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Despite the importance of interregional trade for building effective regional economic policies, there are very few hard data to illustrate such interdependencies. We propose here a novel research framework to predict interregional trade flows by utilizing freely available Web data and machine learning algorithms. Specifically, we extract hyperlinks between archived Websites in the United Kingdom and we aggregate these data to create an interregional network of hyperlinks between geolocated and commercial Web pages over time. We also use existing interregional trade data to train our models using random forests and then make out-of-sample predictions of interregional trade flows using a rolling-forecasting framework. Our models illustrate great predictive capability with R2 greater than 0.9. We are also able to disaggregate our predictions in terms of industrial sectors, but also at a subregional level, for which trade data are not available. In total, our models provide a proof of concept that the digital traces left behind by physical trade can help us capture such economic activities at a more granular level and, consequently, inform regional policies. [ABSTRACT FROM AUTHOR]