Value of Latent Information: Alternative Event Study Methods



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    • Board of Governors of the Federal Reserve System. Opinions expressed in this paper do not necessarily represent the official position of the Board of Governors of the Federal Reserve System or its staff. The author is thankful to Stephen Brown, Gregory Connor, Doug Diamond, Ian Domowitz, Phil Dybvig, Edwin Elton, Shelby Haberman, Milton Harris, Robert Hodrick, Ravi Jagannathan, Avner Kalay, Robert Korajczyk, Craig MacKinley, Steve Sharpe, René Stulz, and Joe Williams; and to the participants of the seminars at Columbia, Duke, Florida, Indiana, Michigan, Minnesota, NYU, UCLA, Toronto, Utah, Washington-Seattle, and at the American Financial Association, the Western Financial Association, and the Econometric Society meetings.


This paper presents an econometric model to value latent information underlying corporate events. This model computes the market's inference of the value of latent information from the probability of an event, conditional on firm-specific, preevent information. It provides a convenient framework for testing significance of preevent information variables, such as accounting attributes and lagged stock return. Simulations show that this model, when applied to both event and preevent period data, can decrease the incidence of bias in event studies. If restricted to only event period data, this model reduces to a truncated regression and does not perform as well as standard procedures.