  We reconstruct the innovation dynamics of about two hundred thousand
companies by following their patenting activity for about ten years. We define
the technological portfolios of these companies as the set of the technological
sectors present in the patents they submit. By assuming that companies move
more frequently towards related sectors, we leverage on their past activity to
build network-based and machine learning algorithms to forecast the future
submission of patents in new sectors. We compare different evaluation metrics
and prediction methodologies, showing that tree-based machine learning
algorithms overperform the standard methods based on networks of
co-occurrences. This methodology can be applied by firms and policymakers to
disentangle, given the present innovation activity, the feasible technological
sectors from those that are out of reach, given their present innovation
activity.
