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|Title:||An Empirical Analysis of the Seasonal Patterns in Aggregate Directors’ Trades|
|Keywords:||Director trading;Informativeness;Patterns;Industry classification|
|Publisher:||Canadian Center of Science and Education|
|Citation:||Nassar, B., Arzoky, M., Tarhini, A. and Tarhini, T., 2015. An Empirical Analysis of the Seasonal Patterns in Aggregate Directors’ Trades. International Journal of Economics and Finance, 7(9).|
|Abstract:||This paper examins the seasonal patterns in aggregate insider trading transactions, specifically, do insiders prefer to trade on any particular day of the week or month of the year? It also, given that such seasonal patterns exist, attempts to relate these patterns to explanations drawn from the literature on calendar anomalies in returns (and volumes). The results outlined from this paper includes: There is a day of the week anomaly in aggregate insider activities (as measured by number and value of insider transactions). Particularly, relative to other days, insiders tend to trade more on Fridays and less on Tuesdays. Also, the distribution of the average value of directors‟ trades (buys and sells) across the week days forms a U shape i.e. high trading value on the beginning of the week (Monday) and the end of the week (Friday). Also, there is a month of the year anomaly in aggregate insider activities (as measured by the number of insider transactions). Insiders tend to trade most frequently in March and least in August. The results of OLS Regression Model indicate that there is no monthly anomaly in aggregate insider selling activities as measured by the aggregate value of insider transactions. The results of TOBIT Regression Model show that the average value of directors‟ selling activities in March is higher and significantly different relative to other months of the year. The results of OLS regression are also confirmed by the results of K-W statistic test which supported the non existence of monthly anomaly in aggregate director trading (measured by the value of director transactions).|
|Appears in Collections:||Dept of Computer Science Research Papers|
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