# Machine Learning and Zombie Firms Classification
## 5 Heterogeneity Between Zombie Indicators  
### CHK Zombie Firms Forecast Model  
Next, we examined the similarity between the CHK and FN criteria through machine learning predictions. Our FN prediction model showed a high predictability for FN zombie firms during the training and test periods. We used this model to predict the CHK zombie firms rather than the FN zombie firms. If the CHK and FN criteria are homogeneous, the zombie firms predicted by our FN prediction model are likely to be categorized as zombie firms even under the CHK criteria, and vice versa.  
Table 4 shows the accuracy of this prediction. We focused on the prediction accuracy during the training period because using the CHK criteria during the test period is still controversial, whereas their application before 2003 is widely accepted. The FN prediction model was highly accurate for FN zombie firms, especially during the training period. The status of 94.87% of the firms was correctly predicted by the FN criteria. In contrast, only 69.69% ((2177 + 20,390)/32,384) of the predictions were correct for the CHK criterion. The predicted zombie firms were more remarkable. Only 37.93% (2177/5740) of the predicted zombie firms were categorized as zombie firms under the CHK criterion. The FN prediction model often failed to predict CHK zombie firms. Similarly to the precision, the recall rate and F1 score of the zombies were 25.82% (2177/8431) and 0.3072, respectively. Among the predicted healthy firms, 76.53% (20,390/26,644) were truly healthy firms. These results imply that the FN prediction model predicts that healthy firms are zombies more frequently than that zombie firms are healthy.  
What can explain this deterioration in the prediction accuracy? One of the most plausible explanations is differences among the target criteria, such as CHK and FN. If they are qualitatively similar, then the prediction model for the criteria should have as high of an accuracy for the other criteria's prediction as the original. Based on our results, the CHK and FN criteria reflect different characteristics.  
We also used the CHK prediction model to predict FN zombie firms as a robustness test. Table 5 shows the accuracy. Similar to the previous case, we observed a drop in the prediction accuracy. The CHK model could predict a firm's status under the CHK criteria for 91.36% of cases, but it could only predict a firm's status under the FN criteria for 70.19% of cases ((2326 + 20,403)/32,384). Predicted healthy firms were classified as healthy firms under the FN criteria over 91% of the time (20,403/22,203), but predicted zombie firms were less likely to be categorized as zombie firms. Its accuracy was only 22.85% (2326/10,181); more than three-quarters of predicted zombie firms were categorized as zombie firms under the FN criteria. The recall rate and F1 score of zombies were 56.37% (2326/4126) and 0.3252, respectively. These results are also consistent with the argument that the CHK and FN criteria reflect different aspects of a firm's status.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Predicted Zombie** & **Predicted Healthy** & **Total** \\ \hline CHK zombie & 2177 & 6254 & 8431 \\ CHK healthy & 3563 & 20,390 & 23,953 \\ \hline Total & 5740 & 26,644 & 32,384 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Prediction of FN model with CHK zombie firms. This table shows the numbers of firms based on the prediction by our model and categorization under the CHK criteria during the training period. The prediction model is built to predict FN zombie firms whose accuracy of prediction of FN zombies are shown in Table 1.