Techniques for Data Analysis Event Study

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

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.

2. What are the empirical questions under investigation? Page 44 How are these questions raised?

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.

3. 1 What is the methodology and research design 3.1 What is the methodology and research design? – Market Residual Technique Page 46 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

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

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.

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.

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 Page 47 Page 52 Is the buy recommendation anticipated by the market? How long does it take for the recommendation to be fully disseminated to the market?

4.1 What are the findings? – Evidence & Interpretation Page 47 Can the impact of the publication of the report on share prices be quantified?

4.2 What are the findings? – Evidence & Interpretation Page 48 Are these daily average residuals significant?

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 Page 50

4.4 What are the findings? – Evidence & Interpretation d.f. = 597-2 = 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?

4.5 What are the findings? – Robustness Test Page 51

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

4.5 What are the findings? – Robustness Test Page 51 Reject H0 Do not reject H0 = 1.645 tn-2,α 13.18

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?