  In this contribution, we propose machine learning techniques to predict
zombie firms. First, we derive the risk of failure by training and testing our
algorithms on disclosed financial information and non-random missing values of
304,906 firms active in Italy from 2008 to 2017. Then, we spot the highest
financial distress conditional on predictions that lies above a threshold for
which 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 financial
distress, i.e., their forecasts fall into the risk category above the threshold
for at least three consecutive years. For our purpose, we implement a gradient
boosting algorithm (XGBoost) that exploits information about missing values.
The inclusion of missing values in our predictive model is crucial because
patterns of undisclosed accounts are correlated with firm failure. Finally, we
show that our preferred machine learning algorithm outperforms (i) proxy models
such as Z-scores and the Distance-to-Default, (ii) traditional econometric
methods, and (iii) other widely used machine learning techniques. We provide
evidence that zombies are on average less productive and smaller, and that they
tend to increase in times of crisis. Finally, we argue that our application can
help financial institutions and public authorities design evidence-based
policies-e.g., optimal bankruptcy laws and information disclosure policies.
