Bilateral Home Bias: A New Measure of Proximity

Abstract

This paper uses recent developments from the fields of cognitive neuroscience and psycho-linguistics to introduce a new measure of proximity to the set of typical gravity variables in a model for bilateral home bias. Given the `weightlessness' of financial assets, gravity variables (among which geographical distance is the most prominent) are meaningful only as proxies for information or familiarity. The new measure of country similarity aims to directly capture the conceptual closeness of countries by comparing their semantic fingerprints. An AI solution which emulates the way the human brain learns and establishes associations among concepts, called the Retina engine, makes it possible to analyse text with human-level accuracy and to extract its semantic fingerprint (a numerical representation of meanings associated with the given term or a text). Akin to an artificial `investor' who has read virtually the entire Wikipedia, the Retina engine is able to quantify and compare textual descriptions of any pair of countries. In a model for bilateral home bias, the resulting measures of country similarity appear informative above and beyond distance and other gravity variables (common language, border, colonial link etc.). At its best, country similarity outperforms distance both in terms of statistical significance and impact on the dependent variable.