Coarse Categories in a Complex World

Real-world news environments comprise both granular quantitative information and coarse categorizations. For instance, company earnings are reported as a dollar figure alongside categorizations, such as whether earnings beat or missed market expectations. When processing capacity is limited, these components may compete for attention. We study the hypothesis that more severe processing constraints increase the relative reliance on coarser signals: people still discriminate between categories but distinguish less granularly within them, creating higher sensitivity around category thresholds but lower sensitivity elsewhere. Using stock market reactions to earnings announcements as our empirical setting, we document that hard-to-value stocks are associated with a more pronounced S-shaped response pattern around category thresholds. Naturalistic experiments that exogenously manipulate processing constraints provide supporting causal evidence in individual investor behavior. We then study two determinants of processing constraints in the field. First, more common sizes of surprise may be processed more precisely. Indeed, regions with more historical mass exhibit far higher return sensitivity. Second, a surprise about the category realization may capture attention, leaving less capacity to process the numerical signal. We find that category surprises, e.g., a profit when a loss was expected, are associated with diminished sensitivity to numerical earnings information.