1. Empirical Financial Economics 3. Semistrong tests: Event Studies Stephen Brown NYU Stern School of Business UNSW PhD Seminar, June 19-21 2006

2. Outline  Efficient Markets Hypothesis framework  Standard Event Study approach  Brown/Warner  Systems Estimation issues  Asymmetric Information context  FFJR Redux

3. Efficient Markets Hypothesis which implies the testable hypothesis... where is part of the agent’s information set In returns: wher e

4. Examples  Random walk model  Assumes information set is constant  Event studies  For event dummy (event)  Time variant risk premia models  z t includes X  Important role of conditioning information

5. Efficient Markets Hypothesis  Tests of Efficient Markets Hypothesis  What is information?  Does the market efficiently process information?  Estimation of parameters  What determines the cross section of expected returns?  Does the market efficiently price risk?

6. Standard Event Study approach 05 10152025 30 t r t1 r t2 r t3 r t4 u 01 u 11 u 21 … u 02 u 12 u 22 … u 03 u 13 u 23 … u 04 u 14 u 24 … u 05 u 15 u 25 … EVEN T

7. Orthogonality condition Event studies measure the orthogonality condition using the average value of the residual where is good news and is bad news If the residuals are uncorrelated, then the average residual will be asymptotically Normal with expected value equal to the orthogonality condition, provided that the event z t has no market wide impact

8. Fama Fisher Jensen and Roll

9. Brown and Warner  Model for observations:  Also considered quantile regressions, multifactor models

10. Block resampled bootstrap procedure 05 10152025 30 t r t1 r t2 r t3 r t4 Choose securities at random

11. Block resampled bootstrap procedure 05 10152025 30 t r t1 r t2 r t3 r t4 EVENT(chosen at random) Choose ‘event dates’ at random

12. Block resampled bootstrap procedure 05 10152025 30 t r t1 r t2 r t3 r t4 EVENT(chosen at random) Test period Estimation period Test period Estimation period Test periodEstimation period Check if sufficient data exists around ‘event date’

13. Basic result Actual level of Abnormal Performance at day “0” Method00.0050.010.015 Mean adjusted return6.4%25.2%75.6%99.6% Market Adjusted return 4.826.079.699.6 Market Model4.427.280.499.6

14. Loss of power when event date uncertain Days in Event period Level of abnormal performance Method00.010.02 Mean adjusted return11 1 4.0% 6.4 13.6% 75.6 37.6% 99.6 Market Adjusted return 11 1 4.0 4.8 13.2 79.6 32.0 99.6 Market Model11 1 2.8 4.4 13.2 80.4 37.2 99.6

15. Misspecification when events coincide Level of abnormal performance Method00.010.02 Mean adjusted return Clustering Nonclusteri ng 13.6% 4.0 21.2% 13.6 29.6% 37.6 Market Adjusted return Clustering Nonclusteri ng 4.0 14.4 13.2 46.0 32.0 Market ModelClustering Nonclusteri ng 3.2 2.8 15.6 13.2 46.0 37.2

16. Schipper and Thompson Analysis The best linear unbiassed estimator of is where  is the difference in average return between announcement and non announcement periods, and   is the regression coefficient of the event dummy on the market However, event study procedure assumes   = 0

17. Systems estimation interpretation, with error covariance matrix  oror

18. Gain from systems estimation GLS estimator is  No gain in efficiency if  Events differ in calendar time ( diagonal)  All events occur at same time ( )  Gain in efficiency if constant across securities  Is this reasonable?

19. Sons of Gwalia example A a Claim AssayReport ( oz/ton) Operations Market observes decision s, but not assay report,value to corporation Market equilibrium requires

20. Event study implication This implies that which gives the return model How do we get ?

21. Justification for corporate finance event study application  Gwalia will dig if assay report is high enough  A standard Probit model  Taylor series expansion justification for cross section regression of excess returns on firm characteristics

22. FFJR Redux

23. Original FFJR results