This paper focuses on the impact that dispersion of opinions and asymmetric information have on turnover near releases of public information, using the probability of information-based trading (PIN) to proxy for information asymmetry and analysts' forecast dispersion for differences of opinion. For earnings announcements of US firms, I find that a one standard deviation increase in dispersion accelerates trading, reducing the difference between turnover around and before announcements by 8.50%. A similar increase in the PIN delays trading, raising the difference by 8.29%. These results help to explain why a large number of events have high turnover before earnings announcements relative to turnover after their release. Furthermore, the information contained in the time-series difference between trading around and before announcements helps to separate the impact of information asymmetry from the impact of proxies for differences of opinion. I also present a theoretical model in which agents who receive private information of heterogeneous quality trade a stock before and after observing a public signal. This public signal is interpreted differently across agents, leading to differences of opinion. I obtain closed-form solutions for expected aggregate volume and its derivatives with respect to these variables, showing that extending static models of asymmetric information is not enough to match the empirical findings.
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Paper provided by IESE Business School in its series IESE Research Papers with number
D/747.
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