Linking asset prices to news without direct asset mentions

Advances in Natural Language Processing (NLP), computing power and data availability are driving an explosion in research about the impact of news on asset prices. However, when relating news to individual assets, this research is based on mentions of specific assets or related terms in the news stories. Such an approach has two shortcomings. First, it requires a substantial time investment in a specific NLP technology. Second, and more importantly, it ignores news articles that do not directly mention a given asset or a pre-defined asset-related term, even if these articles are logically related to the asset in question. Our approach relies instead on a novel NLP technology called 'semantic fingerprinting', which projects any text onto a binary vector representing its meaning. The greater the overlap between the semantic fingerprint of a news article and a given asset description, the more relevant we expect the article to be, whether or not the given asset is mentioned in the news directly. We show that this approach successfully picks up the positive impact of news on prices of commonly traded commodities using a dataset of general news published by The Guardian. We include the needed data and instructions for implementing this approach.