How to detect illegal corporate insider trading? A data mining approach for detecting suspicious insider transactions

摘要

Only in the U.S. Stock Exchanges, the daily average trading volume is about 7 billionshares. This vast amount of trading shows the necessity of understanding thehidden insights in the data sets. In this study, a data mining technique, clusteringbased outlier analysis is applied to detect suspicious insider transactions.1,244,815 transactions of 61,780 insiders are analysed, which are acquired fromThomson Financial, covering a period of January 2010–April 2017. In order todetect outliers, similar transactions are grouped into the same clusters by using atwo‐step clustering based outlier detection technique, which is an integration ofk‐means and hierarchical clustering. Then, it is shown that outlying transactionsearn higher abnormal returns than non‐outlying transactions by using event studymethodology.