Abstract
We reconstruct the innovation dynamics of about two hundred thousand companies by following their patenting activity for about ten years. We define the technology 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 their past activity to build network-based and machine learning algorithms to forecast the future submissions of patents in new sectors. We compare different prediction methodologies using suitable evaluation metrics, showing that tree-based machine learning algorithms outperform 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.
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