Comparing Firm Failure Predictions Between Logit, KMV, and ZPP Models: Evidence from Taiwan’s Electronics Industry

Abstract

This paper applies the Taiwan electronics industry data to detect the discriminatory powers of Logit, KMV, and zero-price probability (ZPP) models that represent respectively the regressive fitting model, the option-based pricing model, and the GARCH time series simulation model. In our circumstances, according to cumulative accuracy profile, receiver operating characteristic, and even Brier score, the KMV performs the worst. The disadvantages for KMV are that the equity market exists some nonlinear characteristics, the unknown market value of asset affected by the change of capital structure is not exogenous, and the failure point is difficult to be estimated correctly. Besides, KMV is however too simple to model the fluctuation of the equity value as the GARCH does. On the other hand, the Logit performs above average. To preclude over-fitting and keep model parsimonious, two significant factors are extracted from as many as forty financial variables for the logistic regression on binary failure data. The result of Logit training has perfect discrimination. However, for the post-sample data, the fitting to categorical but not ordinal data makes Logit have the divergent failure predicted probabilities and highest Briser Score. In practical, ZPP GARCHNorm uses just equity value to predict firm failure but it performs remarkably well supposing that downward price trend or volatility persistence in stock price changes is appropriately caught. It implies that the distorted signals such as overreaction of traders and insider trading would definitely impair the ZPP GARCHNorm. Nevertheless, the larger type I error than type II error in all models indicates that the prediction of non-failed firms should be more examined further than that of failed firms.