In this research we investigate the role of information-based trading in affecting asset returns. Our premise is that in a dynamic market asset prices are continually adjusting to new information. This evolution dictates that the process by which asset prices become informationally efficient cannot be separated from the process generating asset returns. Using the structure of a sequential trade market microstructure model, we derive an explicit measure of the probability of information-based trading for an individual stock, and we estimate this measure using high-frequency data for NYSE-listed stocks for the period 1983-1998. The resulting estimates are a time-series of individual stock probabilities of information-based trading for a very large cross section of stocks. We investigate whether these information probabilities affect asset returns by incorporating our estimates into a Fama-French  asset pricing framework. Our main result is that information does affect asset prices: stocks with higher probabilities of information-based trading require higher rates of return. Indeed, we find that a difference of 10 percentage points in the probability of information-based trading between two stocks leads to a difference in their expected returns of 2.5% per year. We interpret our results as providing strong support for the premise that information affects asset pricing fundamentals.
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