Predicting Bankruptcy Resolution
研究破产公司最终结局（收购、重组或清算）的分类与预测，用237家破产公司数据构建logit模型，发现非会计数据能提升预测准确性，对投资者、法官等利益相关者有政策启示。
This study examines classification and prediction of the bankruptcy resolution event. Filing of bankruptcy is resolved through one of three alternative resolutions: acquisition, emergence or liquidation. Predicting the final bankruptcy resolution has not been examined in the prior accounting and finance literature. This post‐bankruptcy classification and prediction of the final resolution is harder than discriminating between healthy and bankrupt firms because all filing firms are already in financial distress. Motivation for predicting the final resolution is developed and enhanced. A sample of 237 firms filing for bankruptcy is used. Classification and prediction accuracies are determined using a logit model. A ten‐variable, three‐group resolution logit model, which includes five accounting and five non‐accounting variables is developed. The model correctly classifies 62 percent of the firms, significantly better than a random classification. We conclude that non‐accounting data add relevant information to financial accounting data for predicting post bankruptcy resolution. Further, public policy implications for investors, researchers, bankruptcy judges, claimants and other stakeholders are discussed.