Empirical Financial Economics

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: where

4. Examples • Random walk model • Assumes information set is constant • Event studies • For event dummy (event) • Time variant risk premia models • zt includesX • 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 EVENT rt1 u01 u11 u21 … EVENT rt2 u22 … u02 u12 EVENT rt3 u03 u13 u23 … EVENT EVENT rt4 u25 … u04 u14 u24 … u05 u15 30 10 15 20 25 0 5 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 eventzt 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 rt1 rt2 rt3 rt4 30 10 15 20 25 0 5 t Choose securities at random

11. Block resampled bootstrap procedure EVENT(chosen at random) rt1 EVENT(chosen at random) rt2 EVENT(chosen at random) rt3 EVENT(chosen at random) EVENT(chosen at random) rt4 30 10 15 20 25 0 5 t Choose ‘event dates’ at random

12. Block resampled bootstrap procedure EVENT(chosen at random) rt1 Estimation period Test period EVENT(chosen at random) rt2 Test period EVENT(chosen at random) rt3 Estimation period Test period EVENT(chosen at random) EVENT(chosen at random) rt4 Estimation period Test period Test period 30 10 15 20 25 0 5 t Check if sufficient data exists around ‘event date’

13. Basic result

14. Loss of power when event date uncertain

15. Misspecification when events coincide

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  or

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 Claim AssayReport ( oz/ton) Operations a A ,value to corporation Market observes decision s, but not assay report 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