Briefly Describe The Event Study Methodology Procedure (Essay Sample)

Event study methodology
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August 2017
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Event Study methodology procedure
Step 1
The first step in event study methodology involves explicitly defining the event which the study aims to investigate its economic effect. According to MacKinlay (1997) the event could affect a single company such as announcement of earnings or affecting multiple companies in case of merger or acquisition. Additionally, the event under investigation should be new and unexpected in the public arena, otherwise there will not be expected considerable reaction of stock prices of the firm. According to Tong (2010), the event under evaluation may either be within firms control such as divided distribution and bonus share, or outside the firms control such as macroeconomic announcement that in some way affect a company’s operation. When such consideration is undertaken in choosing an event, it is assumed that that under efficient market hypothesis, the economic effect of an event will be immediately be reflected in stock prices (Shaheen, 2006)
Step 2
At this stage, the researcher should specify the date of the event. Such date is defined by Sitthipongpanich (2011, 62) as the announcement date of the event i.e the first day of trading when the event occurrence became public information. As such, if event announcement is done during a non-trading day or the time after the trading has closed, the next trading day then becomes the event day and if announcement is done during a trading day then that day becomes the event day. To mitigate the problem of arriving at biased results, the researcher should also drop any confounding events affecting a company. This is because some news regarding a firm may be released jointly and systematic. And even if a large sample is taken, Park (2004) notes that such systematic bias will not disappear from the results. For instance, if the analysis involve stock splits, the stock splits event announced together with other news of a company should be dropped to make sure that the economic effect of such event is due to the event alone.
Step 3
The third steps involve identifying the time line that an event will be studied. The timeline involves selection of the test period usually referred to as event window and estimation period (Ahern, 2009). The estimation window is will be used to evaluate the expected behaviour of company’s equity returns. The impact on stock by an event will be evaluated in the window period that incorporates some time before and after event announcement date; pre-event window and post-event window (MacKinlay, 1997). But this step possess one main challenge to a researcher; the choice of appropriate lengths and points of the estimation of event window. According to Corrado (1989) there is no definite rule for allocating the period length of event window for event study's estimation. However, while the choice of these parameters discretionary lies on the researcher, a balance of trade-off between potential parameter shift and improved accuracy in estimation should be stroked. This is because, according to Ahern (2004) longer window period usually leads to greater accuracy since they encompass large data. On the other hand such period elevate the risk of including other confounding events that can distort the accuracy of estimated parameters and overall economic effect inferences. As such, if confounding events are identified it is recommendable to adopt a short period while a long period is preferred if such events do not exist. However, according to Park (2004), the results from event study when there exist no confounding factors are not sensitive to changing window lengths as long as the window period is over 100 days. Further, the researcher should make sure that any two windows should not overlap.
Step 4
Armed with specific study event, event time, window and estimation period, the next step involves collecting data on stock prices retrieving the stock price data for the pre and post estimation period as well as well as estimation period. By using company identifiers, the researcher should retrieve stock price data for the company or companies under interest. However, the researcher should be aware of company identifiers because most databases uses the current firm identifier and this could change due to mergers, acquisition, rebranding or change of name. By using the defined event and its date, sample companies’ stocks can be chosen and classified into different groups; high, low or medium. The data should also be obtained or reclassified in the correct time format that reflect periodic measure of window period. in the For instance, if the researcher is interested with economic effect of earning announcement, the data collected would be daily stock value and the event study would be on the earnings announcement. Sitthipongpanich (2011) finds that if the event type relates to a specific firm and multiple firms are to be included in the study, then the event date vary for each firm. As such it is necessary for the researcher to record data for each sample company in accordance to its event date. Armed with event, event date, estimation window, event window and stock data the researcher will now be able to calculate abnormal returns to evaluate economic effect of the event.
Step 5
The next step in event study methodology involves the choice of the models to be used to determine abnormal returns; the returns associated with the event of interest at the date of announcement. In this practice the researcher should first calculate the expected returns for the event under study date, defined by Park (2004) as theoretical return that would have been experienced if the event under examination would not have occurred. The abnormal return is simply the difference between the expected return and the returns experienced on the day of event. The next section explains how abnormal returns are obtained.
Step 6
Estimation of expected returns (Normal Returns)
One of the most important feature of event study methodology regards the choice of the model to be use in estimating the expected returns; returns in absence of an event. A number of models that include market model, constant mean return model, multifactor model and Capital asset pricing model (CAPM). According to Barber and Lyon (1997) constant mean return model and the market model are the most commonly used model to estimate expected returns due to its simplicity. Additionally, Brown and Warner (1985) points out that simple mean returns model often produces no different results from those from more sophisticated models because by choosing a more sophisticated model does not reduce the variance of abnormal returns.
By assuming that expected normal return by company but are constant over time, the constant mean model is defined as;
For each stock I at time T within the window period where;
ui= , E[] =0 and var ()=
As opposed to the constant mean model, the market model relates the return of any specific security to that of market portfolio MacKinlay (1997). The model assumes joint normality of asset in its linear specification and is defined as follows;
, E[]=0, var()=σ2 where;
is the expected return, is the is the market portfolio for asset i. According to MacKinlay (1997) this model presents an improvement of constant mean return model in that it removes variations related to market return, thereby, reducing the variance observed in the abnormal returns and elevating its ability to detect event effects. The preference to this model will largely depend on the reported coefficient of determination in the regression model.
Step 7
Abnormal Returns
The abnormal returns are very criti...