# Machine Learning and Zombie Firms Classification
## 3 Data and Methodology  
### Data  
We obtained financial information from the financial reports of Astra Managers. We collected firm-level data from 1991 to 2021. The samples comprised 97,467 firm-year observations. We defined the training period as before 2002 and tried to predict the zombies for the test period after 2003. We collected the major financial accounts from Astra Managers and only adopted the accounts whose coverage was over 40% to exclude infrequently observed data from our dataset. Some accounts were not recorded for some firm-year level observations; for example, \"construction in progress\" was only recorded if the firms were constructing properties, plants, or equipment. Therefore, after filtering out infrequent financial accounts, we replaced unobserved data with zero. A description of all the financial accounts used as input data is presented in Appendix A. To explore the predictability of machine learning for zombie firms, we used the FN criteria for the first part of this study and both the CHK and FN criteria for the second part. It would be better to use a \"true zombie indicator\" as ground label data if possible. However, there are no common criteria for zombie firms or ground label data. In addition, one of our purposes was to explore the differences between two major zombie indicators; we used the CHK and FN criteria. The CHK criteria were originally proposed by [PERSON] (2006) [3] and CHK. They define zombie firms as those that are insolvent and should exit the market, but are kept alive by help from creditors. To determine whether firms are receiving subsidized credit, the CHK criteria use prime loan rates, which are the interest rates applied to blue-chip firms, to calculate the theoretical minimum required interest payments, as follows:  
\[R_{i,t}^{*}=rs_{t-1}BS_{i,t-1}+\left(\frac{1}{5}\sum_{j=1}^{5}rl_{t-j}\right) BL_{i,t-1}+rcb_{5\;years,t}Bonds_{i-1} \tag{1}\]  
where \(BS_{i,t-1}\), \(BL_{i,t-1}\), and \(Bonds_{i,t-1}\) are the short-term (less than one year) bank loans, long-term (over one year) bank loans, and total bonds outstanding (including convertible and warrant-attached bonds) of firm \(i\) at the end of year \(t\), respectively, and \(rs_{t}\), \(rl_{t}\), and \(rcb_{5\;years,t}\) are the average short-term prime rate in year \(t\), the average long-term prime rate in year \(t\), and the minimum observed coupon rate on any convertible corporate bond issued in the last five years before \(t\). This estimates the lower bound of interest payments. If a firm's interest payments are less than this lower bound, it means that the loan conditions are extremely advantageous for borrowers, and that such firms should be categorized as zombie firms. FN pointed out that the CHK criteria have unignorable type I and II errors, where healthy firms can be classified as zombie firms and zombie firms can be classified as healthy firms. The reason for the former is that the policy interest rates became low after the 2000s. When the policy interest rate is close to zero, banks' lending interest rates also decrease. Ithas been observed that a number of healthy firms have interest rates lower than the prime lending rates in Japan. This was particularly true in the 2000s, when the Japanese economy experienced the quantitative easing monetary policy. The reason for the latter is that there is evergreen lending. Evergreen lending occurs when borrowers are not required to pay the principal, but only the interest, and the borrowers are allowed to borrow additional loans. With this method, banks can conceal the actual value of their troubled loans. In particular, during the banking crisis in Japan, troubled banks increased evergreen lending. To deal with these issues, the FN criteria added profitability and evergreen lending criteria to the CHK criteria to identify zombie firms. As a profitability criterion, firms whose EBIT exceeds the hypothetical risk-free interest payment are excluded from being classified as zombie firms.