This study assesses the stock return performance of 131 firms emerging from Chapter 11. Using differing estimates of expected returns, we consistently find evidence of large, positive excess returns in 200 days of returns following emergence. We also examine the reaction of our sample firms' equity returns to their earnings announcements after emergence from Chapter 11. The positive and significant reactions suggest that our results are driven by the market's expectational errors, not mismeasurement of risk. The results provide an interesting contrast, but not a contradiction, to previous work that has documented poor operating performance for firms emerging from Chapter 11.

ABSTRACT The purpose of this study is to present a new classification procedure, Recursive Partitioning Algorithm (RPA), for financial analysis and to compare it with discriminant analysis within the context of firm financial distress. RPA is a computerized, nonparametric technique based on pattern recognition which has attributes of both the classical univariate classification approach and multivariate procedures. RPA is found to outperform discriminant analysis in most original sample and holdout comparisons. We also observe that additional information can be derived by assessing both RPA and discriminant analysis results.

The purpose of this study is to present a new classification procedure, Recursive Partitioning Algorithm (RPA), for financial analysis and to compare it with discriminant analysis within the context of firm financial distress. RPA is a computerized, nonparametric technique based on pattern recognition which has attributes of both the classical univariate classification approach and multivariate procedures. RPA is found to outperform discriminant analysis in most original sample and holdout comparisons. We also observe that additional information can be derived by assessing both RPA and discriminant analysis results.