Event Studies and Tasting Scores Presentation at the Western Economic Association Vancouver, BC, July 1, 2004

1. Event Studies and Tasting ScoresPresentation at the Western Economic AssociationVancouver, BC, July 1, 2004 Robert Eyler Department of Economics Sonoma State University eyler@sonoma.edu

2. Introduction Event Studies for finance generally Focus on equity prices and changes after an event. Wine industry has few publicly-traded firms. Even fewer non-conglomerates, or affected by one tasting score. Can we detect any shift in a business indicator, like sales dollars, after an event?

3. Intent Use event study methodology to examine sales data for certain wineries versus industry sales. Looking of abnormal returns, those not predicted by market cycles. Looking for significant magnitudes and duration. Looking to see if certain characteristics of wines lead to larger effects.

4. Hedonic Demand Quality a determinant (shifter) of demand Much debate over how quality enters demand function. Generally for durable goods Grichiles (1971), Gordon (1973), Epple (1987) Wine industry has some studies Nerlove (1995), Oczkowski (2001), Schamel and Anderson (2003) Main idea is that changing quality perception can change demand, thus revenue.

5. Event Study Models Fama, et al (1969) seen as seminal study MacKinlay (1997) a great survey Using industry as a baseline, much like a composite stock index, Do abnormal revenues come from tasting events? Are they significantly different from zero? How long do they last? How intuitive are the results?

6. Modeling Market Model Looking for both the abnormal revenues and cumulative abnormal revenues. Time period specific and building over time. Estimation much like security market line. Revtk = ?k + ?kRevtm + etk (Equation 1) Added lagged revenue for serial correlation. Some wines dropped out due to insignificance.

7. Abnormal Revenues Difference between prediction and reality Assumed to be normally distributed, because error term in OLS is normal (Gauss-Markov) Using statistical properties, need to t-test for significance of abnormality Cumulative Abnormal Revenue (CAR) Shows increasing over time, should peak Also need to test for significance.

8. Results Sample wineries show little significant differences from market average after event Used normalization of data to compare all as equals. Percentage shock not significant in total t-tests are sensitive to number of firms As firms rise, assuming same distribution, significance increases. Further research needs to include more wineries.

9. Clusters Tasting Score Cluster Significance in lower tasting scores only Makes intuitive sense, as these wines will have a larger �elasticity� to quality rating than other wines. Should have seen some significance in higher scores also. Need large samples again.

10. Clusters (cont.) Price of Wines Lower priced wines negative affected t-tests insignificant for all but lower priced wines. Makes limited sense. Combination of tasting score and price made little difference.

11. Individual Results 5 of remaining 29 wineries had significant results individually. No characteristic of the wine (origin, tasting score, price, production level, vinatge) showed up as a consistent factor. Caveats other than sample size Other events possible Some wines may have a contractual market, thus little change possible.

14. Conclusions Important topic for this industry Reduction of adverse selection and moral hazard problems for customers large Market effects of tasting scores need more understanding. Need to have larger sample size Randomness in quality ratings key, as in sample wineries. This study intends to expand sample, but results should only become more significant Duration also a factor: assumed here.