Abstract
We show that outcomes (parameter estimates and R-squareds) of regressions of prices on fundamentals allow us
to recover exact measures of the ability of asset prices to aggregate dispersed information. Formally, we show how to
recover absolute and relative price informativeness in dynamic environments with rich heterogeneity across investors
(regarding signals, private trading needs, or preferences), minimal distributional assumptions, multiple risky assets,
and allowing for stationary and non-stationary asset payoffs. We implement our methodology empirically, finding
stock-specific measures of price informativeness for U.S. stocks. We find a right-skewed distribution of price
informativeness, measured in the form of the Kalman gain used by an external observer that conditions its posterior
belief on the asset price. The recovered mean and median are 0.05 and 0.02 respectively. We find that price
informativeness is higher for stocks with higher market capitalization and higher trading volume.