Event study with imperfect competition and private information: earnings announcements revisited

Abstract

Although prior research documents that prices respond to earnings announcements, only a little of the price variation is explained by these announcements. To further investigate the properties of the information environment around these announcements we use NYSE TAQ data and compute the maximum likelihood estimates (MLEs) of the primitive parameters of a Kyle (Econometrica 53(6):1315–1336, 1985) type model within and around earnings announcement windows. These include the precision of fundamentals given only public information, the precision of private signals, and the variance of uninformed liquidity trading (noise). We find that liquidity noise is higher while the precision of beliefs given only public information is lower within an earnings announcement window. The precision of private information is higher in an event window, consistent with greater information acquisition to try and interpret a public announcement. We also document that Kyle’s λ is higher in an event window, showing an overall increase in information asymmetry. Our overall findings suggest that the earnings announcement window is distinguished from the preceding and subsequent windows not by being a period with more public information but as a period with different public information.