  Thanks to the increasing availability of granular, yet high-dimensional, firm
level data, machine learning (ML) algorithms have been successfully applied to
address multiple research questions related to firm dynamics. Especially
supervised learning (SL), the branch of ML dealing with the prediction of
labelled outcomes, has been used to better predict firms' performance. In this
contribution, we will illustrate a series of SL approaches to be used for
prediction tasks, relevant at different stages of the company life cycle. The
stages we will focus on are (i) startup and innovation, (ii) growth and
performance of companies, and (iii) firms exit from the market. First, we
review SL implementations to predict successful startups and R&D projects.
Next, we describe how SL tools can be used to analyze company growth and
performance. Finally, we review SL applications to better forecast financial
distress and company failure. In the concluding Section, we extend the
discussion of SL methods in the light of targeted policies, result
interpretability, and causality.
