Firm's default — new methodological approach and preliminary evidence from Poland

Main Article Content

Tomasz Berent
Bogusław Bławat
Marek Dietl
Przemysław Krzyk
Radosław Rejman

Abstract

Research background: Bankruptcy literature is populated with scores of (econometric) models ranging from Altman’s Z-score, Ohlson’s O-score, Zmijewski’s probit model to k-nearest neighbors, classification trees, support vector machines, mathematical programming, evolutionary algorithms or neural networks, all designed to predict financial distress with highest precision. We believe corporate default is also an important research topic to be identified with the prediction accuracy only. Despite the wealth of modelling effort, a unified theory of default is yet to be proposed.


Purpose of the article: Due to the disagreement both on the definition and hence the timing of default, as well as on the measurement of prediction accuracy, the comparison (of predictive power) of various models can be seriously misleading. The purpose of the article is to argue for the shift in research focus from maximizing accuracy to the analysis of the information capacity of predictors. By doing this, we may yet come closer to understanding default itself.


Methods: We critically appraise the bankruptcy research literature for its methodological variety and empirical findings. Default definitions, sampling procedures, in and out-of-sample testing and accuracy measurement are all scrutinized. In an empirical part, we use a double stochastic Poisson process with multi-period prediction horizon and a comprehensive database of some 15,000 Polish non-listed companies to illustrate the merits of our new approach to default modelling.


Findings & Value added: In the theoretical part, we call for the construction of a single unified default forecasting platform estimated for the largest dataset of firms possible to allow testing the utility of various sources of micro, mezzo, and macro information. Our preliminary empirical evidence is encouraging. The accuracy ratio amounts to 0.92 for t = 0 and drops to 0.81 two years ahead of default. We point to the pivotal role played by the information on firm’s liquidity (alternatively in profitability) and — in contrast to Altman’s tradition — hardly any contribution to predictive power of other financial ratios. Macro data is shown to be critical. It adds, on average, more than 10 p.p. to accuracy ratio.  In the future, we hope to integrate listed and non-listed firms data into one model, ideally at higher frequency than annual, and include the information on firm's competitiveness position.

Article Details

How to Cite
Berent, T., Bławat, B., Dietl, M., Krzyk, P., & Rejman, R. (2017). Firm’s default — new methodological approach and preliminary evidence from Poland. Equilibrium. Quarterly Journal of Economics and Economic Policy, 12(4), 753-773. https://doi.org/10.24136/eq.v12i4.39
Section
Bankruptcy prediction methods

References

Acharya, V. V.; Bharath, S. T., & Srinivasan, A. (2003). Understanding the recovery rates on defaulted securities. Centre for Economic Policy Research. doi: 10.2139/ssrn.442901.
Agarwal, V., & Taffler, R. (2008). Comparing the performance of market-based and accounting-based bankruptcy prediction models. Journal of Banking & Finance, 32(8). doi: 10.1016/j.jbankfin.2007.07.014.
Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23(4). doi: 10.1111/j.1540-6261.1968.tb00843.x.
Altman, E. I., & Sabato, G. (2007). Modelling credit risk for SMEs: evidence from the US market. Abacus, 43(3). doi: 10.1111/j.1467-6281.2007.00234.x.
Becerra, V. M., Galvão, R. K., & Abou-Seada, M. (2005). Neural and wavelet network models for financial distress classification. Data Mining and Knowledge Discovery, 11(1). doi: 10.1007/s10618-005-1360-0.
Berent, T., & Jasinowski, S. (2012). Financial leverage puzzle–preliminary conclusions from literature review. Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu, 271(1).
Berkovitch, E., Israel, R., & Zender, J. F. (1998). The design of bankruptcy law: a case for management bias in bankruptcy reorganizations. Journal of Financial and Quantitative Analysis, 33(04). doi: 10.2307/2331127.
Bhimani, A., Gulamhussen, M. A., & Lopes, S. D. R. (2013). The role of financial, macroeconomic, and non-financial information in bank loan default timing prediction. European Accounting Review, 22(4). doi: 10.1080/09638180.2013. 770967.
Black, F., & Scholes, M. (1973). The pricing of options and corporate liabilities. Journal of Political Economy, 81(3). doi: 10.1086/260062.
Boyarchenko, S. I., & Levendorskii, S. Z. (2002). Non-Gaussian Merton-Black-Scholes theory. Wspc. doi: 10.1142/4955.
Brigo, D., Pallavicini, A., & Torresetti, R. (2010). Credit models and the crisis: a journey into CDOs, copulas, correlations and dynamic models. Wiley: The Wiley Finance Series. doi: 10.1002/9781118374733.
Bławat, B. (2015). Modeling default with sector positioned variables. Global Credit Review (forthcoming).
Boritz, J., Kennedy, D., & Sun, J. (2007). Predicting business failures in Canada. Accounting Perspectives, 6. doi: 10.1506/G8T2-K05V-1850-52U4.
Chambers, R., Kokic, P., Smith, P., & Cruddas, M. (2000). Winsorization for identifying and treating outliers in business surveys. In Proceedings of the Second International Conference on Establishment Surveys.
Chava, S., & Jarrow, R. A. (2004). Bankruptcy prediction with industry effects. Review of Finance, 8(4). doi: 10.1093/rof/8.4.537.
Cherubini, U., Luciano, E., & Vecchiato, W. (2004). Copula methods in finance. Wiley. doi: 10.1002/9781118673331.
Coppens, F., González, F., & Winkler, G. (2007). The performance of credit rating systems in the assessment of collateral used in Eurosystem monetary policy operations. ECB Occasional Paper, 65. doi: 10.2139/ssrn.1687537.
Duan, J.-C., Sun, J., & Wang, T. (2012). Multiperiod corporate default prediction – a forward intensity approach. Journal of Econometrics, 170(1). doi: 10.1016/j.jeconom.2012.05.002
Duffie, D., & Lando, D. (2001). Term structures of credit spreads with incomplete accounting information. Econometrica, 69(3). doi: 10.1111/1468-0262.00208.
Duffie, D., Saita, L., & Wang, K. (2007). Multi-period corporate default prediction with stochastic covariates. Journal of Financial Economics, 83(3). doi: 10.1016/j.jfineco.2005.10.011.
Emel, A. B., Oral, M., Reisman, A., & Yolalan, R. (2003). A credit scoring approach for the commercial banking sector. Socio-Economic Planning Sciences, 37(2). doi: 10.1016/S0038-0121(02)00044-7.
Fernández E., & Olmeda I. (1995). Bankruptcy prediction with Artificial Neural Networks. In J. Mira & F. Sandoval (Eds). From natural to artificial neural computation. IWANN 1995. Lecture notes in computer science, vol 930. Berlin, Heidelberg: Springer. doi: 10.1007/3-540-59497-3_296.
Frey, R., & Schmidt, T. (2009). Pricing corporate securities under noisy asset information. Mathematical Finance, 19(3). doi: 10.1111/j.1467-9965.2009. 00374.x.
Galvao, R. K., Becerra, V. M., & Abou-Seada, M. (2004). Ratio selection for classification models. Data Mining and Knowledge Discovery, 8(2). doi: 10.1023/B:DAMI.0000015913.38787.b3.
Gray, S., Mirkovic, A., & Ragunathan, V. (2006). The determinants of credit ratings: Australian evidence. Australian Journal of Management, 31(2). doi: 10.1177/031289620603100208.
Grice, J. S., & Ingram, R. W. (2001). Tests of the generalizability of Altman’s bankruptcy prediction model. Journal of Business Research, 54. doi: 10.1016/S0148-2963(00)00126-0.
Hillegeist, S. A., Keating, E. K., Cram, D. P., & Lundstedt, K. G. (2004). Assessing the probability of bankruptcy. Review of Accounting Studies, 9(1). doi: 10.1023/B:RAST.0000013627.90884.b7.
Hirsa, A., & Neftçi, S. (2014). An introduction to the mathematics of financial derivatives. Elsevier.
Javaheri, A. (2005). Inside volatility arbitrage: the secrets of skewness. Wiley.
Karatzas, I., & Shreve, S. E. (1988). Brownian motion and stochastic calculus. Springer Verlag. doi: 10.1007/2F978-1-4684-0302-2.
Kim, B., Duan, J.-C., Kim, C., Kim, W., & Shin, D. (2012). Default probabilities and interest expenses of privately held firms. Technical report. Working Paper, Risk Management Institute at National University of Singapore.
Kim, H. S., & Sohn, S. Y. (2010). Support vector machines for default prediction of SMEs based on technology credit. European Journal of Operational Research 201(3). doi: 10.1016/j.ejor.2009.03.036.
Kingman, J. F. C. (1993). Poisson processes (Oxford studies in probability). Oxford University Press.
Lando, D. (1998). On Cox processes and credit risky securities. Review of Derivatives Research, 2(2-3). doi: 10.1007/BF01531332.
Lang, L. H., & Stulz, R. (1992). Contagion and competitive intra-industry effects of bankruptcy announcements: an empirical analysis. Journal of Financial Economics, 32(1). doi: 10.1016/0304-405X(92)90024-R.
Lieu, P.-T., Lin, C.-W., & Yu, H.-F. (2008). Financial early-warning models on cross-holding groups. Industrial Management & Data Systems, 108(8). doi: 10.1108/02635570810904613.
Lin, L., & Piesse, J. (2004). Identification of corporate distress in UK industrials: a conditional probability analysis approach. Applied Financial Economics, 14(2). doi: 10.1080/0960310042000176344.
Maksimovic, V., & Phillips, G. (1998). Asset efficiency and reallocation decisions of bankrupt firms. Journal of Finance, 53(5). doi: 10.1111/0022-1082.00063.
Merton, R. C. (1974). On the pricing of corporate debt: the risk structure of interest rates. Journal of Finance, 29(2). doi: 10.2307/2978814.
Merton, R. C. (1973). An intertemporal capital asset pricing model. Econometrica: Journal of the Econometric Society, 41(5). doi: 10.2307/1913811.
Mikosch, T. (2009). Non-life insurance mathematics: an introduction with the Poisson process. Springer. doi: 10.1007/978-3-540-88233-6.
Nelsen, R. B. (2006). An introduction to copulas. Springer Verlag. doi: 10.1007/978-1-4757-3076-0.
Nelson, E. (2001). Dynamical theories of Brownian motion. Vol. 17. Princeton University Press.
Odom, M. D., & Sharda, R. (1990). A neural network model for bankruptcy prediction. 1990 IJCNN international joint conference on neural networks. San Diego. doi: 10.1109/IJCNN.1990.137710.
Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1). doi: 10.2307/2490395.
Opler, T. C., & Titman, S. (1994). Financial distress and corporate performance. Journal of Finance, 49(3). doi: 10.1111/j.1540-6261.1994.tb00086.x.
Platt, H. D., & Platt, M. B. (2002). Predicting corporate financial distress: reflections on choice-based sample bias. Journal of Economics and Finance, 26. doi: 10.1007/BF02755985.
Ribeiro, B., Silva, C., Chen, N., Vieira, A., & Carvalho das Neves, J. (2012). Enhanced default risk models with SVM+. Expert Systems with Applications, 39(11). doi: 10.1016/j.eswa.2012.02.142.
Roman, L. (2009). Quality of earnings and earnings management. Journal of AICPA.
Sandin, A. R., & Porporato, M. (2008). Corporate bankruptcy prediction models applied to emerging economies: evidence from Argentina in the years 1991-1998. International Journal of Commerce and Management, 17(4). doi: 10.1108/10569210710844372.
Shleifer, A., & Vishny, R. W. (1992). Liquidation values and debt capacity: a market equilibrium approach. Journal of Finance, 47(4). doi: 10.1111/j.1540-6261.1992.tb04661.x.
Shumway, T. (2001). Forecasting bankruptcy more accurately: a simple hazard model. Journal of Business, 74(1). doi: 10.1086/209665.
Sjostrand, K. (2005). Matlab implementation of LASSO, LARS, the elastic net and SPCA. Informatics and Mathematical Modelling. Technical University of Denmark, DTU.
Spiegelhalter, D. (1977). A test for normality against symmetric alternatives. Biometrika, 64(2). doi: 10.1093/biomet/64.2.415.
Spiegelhalter, D. J. (1986). Probabilistic prediction in patient management and clinical trials. Statistics in medicine, 5(5). doi: 10.1002/sim.4780050506.
Tian, S., & Yu, Y. (2013). Variable selection for international bankruptcy forecasts. in Innovation Conference (SIIC). Suzhou-Silicon Valley-Beijing International. doi: 10.1109/SIIC.2013.6624162.
Vasicek, O. (1987). Probability of loss on loan portfolio. KMV Corporation, 12(6). doi: 10.1002/9781119186229.ch17.
Wilson, R., & Sharda, R. (1992). Neural networks. OR/MS Today.
Witkowska, D., Mazur, A., & Staniec, I. (2004-2005). Classification of borrowers: artificial neural networks and classification function. Folia Oeconomica Stetinensia, 3-4(11-12).
Yim, J., & Mitchell, H. (2005). Comparison of country risk models: hybrid neural networks, logit models, discriminant analysis and cluster techniques. Expert Systems with Applications, 28(1). doi: 10.1016/j.eswa.2004.08.005.
Zhang, G., Y Hu, M., Eddy Patuwo, B., & C Indro, D. (1999). Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis. European Journal of Operational Research, 116(1). doi: 10.1016/S0377-2217(98)00051-4.
Zmijewski, M. E. (1984), Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting Research, 22. doi: 10.2307/2490859