This study evaluates three corporate failure prediction models using latest available data on corporate insolvencies for non-financial constitutes represented in CDAX. We estimate semiparametric Cox proportional hazards models considering Andersen-Gill counting process (AG-CP) to explore the importance of accounting and financial ratios as well as industry effects that are useful in detecting potential insolvencies. The contribution of this paper is twofold. First, the literature on corporate

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... fault prediction is manifold and predominantly focused on U.S. data. Thus, academic contribution based on German-listed companies is limited. To our best knowledge, we are the first to conduct thorough comparative out-of-sample Cox regression models considering AG-CP based on a unique dataset for non-financial constitutes subject to the German insolvency statute ("InsO"). Relying on a parsimonious accounting-based approach inspired by Altman (1968) and Ohlson (1980) is merely adequate. Shumway (2001) and Campbell et al. (2008) variable selection delivers the best discriminatory power and calibration results. In particular, a combination of pure accounting ratios augmented with market-driven information in Model (2) indicates superior accuracy rates in top deciles. However, in-sample empirical results underpin the importance towards market-based indicators, as all accounting ratios enter statistically insignificant. Secondly, we test to what extend industry variables improve discriminatory power and forecasting accuracy of fitted models. Contrary to the findings of Chava & Jarrow (2004), our research implies that industry grouping adds marginal predictive power and no overall improvement in accuracy rates when market variables are already included in the probability of default (PD) model.