Predicting zombie firms after the COVID-19 pandemic using explainable artificial intelligence

Abstract

This study examines various artificial intelligence (AI) models for predicting financially distressed firms with poor profitability (“Zombie firms”). In particular, we adopt the Explainable AI (“XAI”) approach to overcome the limitations of the previous AI models, which is well-known as the black-box problem, by utilizing the Local Interpretable Model-agnostic Explanations (LIME) and the Shapley Additive Explanations (SHAP). This XAI approach thus enables us to interpret the prediction results of the AI models. This study focuses on the Korean sample from 2019 to 2023, as it is expected that the COVID-19 pandemic increases the number of zombie firms. We find that the XGBoost model based on a boosting technique has the best predictive performance among several AI models, including the traditional ones (e.g. the logistic regression). In addition, by using the XAI approach, we provide visualized interpretations for the prediction results from the XGBoost model. The analysis further reveals that the return on sales and the selling, general and administrative costs are the most impactful variables for predicting zombie firms. Overall, this study focusing on several AI models not only shows the improvement for the prediction of zombie firms (relative to the traditional models) but also increases the reliability of the prediction results by adopting the XAI approach, providing several implications for market participants, such as financial institutions and investors.

Keywords

• Explainable AI (XAI)
• Zombie firms
• XGBoost
• SHAP
• LIME
• Financial forecast