Working Paper: NBER ID: w25210
Authors: Eduardo Dvila; Cecilia Parlatore
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.
Keywords: Price Informativeness; Financial Markets; Information Aggregation
JEL Codes: D82; D83; G14
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Linear regressions of prices on fundamentals (C29) | Recovery of absolute and relative price informativeness (E30) |
| Variance of the error term in the regression (C21) | Absolute price informativeness (D41) |
| Difference in R-squared values between regressions that include and exclude lagged fundamentals (C29) | Relative price informativeness (D41) |
| Price characteristics (larger market capitalizations and higher trading volumes) (G10) | Price informativeness (G14) |
| Kalman gain of 0.2 (C69) | Weighs the information contained in the price by 20% relative to prior beliefs (D80) |
| Right-skewed distribution of price informativeness among U.S. stocks (D39) | Mean Kalman gain of 0.05 and median of 0.02 (C46) |