摘要 : This article applies a recent technology grounded in cognitive neuroscience and psycholinguistics to introduce a new measure of proximity to the set of typical gravity variables in a model for bilateral home bias. Given the “weigh... 展开 This article applies a recent technology grounded in cognitive neuroscience and psycholinguistics 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 artificial intelligence (Al) solution that aims to emulate the way the human brain learns and establishes associations among concepts, called the Retina engine, is designed to analyze any given text with human-level accuracy and to extract its “semantic fingerprint” (a numerical representation of meanings associated with the 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. 收起