Information Networks: Evidence from Illegal Insider Trading Tips
Kenneth R. Ahern, October 4th, 2014
University of Southern California – Marshall School of Business
This paper exploits a novel hand collected dataset to provide a comprehensive analysis of the demographics and social relationships behind illegal insider trading networks. I find that the majority of inside traders are connected through family and friendship links and a minority are connected through professional relationships. Traders cluster by age, occupation, gender, and location. Traders earn prodigious returns of about 35% over 21 days, where traders farther from the original source earn lower percentage returns, but higher dollar gains. More broadly, this paper provides some of the first evidence on information networks using direct observations of person-to-person communication.
Information Networks: Evidence from Illegal Insider Trading Tips – Introduction
In March 2007, a credit analyst at UBS learned through his job that the private equity firm, Hellman & Friedman, would acquire the software company, Kronos. On March 14, the UBS analyst tipped this information to his friend, Deep Shah, an analyst at Moody’s. On the same day, Shah tipped the information to his roommate’s cousin, Roomy Khan. The following day, Roomy Khan tipped a family friend, Shammara Hussain, two former business associates, Jeffrey Yokuty and his boss, Robert Feinblatt, and another friend, Thomas Hardin. On March 19th, Hardin tipped his friend, Gautham Shankar, who tipped Zvi Goffer, David Plate, and unidentified traders at the investment firm Schottenfeld Group. Plate subsequently tipped others at Schottenfeld and Goffer tipped his long-time friend, Joseph Mancuso. After the acquisition was officially announced on March 23, the group of inside traders had realized ill-gotten gains of $2.9 million.
Figure 1 shows that these trades are a small part of a larger network of 50 inside traders centered around Raj Rajaratnam, the former hedge fund manager of the Galleon Group. In turn, this network is just one of many networks of inside traders. According to the U.S. Attorney for the Southern District of New York, insider trading is “rampant” on Wall Street (Frontline, 2014).
However, the existing academic research on illegal insider trading is limited (Meulbroek, 1992). We still know relatively little about basic facts: Who are inside traders? How do they know each other? What type of information do they share, and how much money do they make? Beyond illegal trading, understanding these networks may provide insights into how social relations influence the activity of market participants more broadly.
In this paper, I provide a comprehensive analysis of 183 insider trading networks. I identify networks using hand-collected data from all of the insider trading cases filed by the Securities and Exchange Commission (SEC) and the Department of Justice (DOJ) between 2009 and 2013. The data from the original case documents are highly detailed. They include biographical information on the insiders and descriptions of their social relationships, such as family, friends, and business associates. The data also include the specific information that is shared, the date the information is shared, the amount and timing of insider trades, and the types of securities traded. I combine these case filings with novel data from the LexisNexis Public Records Database (LNPRD) to identify insiders’ broader social networks of family, neighbors, and associates, to serve as counterfactual observations. The data cover 1,139 insider tips shared by 622 insiders who made an aggregated $928 million in illegal profits. In sum, the data assembled for this paper provide an unprecedented view of how investors share material, nonpublic information through word-of-mouth communication.
This paper’s main objective is to present a series of facts about illegal insider trading. First, I present a detailed profile of individual traders, the events on which they trade, the firms that are the subject of the information, and the traders’ investment returns. Second, I present a range of findings about how insiders are connected to each other through social relationships. Third, I analyze the flow of information from the original source to the final tippee. Finally, I present the characteristics of the average insider network as a whole.
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