We infer the latent social networks of investors using data on their stock holdings. We map linkages to portfolio weights using a portfolio-choice model. The precision of an investor’s private signal about firm value is assumed to increase with his connections in the city where the firm is headquartered. Using money-manager data, we find that managerial linkages to a city are overly dispersed relative to the Erdös–Rényi model of i.i.d. connections. Managers at the tail of this distribution with non-i.i.d. linkages have more university alumni in that city. Their stock holdings there outperform their holdings in other cities.

Measuring the value of labor-market hires for stock prices, be it underwriters when firms go public (IPOs) or chief executive officers (CEOs), is difficult due to selection. Opaque firms with higher costs of capital benefit more from prestigious underwriters, while productive firms benefit more from talented CEOs. Using assignment models, we show that the importance of talent (or agent heterogeneity) relative to selection (or firm heterogeneity) is measured by wage increases across agents of different compensation ranks divided by changes in output across their firms. The median of this ratio is 0.5% for underwriters and 2% for CEOs.

ABSTRACT Event studies of market efficiency measure earnings surprises using the consensus error ( CE ), given as actual earnings minus the average professional forecast. If a subset of forecasts can be biased, the ideal but difficult to estimate parameter‐dependent alternative to CE is a nonlinear filter of individual errors that adjusts for bias. We show that CE is a poor parameter‐free approximation of this ideal measure. The fraction of misses on the same side ( FOM ), which discards the magnitude of misses, offers a far better approximation. FOM performs particularly well against CE in predicting the returns of U.S. stocks, where bias is potentially large.