Comparing the Performance of Deep Learning Methods to Predict Companies' Financial Failure

Abstract

One of the most crucial problems in the eld of business is nancial forecasting. Many companies are interested in forecasting their incoming nancial status in order to adapt to the current nancial and business environment to avoid bankruptcy. In this work, due to the effectiveness of Deep Learning methods with respect to classi cation tasks, we compare the performance of three well-known Deep Learning methods (Long-Short Term Memory, Deep Belief Network and Multilayer Perceptron model of 6 layers) with three bagging ensemble classi ers (Random Forest, Support Vector Machine and K-Nearest Neighbor) and two boosting ensemble classi ers (Adaptive Boosting and Extreme Gradient Boosting) in companies' nancial failure prediction. Because of the inherent nature of the problem addressed, three extremely imbalanced datasets of Spanish, Taiwanese and Polish companies' data have been considered in this study. Thus, ve oversampling balancing techniques, two hybrid balancing techniques (oversamplingundersampling) and one clustering-based balancing technique have been applied to avoid data inconsistency problem. Considering the real nancial data complexity level and type, the results show that the Multilayer Perceptron model of 6 layers, in conjunction with SMOTE-ENN balancing method, yielded the best performance according to the accuracy, recall and type II error metrics. In addition, Long-Short Term Memory and ensemble methods obtained also very good results, outperforming several classi ers used in previous studies with the same datasets.