Artificial neural networks in reorganization outcome and investment of distressed firms: The Taiwanese case

Using a sample of 59 Taiwanese firms in distress, we investigate investor gains or losses around the filing date and the confirmation date of the reorganization plan and propose ex ante trading strategies that can generate potentially substantial returns from these stocks. We further experiment with a novel approach to sensitivity analysis of neural network inputs for refining and rating the most important input variables and then construct artificial neural network (ANN) models to classify and predict the post-reorganization filing resolutions in a three- (Model 1) and two-group (Models 2 and 3) resolution setting. We are unaware of any other paper that has used artificial intelligence techniques in analyzing post-bankruptcy resolutions. The results show that our five-variable models produce a consistent estimate between the training and testing sets; that is, both three- and two-group models are of a high degree of accuracy and stability, demonstrating the robustness of the ANN models.