Long-horizon predictions of credit default with inconsistent customers

We developed a decision support framework for default predictions that addresses two common issues: inconsistent customers and predictions of future defaults. We developed a T−m default prediction model using multivariate adaptive regression splines to address the methodological challenges. We confirm that this model outperforms typical approaches in terms of default prediction accuracy. Furthermore, an empirical application of our new framework involving unique data on defaults among Chinese-listed companies yields several substantive insights. Owing to the high interpretability of our predictions, we identify certain industry sectors that should receive high (and low) credit risk assessments. In addition, our research has important implications for the investment decisions of financial institutions and investors and government regulations.