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In this contribution, we propose machine learning techniques to predictzombie firms. First, we derive the risk of failure by training and testing ouralgorithms on disclosed financial information and non-random missing values of304,906 firms active in Italy from 2008 to 2017. Then, we spot the highestfinancial distress conditional on predictions that lies above a threshold forwhich a combination of false positive rate (false prediction of firm failure)and false negative rate (false prediction of active firms) is minimized.Therefore, we identify zombies as firms that persist in a state of financialdistress, i.e., their forecasts fall into the risk category above the thresholdfor at least three consecutive years. For our purpose, we implement a gradientboosting algorithm (XGBoost) that exploits information about missing values.The inclusion of missing values in our predictive model is crucial becausepatterns of undisclosed accounts are correlated with firm failure. Finally, weshow that our preferred machine learning algorithm outperforms (i) proxy modelssuch as Z-scores and the Distance-to-Default, (ii) traditional econometricmethods, and (iii) other widely used machine learning techniques. We provideevidence that zombies are on average less productive and smaller, and that theytend to increase in times of crisis. Finally, we argue that our application canhelp financial institutions and public authorities design evidence-basedpolicies-e.g., optimal bankruptcy laws and information disclosure policies.

Machine Learning for Zombie Hunting: Predicting Distress from Firms' Accounts and Missing Values