This paper provides the first empirical study on bond defaults in the Chinese market. It overcomes the deficiencies of existing methods, which suffer from lack of actual default data for back testing. With newly available bond default data, we analyze the roles of market variables against accounting variables under various models. While we find that Merton’s market-based structural model and KMV’s Distance to Default exhibit languid discriminating power compared with hazard models that have carefully constructed predictors, other market variables carry significant information about bond defaults and could help improve on models with only the accounting variables. This implies that the collective intelligence of the market could somehow mitigate the distortion caused by misreported accounting information. Further, model performance can be significantly improved by adding predicting variables that link an individual financial measure to the broader market performance, such as the relative margin—a business environment proxy introduced in this study. We not only shed light on the default behavior of the Chinese bond market, but also provide a promising approach to improve the variable selection process.