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The features of daily stock returns have been examined by Brown and Warner (16). The authors have also explored the impact of event study methodologies. In other words, data obtained from stock returns contain specific features that can assist stock analysts to measure the daily returns in stocks. Nonetheless, event studies should not be taken for granted especially when it comes to the analysis of daily data generated from the stock market. In the case whereby the daily data characteristics are not considered in the stock prices, it is usually necessary to employ procedures that are standard. In other words, the adopted procedures should be accepted across the board without a lot of difficulty. This implies that the daily data characteristics are not mandatory in the process of assessing the stock market prices.
The daily excess returns may experience autocorrelation with transitions in their variance. If the latter relationship takes place as a result of an event that has been conditioned, it can be of great value to the stock market. Moreover, it is necessary to specify cross-sectional dependence tests bearing in mind that such tests are more powerful than those are related to potential dependence (Brown and Warner 15). From these discussions, it is evident that there are myriads of factors that should be accounted for when analyzing the movement of stock prices in the market. It is often anomalous to rely on a limited number of parameters and measurements when undertaking such analyses. In addition, it is important to note that there are tests and procedures that can be ignored because they are not mandatory in the outcome of the entire process.
Reference
Brown, Stephen and Jerold, Warner. “Using Daily Stock Returns: The Case of Event Studies”. Journal of Financial Economics 14 (1985): 3-31. Print.
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