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abnormal return
For example, when the abnormal return is 1.5 percent and there is a sample size of 30 the power is 0.54.
      
For the same sample, Jarrell and Poulsen find an average abnormal return of 1.14 percent for acquirers.
      
First, there is a question as to how the abnormal return is identified from the data.
      
Finally, the average abnormal return experienced by firms comprising each individual sample considered in the analysis was calculated.
      
Figure 3 gives the evolution of the Cumulative Average Abnormal Return through time.
      
For these subsamples, we find the abnormal return surrounding option introduction to be just as negative as for the sample as a whole.
      
For example, an increase in home bias of 100 kilometers only results in a 0.0229 percent higher cumulated abnormal return.
      
Generally if the abnormal return is large one will have little difficulty rejecting the null hypothesis of no abnormal return.
      
However, for a given sample size, increases in power are substantial when the abnormal return is larger.
      
Importantly, our results are robust to different ways of estimating abnormal return.
      
It is argued that the negative pre-acquisition abnormal return is an indication of an inefficient management in the target firm.
      
In some cases theory provides hypotheses concerning the relation between the magnitude of the event abnormal return and firm characteristics.
      
In the 1980s they find the average abnormal return is negative at -1.10 percent.
      
In contrast, when firms decide to use straight debt financing, the average abnormal return is closer to zero.
      
In this second case, any abnormal return around the announcement should reflect outsiders' prior information.
      
Last, the abnormal return following a split is negatively related to the level of institutional ownership prior to the split.
      
Most of them were caused as functions failed to clean up local resources on abnormal return paths.
      
MPI-1.2 allows users to attach an errorhandling function to each communicator, which would be invoked in case of an abnormal return.
      
One common modification is to standardize each abnormal return using an estimator of its standard deviation.
      
Often such an exercise can be helpful when multiple hypotheses exist for the source of the abnormal return.
      
 

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