TURNAROUNDS-Modeling the Probability of a Turnaround

The objective of this paper is to examine the possibility of predicting the recovery of a distressed firm into a turnaround based on its current financial situation and a set of variables that are considered of having a significant impact on the turnaround probability. To assess this problem 150 firms are used that were distressed at some point during the period 1991 to 2001. These firms were all listed in one of the major US stock exchanges and were all randomly chosen, with 86 failing to recover from distress and 64 making a successful turnaround. In order to establish a forecast model, two different quantitative econometrical methods are applied; Linear Discriminant Analysis and Logistic Regression. The model predicting the outcome of the 150 distressed firms with the highest accuracy is tested for its prediction power on a holdout sample that consisted of 3140 distressed firms. These 3140 firms were all listed at one of the major US stock exchanges and are distressed at some point during the period 2002 to 2008. The prediction accuracy of the best model amounted to 92.7 % in the in-sample and 89% in the holdout sample. The decisive variables that were selected by this model are firm size, severity of distress and total debt to total assets. Finally, we compare the returns yielded by a portfolio consisting of the turnarounds that were predicted by the model out of the holdout sample to the returns generated by the S&P 500. The annual returns for the seven years back-testing period, 2004-2010, for our portfolio amounted to 18%, while the annual return for the S&P 500 was 4%