Firm Default Prediction: A Bayesian Model-Averaging Approach
提出一种贝叶斯模型平均方法，解决企业违约预测中真实模型未知的问题，发现只有负债资产比和市场收益波动率是稳健预测因子，且模型平均预测优于单个模型。
I develop a new predictive approach using Bayesian model averaging to account for incomplete knowledge of the true model behind corporate default and bankruptcy filing. I find that uncertainty over the correct model is empirically large, with far fewer variables being significant predictors of default compared with conventional approaches. Only the ratio of total liabilities to total assets and the volatility of market returns are robust default predictors in the overall sample and individual industry groups. Model-averaged forecasts that aggregate information across models or allow for industry-specific effects substantially outperform individual models.