Multicriteria decision aid models for the prediction of securities class actions: evidence from the banking sector

In recent years, there has been an increase in the number and value of securities class actions (SCAs), attracting the attention of various stakeholders such as investors, managers, policy makers, lawyers, etc. The present study extends the literature, by investigating for the first time the development of a classification model to forecast SCAs filed against US banks. Our results show that the proposed multicriteria decision aid model achieves a satisfactory accuracy, by classifying correct around 80 % of the banks in an out-of-sample testing. We obtain similar results when we use a walk-forward approach, instead of a tenfold cross-validation technique, for the estimation and testing of the model. Further analysis indicates that the classification accuracies can improve further by the inclusion of a corporate governance indicator that relates to executive and director compensation and ownership.