Semantic Folding helps improve prediction of stock return correlations

13 Apr 2016

A recent academic study conducted by researchers from Leiden, Ben-Gurion and Toulouse Universities examined the performance of’s Semantic Folding approach for content analysis in a finance setting. Compared to the commonly used word-list method, Semantic Folding proved to have greater predictive power. Its other advantages were speed and ease of use.

“Like the human brain, our Semantic Folding engine learns a language and understands the meaning of text by making analogies. Like the brain, it is both efficient and accurate. We are thrilled to see these compelling results confirmed by an independent academic study”, comments Francisco Webber, inventor and co-founder of

The research team used’s Retina API to create semantic fingerprints of the 30 Dow Jones Industrial Average constituents, based on business description sections of the companies’ annual reports. For each pair of companies, the similarity of their semantic fingerprints was compared to predict correlations between their stock returns over the following year.

The study found Semantic Folding to have greater predictive power than the traditional word-list based approach. Moreover, fingerprint similarity continued to significantly predict stock return correlations even when other measures of company similarity were controlled for.

The authors contend that Semantic Folding is simpler to use, has lower setup costs, and runs faster than the standard word-list based method. In addition, semantic fingerprints were considered to have an appealing visual interpretation. The authors argue that Semantic Folding significantly lowers the entry barriers for investigators interested in applying content analysis to financial data. To this end, the study includes sample code and suggests possible applications of’s Semantic Folding engine in several finance contexts.

The study, entitled “Using Semantic Fingerprinting in Finance” is available here.