1. Techniques for Data Analysis Event Study

2. What are we going to do in today’s lecture?
Central Topic: Event Study
Stock Prices and the Publication of Second-Hand Information, (Lloyd Davies & Canes)
Summary
1. What is the overall objective of the study?
2. What are the issues under investigation?
3. What is the methodology & how is the research designed to investigate the issues in questions ?
4. What are the findings of the study & how to present and interpret the evidence?
Conclusion

3. Behaviour of stock prices
1. What is the overall objective of the study?
Stock prices
Publication of stock analysts’ recommendations (to buy/to sell)
Impact
Behaviour of stock prices
An event study refers to an empirical investigation of the relationship between security prices and firm-specific events.
Disclosure of firm-specific events
Relationship
Abnormal return on security j
Event i
Abnormal return on security j conditional on the disclosure of event i equals zero
Event i has no significant impact upon prices of security j.

4. 2. What are the empirical questions under investigation?
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How are these questions raised?

5. 2. What are the empirical questions under investigation?
Market efficiency hypothesis
4. Are initial stock-price adjustments following a false recommendation later reversed?
1. Do prices adjust when an analyst revises his stock recommendation?
2. Does rate of adjustment depend on how widely recommendation is disseminated?
3. Do buy & sell recommendations have different stock market effects?
Stock market adjusts in an efficient manner to the arrival of new information
Single-investor arbitrage model
As the information becomes more widespread, further price adjustments will occur.
Impact of capital gain tax & interest
Sell Recommendations may have smaller impact on stock price than buy recommendations.
Stock returns are ultimately determined by cash flows
Any abnormal price increase(decrease) that results from a false recommendation must be followed by abnormally low (high) returns later on.

6. 3. 1 What is the methodology and research design
3.1 What is the methodology and research design? – Market Residual Technique
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The Market Model (MM) assumes that returns are generated according to the following mechanism:
Return =
Systematic component linearly related to market return
Unsystematic component uncorrelated with market return
Return on security j in period t
Market return in period t
Random error in period t
Effect of firm-specific events
Estimated abnormal return on security j in period t
Observed return on security j in period t
Estimated return on security j in period t

7. 3.2 What is the methodology and research design? – Abnormal Return
Q1: How can the effect of the event of interest be isolated from other factors?
Systematic component linearly related to market return
Unsystematic component uncorrelated with market return
General market-wide economy effects on stock return
variation in return left unexplained by market-wide movements
Q2: How to obtain abnormal return?
Estimate normal returns using the market model outside event window & compare actual returns during event window with normal returns
Calculate actual returns and market returns using the data collected
Regress on to estimate market model coefficients
Estimate expected (normal) returns using
Estimate abnormal returns using

8. Test Period (Event Window)
3.3 What is the methodology and research design? – Event Window & Estimation Period
Estimate market model coefficients & expected (normal) return
Test the impact of event of interest on the behaviour of share prices
t = -T
Estimation
Period
Test Period (Event Window)
Estimation Period
t = E
t = -s
Event Date, t = 0
t = r
Trade-off
Width of the window
Accuracy
Reliability of estimation
The nature and dates of at least four significant (preferably related) information events occurring over a relatively short period of time (not more than eight months) can be identified.
Three years of continuous daily stock price data, beginning at least two years before the first information event and ending not less than three months after the final information event, are available for analysis.

9. 3. 4 What is the methodology and research design
3.4 What is the methodology and research design? – Calculations of returns
Observed return on security j in period t
Observed market return in period t
Price of security j at the end of period t
Dividends paid during period t
Price of security j at the end of period t-1
Theoretically logarithmic (geometric) returns are better at capturing the compound effect of rate of return.
Empirically the logarithmic returns are more likely to be normally distributed and so conform to the assumptions of standard statistical techniques.

10. 3. 5 What is the methodology and research design
3.5 What is the methodology and research design? – Cumulative Abnormal Return
Almost all event studies call for abnormal returns to be cumulated over a number of periods (Test Periods).
Cumulative abnormal return for day k is defined as
k = -20……20
Q: Why CARs?
To fully capture the effect of an event on share prices;
To accommodate uncertainty over the exact date of the event
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Is the buy recommendation anticipated by the market?
How long does it take for the recommendation to be fully disseminated to the market?

11. 4.1 What are the findings? – Evidence & Interpretation
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Can the impact of the publication of the report on share prices be quantified?

12. 4.2 What are the findings? – Evidence & Interpretation
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Are these daily average residuals significant?

13. 4.3 What are the findings? – Evidence & Interpretation
To formally test whether the price adjustment on the event date is significant, hypothesis testing is needed.
For buys
For sells
with n-2 degrees of freedom
Standard deviation of abnormal returns
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14. 4.4 What are the findings? – Evidence & Interpretation
d.f. = = 595
t595,0.05 = 1.645
Reject H0
Do not reject H0
= 1.645
tn-2,α
9.6
The average abnormal return of 0.923% is significant at 5% level.
How robust is the result?

15. 4.5 What are the findings? – Robustness Test
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16. 2.3 How to apply least squares analysis to obtain a linear regression model?
b0 and b1 are obtained by minimising the sum of squared differences between y and :
Slope coefficient estimator
Y-intercept estimator
These functions provide point estimates of slope & intercept of straight line
Assumptions about the error term:
The error terms, εi are independent of the x values;
The error terms are random variables with mean 0 and constant variance, σ2
3. The random error terms, εi, are not correlated with one another, so that
Equal variance (Homoscedasticity)
Normality
Independence of errors

17. 4.5 What are the findings? – Robustness Test
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Reject H0
Do not reject H0
= 1.645
tn-2,α
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18. 4.6 How to present your finding?
1. What is the evidence out of your study?
2. What does your evidence suggest?
3. Is the evidence in your study significant enough to support conclusion?
4. Are there any other factors that might affect validity and significance of your evidence?
5. If yes, what are your consideration and possible solution?