Bilateral Home Bias: A New Measure of Proximity
28 Pages Posted: 4 Nov 2020 Last revised: 28 Aug 2022
Date Written: September 11, 2020
This paper applies a recent technology grounded in 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 quantifying the similarity of country descriptions. 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 a given term or text). This provides empirical researchers with the opportunity to quantify and compare texts of any length, with extreme efficiency. In a model for bilateral home bias, measures of country similarity (based on the overlap of semantic fingerprints of economic descriptions of country pairs) 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.
Keywords: Home Bias, Gravity Models, International Portfolio Choice, Textual Analysis, Natural Language Understanding
JEL Classification: G11, G15
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