Research

Informational economic transmission  is important (even after controlling for countries’ fundamental real and financial linkages), where informational connections emerge from an anomalous interdependence in agents’ beliefs about countries’ economic performance. We propose a novel measure of informational interdependence: the correlation (between countries) of analysts' GDP one-year-forecast errors. Our measure is based on a learning model, where informational transmission arise when agents learn, but over-weight information about common factors instead of country-specific information (due to frictions in the learning process).  Informational interdependence is substantial, after performing several validity analyses and robustness checks. Furthermore, we show considerable higher-order spillovers of economic shocks.

We empirically study how excess comovement in stock returns can be explained by stock-to-stock interdependence through informational links. Informational linkages emerge from anomalous interdependence in agents' beliefs about stocks' economic performance. We propose a novel measure for informational linkages based on agents' learning process, which is biased towards common information due to learning frictions, generating correlated beliefs across stocks. We empirically measure these correlated beliefs based on analysts' forecast errors. Our results show that informational connections explain stock returns after cleaning for fundamental connections and controlling for various explanations already studied in the literature. We use our estimated informational linkages to study the propagation of a climate event and simulated shocks through the stock market, finding quantitatively important indirect effects.