A hybrid forward-intensity model for private firm default. An empirical test of Minsky’s taxonomy in an emerging market.
DOI:
https://doi.org/10.17561/ree.n1.2027.9888Palabras clave:
credit risk, financial fragility, private firms, default prediction, forward intensity modelResumen
In financial risk management, predicting default for privately held firms is a critical area of study, and more concretely in emerging markets where comprehensive market data are often unavailable. Traditional accounting-based models are backward-looking and lack a theoretical foundation for how financial fragility develops, while market-based models are, in certain circumstances, infeasible to estimate. To address this analytical gap, this paper develops a hybrid forward-intensity model to estimate default probabilities for privately held firms and to construct a firm-level classification grounded in the Minskyan taxonomy of financial positions. An implied distance-to-default metric for non-listed firms is constructed through a cross-sectional mapping from publicly traded firms that matches accounting fundamentals with market-based risk measures. From this metric, multi-horizon forward default intensities follow the approach of Duan et al. (2012) and incorporate firm-level balance sheet variables together with aggregate financial conditions. Firms are then classified endogenously into Hedge, Speculative, and Ponzi regimes through hierarchical clustering. The empirical application draws on a panel of 9,346 Colombian privately held manufacturing firms spanning Q1 2009 to Q1 2018, with annual financial statements and monthly default intensity estimation. A parameter transfer procedure based on propensity score matching and temporal out-of-sample validation matches public-firm parameters to comparable private firms. The hybrid model achieves higher out-of-sample discrimination accuracy than benchmark alternatives. Credit spreads charged by commercial lenders increase monotonically across Hedge, Speculative, and Ponzi clusters. Maturity mismatch is the dominant predictor at medium and long horizons, while liquidity retains predictive power at short horizons.
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Acharya, V. V., Davydenko, S. A., & Strebulaev, I. A. (2012). Cash holdings and credit risk. Review of Financial Studies, 25(12), 3572–3609. https://doi.org/10.1093/rfs/hhs106
Adrian, T., & Brunnermeier, M. K. (2016). CoVaR. American Economic Review, 106(7), 1705–1741. https://doi.org/10.1257/aer.20120555
Almeida, H., Campello, M., Laranjeira, B., & Weisbach, M. S. (2012). Corporate debt maturity and the real effects of the 2007 credit crisis. Critical Finance Review, 1, 3–58. https://doi.org/10.1561/104.00000001
Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23(4), 589–609. https://doi.org/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), 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), 95–127.
Ayyagari, M., Beck, T., & Demirgüç-Kunt, A. (2007). Small and medium enterprises across the globe. Small Business Economics, 29(4), 415–434. https://doi.org/10.1007/s11187-006-9002-5
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
Bernanke, B. S., & Gertler, M. (1989). Agency costs, net worth, and business fluctuations. American Economic Review, 79(1), 14–31.
Bharath, S. T., & Shumway, T. (2008). Forecasting default with the Merton distance to default model. Review of Financial Studies, 21(3), 1339–1369. https://doi.org/10.1093/rfs/hhn044
Black, F., & Cox, J. C. (1976). Valuing corporate securities: Some effects of bond indenture provisions. Journal of Finance, 31(2), 351–367.
Chan-Lau, J. A., Hu, R., Mungo, L., Qu, R., Xin, W., & Zhong, C. (2024). Monitoring privately-held firms’ default risk in real time: A signal-knowledge transfer learning model. IMF Working Papers, 2024(115). https://doi.org/10.5089/9798400278396.001
Chava, S., & Jarrow, R. A. (2004). Bankruptcy prediction with industry effects. Review of Finance, 8(4), 537–569. https://doi.org/10.1093/rof/8.4.537
Cheraghali, H., & Molnár, P. (2024). SME default prediction: A systematic methodology-focused review. Journal of Small Business Management, 62(6), 2847–2905. https://doi.org/10.1080/00472778.2023.2277426
Ciampi, F., Giannozzi, A., Marzi, G., & Altman, E. I. (2021). Rethinking SME default prediction: A systematic literature review and future perspectives. Scientometrics, 126(3), 2141–2188. https://doi.org/10.1007/s11192-020-03856-0
Dang, T. V., Gorton, G., & Holmström, B. (2012). Ignorance, debt and financial crises. https://www.columbia.edu/~td2332/Paper_Ignorance.pdf
Denis, D. J., & Sibilkov, V. (2010). Financial constraints, investment, and the value of cash holdings. Review of Financial Studies, 23(1), 247–269. https://doi.org/10.1093/rfs/hhp031
Duan, J. C., Kim, B., Kim, W., & Shin, D. (2018). Default probabilities of privately held firms. Journal of Banking & Finance, 94, 235–250. https://doi.org/10.1016/j.jbankfin.2018.08.006
Duan, J. C., Sun, J., & Wang, T. (2012). Multiperiod corporate default prediction: A forward intensity approach. Journal of Econometrics, 170(1), 191–209. https://doi.org/10.1016/j.jeconom.2012.04.007
Duffie, D., & Lando, D. (2001). Term structures of credit spreads with incomplete accounting information. Econometrica, 69(3), 633–664.
Duffie, D., Saita, L., & Wang, K. (2007). Multi-period corporate default prediction with stochastic covariates. Journal of Financial Economics, 83(3), 635–665.
du Jardin, P. (2021). Dynamic self-organizing feature map-based models applied to bankruptcy prediction. Decision Support Systems, 147, 113576. https://doi.org/10.1016/j.dss.2021.113576
Fernández-Delgado, M., Cernadas, E., Barro, S., & Amorim, D. (2014). Do we need hundreds of classifiers to solve real world classification problems? Journal of Machine Learning Research, 15, 3133–3181. https://jmlr.org/papers/v15/delgado14a.html
Gopalan, R., Song, F., & Yerramilli, V. (2014). Debt maturity structure and credit quality. Journal of Financial and Quantitative Analysis, 49(4), 817–842. https://doi.org/10.1017/S0022109014000520
He, Z., & Xiong, W. (2012). Dynamic debt runs. Review of Financial Studies, 25(6), 1799–1843. https://doi.org/10.1093/rfs/hhs004
Hennig, C. (2007). Cluster-wise assessment of cluster stability. Computational Statistics and Data Analysis, 52(1), 258–271.
Holmström, B., & Tirole, J. (1998). Private and public supply of liquidity. Journal of Political Economy, 106(1), 1–40.
Jiménez, G., Ongena, S., Peydró, J. L., & Saurina, J. (2014). Hazardous times for monetary policy: What do twenty-three million bank loans say about the effects of monetary policy on credit risk-taking? Econometrica, 82(2), 463–505. https://doi.org/10.3982/ECTA10104
Kashyap, A. K., & Stein, J. C. (2000). What do a million observations on banks say about the transmission of monetary policy? American Economic Review, 90(3), 407–428. https://doi.org/10.1257/aer.90.3.407
Keen, S. (1995). Finance and economic breakdown: Modeling Minsky’s financial instability hypothesis. Journal of Post Keynesian Economics, 17(4), 607–635.
Keynes, J. M. (1936). The general theory of employment, interest and money. Macmillan.
Lessmann, S., Baesens, B., Seow, H. V., & Thomas, L. C. (2015). Benchmarking state-of-the-art classification algorithms for credit scoring. European Journal of Operational Research, 247(1), 124–136. https://doi.org/10.1016/j.ejor.2015.05.030
Levine, R. (2002). Bank-based or market-based financial systems: Which is better? Journal of Financial Intermediation, 11(4), 398–428. https://doi.org/10.1006/jfin.2002.0341
Merton, R. C. (1974). On the pricing of corporate debt: The risk structure of interest rates. Journal of Finance, 29(2), 449–470. https://doi.org/10.1111/j.1540-6261.1974.tb03058.x
Minsky, H. P. (1986). Stabilizing an unstable economy. Yale University Press.
Njeru, A. (2019). An alternative approach to estimation of the probability of default for commercial entities: The modified KMV Merton model. Journal of Risk and Financial Management, 12(3), 134.
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
Rashid, U., Abdullah, M., Khatib, S. F., Khan, F. M., & Akhter, J. (2024). Unravelling trends, patterns and intellectual structure of research on bankruptcy in SMEs: A bibliometric assessment and visualisation. Heliyon, 10(2), e24254. https://doi.org/10.1016/j.heliyon.2024.e24254
Shumway, T. (2001). Forecasting bankruptcy more accurately: A simple hazard model. Journal of Business, 74(1), 101–124. https://doi.org/10.1086/209665
Whalen, C. J. (2001). The financial instability hypothesis: A restatement. Public Policy Brief No. 62. Levy Economics Institute.
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