial classification: The case of financial distress. The Journal of Finance, XL(1), 269–491.10.1111/j.1540-6261.1985.tb04949.x
   Google Scholar
• Habib, A., Bhuiyan, B., & Islam, A. (2013). Financial distress, earnings management and market pricing of accruals during the global financial crisis. Managerial Finance, 39(2), 155–180.10.1108/03074351311294007
   Web of Science ®Google Scholar
• Hol, S. (2007). The influence of the business cycle on bankruptcy probability. International Transactions in Operational Research, 14, 75–90.10.1111/itor.2007.14.issue-1
   Google Scholar
• Kabir, C., Tesfamariam, S., & Sadiq, R. (2015). Predicting water main failures using Bayesian model averaging and survival modeling approach. Reliability Engineering and System Safety, 142, 498–514.10.1016/j.ress.2015.06.011
   Web of Science ®Google Scholar
• Keasey, K., & Watson, R. (1991). Financial distress prediction models: A review of their usefulness. British Journal of Management, 2, 89–102.10.1111/bjom.1991.2.issue-2
   Google Scholar
• Kim, C. S., Mauer, D. C., & Sherman, A. E. (1998). The determinants of corporate liquidity: Theory and evidence. The Journal of Financial and Quantitative Analysis, 33(3), 335–359.10.2307/2331099
   Web of Science ®Google Scholar
• Koenig, G. (1985). Entreprises en difficultés: Des symptômes aux remèdes [Companies in distress: Symptoms to remedies]. Revue Francaise de Gestion [French Review of Management], 50, 84–92.
   Google Scholar
• Laitinen, T., & Kankaanpää, M. (1999). Comparative analysis of failure prediction methods: The Finnish case. The European Accounting Review, 8(1), 67–92.10.1080/096381899336159
   Google Scholar
• Lane, W., Looney, S., & Wansley, J. (1986). An application of the cox proportional hazards model to bank failure. Journal of Banking & Finance, 10(4), 511–531.10.1016/S0378-4266(86)80003-6
   Web of Science ®Google Scholar
• Lee, M.-C. (2014). Business bankruptcy prediction based on survival analysis approach. International Journal of Computer Science & Information Technology, 6(2), 103–119.10.5121/ijcsit
   Google Scholar
• Liang, D., Lu, C. C., Tsai, C. F., & Shih, G. A. (2016). Financial ratios and corporate governance indicators in bankruptcy prediction: A comprehensive study. European Journal of Operational Research, 252, 561–572.10.1016/j.ejor.2016.01.012
   Web of Science ®Google Scholar
• Lin, F. U., & McClean, S. (2001). A data mining approach to the prediction of corporate failure. Knowledge-Based Systems, 14, 189–195.10.1016/S0950-7051(01)00096-X
   Web of Science ®Google Scholar
• Lin, T. (2009). A cross model study of corporate financial distress prediction in Taiwan: Multiple discriminant analysis, logit, probit and neural networks models. Neurocomputing, 72(16), 3507–3516.10.1016/j.neucom.2009.02.018
   Web of Science ®Google Scholar
• López Iturriaga, F. J., & Pastor Sanz, I. (2015). Bankruptcy visualization and prediction using neural networks: A study of U.S. commercial banks. Expert Systems with Applications, 42, 2857–2869.10.1016/j.eswa.2014.11.025
   Web of Science ®Google Scholar
• Luoma, M., & Laitinen, E. (1991). Survival analysis as a tool for company failure prediction. Omega International Journal of Management Science, 19(6), 673–678.10.1016/0305-0483(91)90015-L
   Web of Science ®Google Scholar
• Maio, F., Hu, J., Tse, P., Pecht, M., Tsui, K., & Zio, E. (2012). Ensemble-approaches for clustering health status of oil sand pumps. Expert Systems with Applications, 39, 4847–4859.10.1016/j.eswa.2011.10.008
   Web of Science ®Google Scholar
• Mateos Ronco, A., & Lopez Mas, A. (2011). Developing a business failure prediction model for cooperatives: Results of an empirical study in Spain. African Journal of Business Management, 5(26), 10565–10576.
   Google Scholar
• Mousavi, M. M., Ouenniche, J., & Xu, B. (2015). Performance evaluation of bankruptcy prediction models: An orientation-free super-efficiency DEA-based framework. International Review of Financial Analysis, 42, 64–75.10.1016/j.irfa.2015.01.006
   Web of Science ®Google Scholar
• Niemann, M., Schmidt, J. H., & Neukirchen, M. (2008). Improving performance of corporate rating prediction models by reducing financial ratio heterogeneity. Journal of Banking & Finance, 32, 434–446.10.1016/j.jbankfin.2007.05.015
   Web of Science ®Google Scholar
• Ohlson, J. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109–131.10.2307/2490395
   Web of Science ®Google Scholar
• Parker, S., Peters, G., & Turetsky, H. (2002). Corporate governance and corporate failure: A survival analysis. Corporate Governance, 2(2), 4–12.10.1108/14720700210430298
   Google Scholar
• Pereira, J. (2014). Survival analysis employed in predicting corporate failure: A forecasting model proposal. International Business Research, 7(5), 9–20.
   Google Scholar
• Platt, H. D., & Platt, M. B. (2002). Predicting corporate financial distress: Reflections on choice-based sample bias. Journal of Economics and Finance, 26(2), 184–199.10.1007/BF02755985
   Google Scholar
• Polikar, R. (2006). Ensemble based systems in decision making. IEEE Circuits and Systems Magazine, 6(3), 21–45.10.1109/MCAS.2006.1688199
   Google Scholar
• Rahman, S., Tan, L. H., Hew, O. L., & Tan, Y. S. (2004). Identifying financial distress indicators of selected banks in Asia. Asian Economic Journal, 18(1), 45–57.10.1111/asej.2004.18.issue-1
   Google Scholar
• Rokach, L. (2010). Ensemble-based classifiers. Artificial Intelligence Review, 33, 1–39. doi:10.1007/s10462-7
   Web of Science ®Google Scholar
• Rose, P., Andrews, W., & Giroux, G. (1982). Predicting business failure: A macroeconomic perspective. Journal of Accounting and Finance, 6(1), 20–31.
   Google Scholar
• Shumway, T. (2001). Forecasting bankruptcy more accurately: A simple hazard model. The Journal of Business, 74(1), 101–124.10.1086/jb.2001.74.issue-1
   Web of Science ®Google Scholar
• Simar, L., & Wilson, P. (2007). Estimation and inference in two-stage, semi-parametric models of production processes. Journal of Econometrics, 136, 31–64.10.1016/j.jeconom.2005.07.009
   Web of Science ®Google Scholar
• Sinkey, J. F. (1975). A multivariate statistical analysis of the characteristics of problem banks. Journal of Finance, 30(1), 21–36.10.1111/j.1540-6261.1975.tb03158.x
   Web of Science ®Google Scholar
• Stein, R. M. (2007). Benchmarking default prediction models: Pitfalls and remedies in model validation. Journal of Risk Model Validation, 1, 77–113.10.21314/JRMV.2007.002
   Google Scholar
• 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, modeling, sampling, and featuring approaches. Knowledge-Based Systems, 57, 41–56.10.1016/j.knosys.2013.12.006
   Web of Science ®Google Scholar
• Tinoco, M. H., & Wilson, N. (2013). Financial distress and bankruptcy prediction among listed companies using accounting, market and macroeconomic variables. International Review of Financial Analysis, 30, 394–419.10.1016/j.irfa.2013.02.013
   Web of Science ®Google Scholar
• Trujillo-Ponce, A., Samaniego_Medina, R., Cardone-Riportella, C. (2014). Examining what best explains corporate credit risk: Accounting-based versus market-based models. Journal of Business Economics and Management, 15(2), 253–276.10.3846/16111699.2012.720598
   Web of Science ®Google Scholar
• Van Horne, J. C. (1974). Financial management and policy. Englewood Cliffs, NJ: Perntice Hall Inc.
   Google Scholar
• Vogelenzang, P. (1980). Foreign Sovereign compulsion in American antitrust law. Stanford Law Review, 33(1), 131–153.10.2307/1228525
   Google Scholar
• Xu, X., & Wang, Y. (2009). Financial failure prediction using efficiency as a predictor. Expert Systems with Applications, 36(1), 366–373.10.1016/j.eswa.2007.09.040
   Web of Science ®Google Scholar
• Zanakis, S., & Zopounidis, C. (1997). Prediction of Greek company takeovers via multivariate analysis of financial ratios. Journal of the Operational Research Society, 48(7), 678–687.10.1057/palgrave.jors.2600401
   Web of Science ®Google Scholar
• Zopounidis, C. (1995). Évaluation du risque de défaillance de l’entreprise: méthodes et cas d’application. Paris: Economica, serie Techniques de Gestion.
   Google Scholar