Asset restructuring performance prediction for failure firms

Abstract

AbstractThis study aims to forecast asset restructuring performance for failing public firms and to test the effectiveness of different strategies of assets restructuring by using ten models, including: standalone models of multivariate discriminant analysis (MDA), logistic regression (Logit), probit, case‐based reasoning (CBR), support vector machine (SVM), and their bagged ensembles. Moreover, this study proposes a knowledge base by collecting positive and negative samples and uses random down‐sampling method to pair success samples with failure samples in order to focus more on real‐happened failure samples when forming a balanced data set. After variables filtering process, ten financial ratios, covering aspects of debt‐paying, risk level, development, and operating abilities, are identified as significantly efficient predictors. The following empirical results are found, namely (a) adopting more means of asset restructuring leads to a higher chance for performance improvement; (b) equity transfer, asset stripping, and asset acquisition are the three most commonly used means, and the last two are among the most efficient means; (c) compared to the other 9 models, SVM has the most balanced prediction performance in terms of total accuracy, true positive ratio, and true negative ratio; and (d) predicting a failing event of achieving performance improvement with asset restructuring is more difficult than predicting a successful event, which needs more focus with the state‐of‐the‐art models.