TAILIEUCHUNG - USING DAILY STOCK RETURNS - The Case of Event Studies*

In this paper, we focus our attention on providing a formal framework for expressing data mining tasks in- volving time granularities, and on proposing efficient algo- rithms for performing such tasks. To this end, we introduce the notion of an event structure. An event structure is essen- tially a set of temporal constraints on a set of variables representing events. Each constraint bounds the distance between a pair of events in terms of a time granularity. For example, we can constrain two events to occur in a prescribed order, with the second one occurring between four and six hours after the first but within the same busi- ness day. We consider. | Journal of Financial Economics 14 1985 3-31. North-Holland USING DAILY STOCK RETURNS The Case of Event Studies Stephen J. BROWN Yale University New Haven CT 06520 USA Jerold B. WARNER University of Rochester Rochester NY 14627 USA Received November 1983 final version received August 1984 This paper examines properties of daily stock returns and how the particular characteristics of these data affect event study methodologies. Daily data generally present few difficulties for event studies. Standard procedures are typically well-specified even when special daily data characteristics are ignored. However recognition of autocorrelation in daily excess returns and changes in their variance conditional on an event can sometimes be advantageous. In addition tests ignoring cross-sectional dependence can be well-specified and have higher power than tests which account for potential dependence. 1. Introduction This paper examines properties of daily stock returns and how the particular characteristics of these data affect event study methodologies for assessing the share price impact of firm-specific events. The paper extends earlier work Brown and Warner 1980 in which we investigate event study methodologies used with monthly returns. In our previous work we conclude that a simple methodology based on the market model is both well-specified and relatively powerful under a wide variety of conditions and in special cases even simpler methods also perform well. However the applicability of these conclusions to event studies using daily data is an open question . Brown and Warner 1980 p. 21 Masulis 1980 p. 157 Dann 1981 p. 123 DeAngelo and Rice 1983 p. 348 McNichols and Manegold 1983 p. 58 . Daily and monthly data differ in potentially important respects. For example daily stock returns This work has benefitted from suggestions of colleagues at a number of seminars particularly at Rochester Yale and the University of Southern California Conference on Event Study .

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