# Machine Learning and Zombie Firms Classification
## 1 Introduction
Blue bar is a firm which is categorized as healthy firm by CHK and FN criteria. Orange bar is a firm which is categorized as zombie firm by both criteria. Green (red) bar is a firm which is categorized as zombie firm by only CHK (FN) firms. analysis. It might be not surprising that the most important financial account was different, because the FN criteria additionally use profitability measures. However, we observed that there was only one account that reflected a firm's loan situation out of the ten most important accounts for the FN zombie prediction model. The other nine accounts were related to profitability measures. In contrast, the accounts related to profits were not in the ten most important accounts for the CHK prediction models. The most important variables for predicting CHK zombies were long-term bonds, short-term loans and bonds, and interest expenses. This suggests that the CHK criteria are less dependent on a firm's performance and that the FN criteria capture low-profitability firms more than originally intended. Rather, the CHK criteria are more likely to choose firms that may have some issues related to loans and debts. This research applied machine learning to zombie firm analyses. One of the closest studies to this research is that by [9]. They argued that missing information in financial accounts is correlated with firm failures, and they used the XGBoost model to avoid this problem. They showed that a machine learning model had a higher accuracy than statistical models such as the logit model. Compared to them, our purpose was not only to predict zombie firms, but also to explore the differences in zombie criteria. Our random forest model had a high accuracy for zombie firm predictions and interpretability by calculating the variable contributions. This is the reason why we used the random forest model. In addition, missing information was not crucial for this study. They included small firms whose requirements for disclosure were often less strict than those of listed firms. In contrast, we only focused on listed firms, where the requirements for disclosure were the same for all firms. For these reasons, this study has different implications and contributions than the study by [9]. This study makes three contributions. Firstly, the high predictability of machine learning methods for zombie firm predictions directly offers zombie firm identification methods to practitioners such as policymakers, bankers, and investors. Our machine learning model uses only financial accounts obtained from publicly available financial reports. Since it does not use any information about policy interest rates, our approach can avoid the issue raised from the low interest rate arguments. Policymakers and banks can directly assess whether a firm is a zombie or not by our approach, which allows them to make more efficient decisions. For example, when the government subsidizes a firm, it will not undermine the market efficiency if it knows whether the beneficiary firm is a zombie by our approach. Our approach also allows investors to identify whether a firm is a zombie or not so they can form an efficient portfolio. Secondly, we showed that the CHK and FN criteria are qualitatively dissimilar and reflect different aspects of a firm's status. The FN criteria were proposed to handle the issue of low interest rates in the CHK criteria. They include profitability and evergreen lending criteria. These inclusions tackle the type I and type II errors observed in the CHK criteria after 2003. However, it is also argued that this procedure makes the criteria reflect firms that are only unprofitable, rather than zombie firms. Our results offer new insights into this debate. We show that the crucial financial accounts for the FN criteria are income-related accounts, whereas loans and debts are important for the CHK criteria, implying that the FN criteria are more likely to exhibit firms that are just unprofitable. Thirdly, we offer new examples of machine learning techniques to corporate finance fields.