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Event Studies on a Small Stock Exchange

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Conducting Event Studies on a Small Stock (Bartholdy , Olson and Peare (2007))
Research Question
In the study, the authors want to examine the efficiency of event studies when this methodology is applied to thinly traded stocks. The event studies are usually conducted to see whether abnormal price changes can be detected. The contribution of the authors are in thin traded stocks context.
Assumptions:
In the study, for application of one methodology author assumed that the returns are normally distributed and there is no cross-sectional dependence between stock returns. The simulation study for event days are implemented by assuming uniform distribution.
Methodology and Data
The methodology involves the use of CAPM model to estimate the returns of stocks. The stock portfolio is composed of 10, 25 and 50 stocks. Because of thin trade stock motivation the trade to trade adjustment was made. The estimation is based on 250 trading days total and 247 of this period corresponds to parameter estimation and the rest, t-1, t, t+1 is used for event study. The parameter estimations were based on OLS with heteroskedasticity correction. The abnormality is detected by analyzing difference between market return and expected return from CAPM model. Returns for event days are the subjects of test statistics. The examined statistics were based on t-test with cross sectional independence, t-test with standardized abnormal return and t-test with adjusted standardized abnormal return.
These tests are the parametric tests for abnormality, the authors also conducted non-parametric test such as rank test, sign test and generalized sign test. The event days are specified by simulation and uniform distribution is assumed. After event day specification the impact of 0.5% and 2% are added to abnormal return on the event day. The simulation is repeated 1000 times to achieve a distribution of abnormal returns for each stock. The stock data is taken from Copenhagen Stock Exchange.
Conclusion
The paper suggests that event studies can be done after some adjustments. The statistical power of the abnormal return detection is material after 25 event day determination. Authors suggest the success of non-parametric tests over parametric t-tests because of non-normality of returns except the case of event caused volatility. But when non-normality, variance change, unknown event day and thin trade is considered no tests are superior to other was found. As a result, power is high in thick traded stocks and low in thin traded stocks.

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