# Machine Learning and Zombie Firms Classification
## 4 Zombie Indicator Prediction  
### FN Criteria  
In this section, we describe the results for the prediction of FN zombie firms using our random forest models. We argue that our random forest model successfully predicted the FN zombie firms during the test period. Table 1 shows the results of our prediction of FN zombie firms. We correctly predicted the firms' status during the training period 94.87% of the time ((4102 + 26,620)/32,394). Among the predicted zombie firms, the precision was 71.46% (4102/5740). On the other hand, almost all the firms predicted to be healthy were actually categorized as healthy firms. Considering the test period, the accuracy was 93.77% ((3824 + 57,204)/65,083). Among the predicted zombie firms, the precision was 64.98% (3824/5885). A total of 96.63% (57,204/59,198) of the firms predicted as healthy firms were labeled as healthy firms according to the FN criteria. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Training**} & & \\ \hline  & Predicted zombie & Predicted healthy & Total \\ \hline FN zombie & 4102 & 24 & 4126 \\ FN healthy & 1638 & 26,620 & 28,258 \\ \hline total & 5740 & 26,644 & 32,384 \\ \hline
**Test** & & & \\ \hline  & Predicted zombie & Predicted healthy & Total \\ \hline FN zombie & 3824 & 1994 & 5818 \\ FN healthy & 2061 & 57,204 & 59,265 \\ \hline Total & 5885 & 59,198 & 65,083 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Result of the prediction model of FN zombies. The top table shows the total numbers of firms based on the prediction by our model and categorization under the FN criteria during the training period. The bottom table shows the results for the test period. Our FN zombie prediction model had high recall rates, especially for the training period. The recall rate for the FN zombies was 99.42% (4102/4126) and that for healthy firms was 94.20% (26,620/28,258). The zombie firm recall rate declined to 65.73% (3824/5818) for the test sample, but the healthy firm recall rate increased to 96.52% (57,204/59,265). The F1 scores for zombie firms and healthy firms were 0.8315 and 0.9697, respectively, for the training period, and 0.6535 and 0.9658 for the test period. The performance measures for the predictions of all the models are presented in Table 2.  
Figure 4 shows the evolution of the firm ratio based on the predicted status and labeled status. We observed that, during the training period, almost all the zombie firms were predicted as zombie firms. Type II errors, or firms that were predicted as zombies but were healthy, gradually increased as time went on, lowering the accuracy of the predictions. Notice that the ratio of type II errors was always below the ratio of true positive predictions, meaning that our zombie predictions always had an accuracy better than 50%. This increasing trend in type II errors was not observed beyond 2003. Instead, type I errors (firms that were predicted as healthy, but were zombies) increased compared to the training period. This made the predicted healthy firms less accurate and lowered the total accuracy after 2003. Notice that, even though the total accuracy decreased, it stayed above 90% during the test period, suggesting that our model predicted the zombie firms well. When we look at the time around 2009, after the occurrence of the global financial crisis, the zombie ratio increased. Our prediction successfully increased the number of zombie firms and decreased the healthy firms, as occurred in reality. This suggests that our model can handle situations involving an exogeneous shock that occurs in the economy.