Using data collected from a sample of 42 companies, 11 different financial indicators are tested from which 4 are identified as good predictors of the performances of specially treated (ST) companies after asset restructuring. Different possible combinations of the four indicators are developed from which an optimal set consisting of three indicators are identified using linear discriminant function. The three optimal set of variables are total asset growth, asset-liability ratio, and return on equity. A linear discriminant function is developed, using these three indicators as variables, for predicting company performance after asset restructuring. Using out-of-sample testing, the predictive accuracy of this model was tested to be 79.3%. This shows that the model is an effective and useful tool for predicting a company’s performance after assets restructuring. Further examples and illustrations with the data collected from the sampled companies also show that: (1) Companies which kick off the cap of ST successfully after asset restructuring will have a significant performance improvement in the year of asset restructuring and in the year after, (2) Performances of the companies that kick of the cap of ST successfully may not improve in the second year after assets restructuring, and (3) Companies that are still labeled as ST after asset restructuring may not be able to improve their performances in the following years if no other actions are taken.
Li, Hui and Zhang, Ye
"Forecasting the Performance Improvements of Specially Treated Chinese Listed Companies after Asset Restructuring: A discriminant Analysis Approach,"
Annals of Management Science: Vol. 1
, Article 1.
Available at: https://digitalscholarship.tnstate.edu/ams/vol1/iss1/1