LiFoL: An Efficient Framework for Financial Distress Prediction in High-Dimensional Unbalanced Scenario

Abstract

Corporate financial distress will significantly damage the company’s and its stakeholders’ interests and even lead to a global financial crisis. Therefore, finding an efficient method for financial distress prediction (FDP) to avoid greater losses is essential. Although there is a lot of research and progress in this field, the existing methods rarely consider the problems of high dimensionality and class imbalance, which will largely limit the models to achieve satisfactory performance. To alleviate these problems, this article first proposes a novel Lightspace-SMOTE upsampling method, which can reduce the feature dimensionality and increase the signal-to-noise ratio (SNR) of the original data and then upsample it to increase the number of minor class samples. In addition, this article proposes an efficient ensemble framework (LiFoL) that combines Lightspace-SMOTE, focal loss (FL), and LightGBM, which can not only focus more on minor class and the hard-to-class samples but also obtain better performance. At the same time, the feature importance provided by the model can provide strong support for model interpretability. Experimental results show that the Lightspace-SMOTE upsampling method can help the model achieve higher scores in area under ROC curve (AUC) and recall, especially in the case of longer prediction periods. Compared with current methods, LiFoL can achieve more than 10% improvement in AUC and more than 20% in recall.