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Large-scale insider trading analysis: patterns and discoveries

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Abstract

How do company insiders trade? Do their trading behaviors differ based on their roles (e.g., chief executive officer vs. chief financial officer)? Do those behaviors change over time (e.g., impacted by the 2008 market crash)? Can we identify insiders who have similar trading behaviors? And what does that tell us? This work presents the first academic, large-scale exploratory study of insider filings and related data, based on the complete Form 4 fillings from the U.S. Securities and Exchange Commission. We analyze 12 million transactions by 370 thousand insiders spanning 1986–2012, the largest reported in academia. We explore the temporal and network-based aspects of the trading behaviors of insiders, and make surprising and counterintuitive discoveries. We study how the trading behaviors of insiders differ based on their roles in their companies, the types of their transactions, their companies’ sectors, and their relationships with other insiders. Our work raises exciting research questions and opens up many opportunities for future studies. Most importantly, we believe our work could form the basis of novel tools for financial regulators and policymakers to detect illegal insider trading, help them understand the dynamics of the trades, and enable them to adapt their detection strategies toward these dynamics.

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Notes

  1. Enacted May 23, 2003.

  2. A point identified, with hindsight, as the start of the financial crisis.

  3. The relevant portion of Section 16(b) reads:

    For the purpose of preventing the unfair use of information which may have been obtained by [an insider] by reason of his relationship to the issuer, any profit realized by [an insider] from any purchase and sale, or any sale and purchase, of any equity security of such issuer...within any period of <6 months...shall inure to and be recoverable by the issuer, irrespective of any intention on the part of [the insider] in entering into such transaction of holding the security...purchased or of not repurchasing the security...sold for a period exceeding 6 months. Suit to recover such profit may be instituted...by the issuer, or by the owner of any security of the issuer in the name and in behalf of the issuer if the issuer shall fail or refuse to bring such suit within sixty days after request or shall fail diligently to prosecute the same thereafter[.].

  4. Under Smolowe v. Delendo Corp., 136 F.2d 231 (1943), when calculating the amount of short-swing profit realized by an insider, transactions should be match to reach the maximum possible profit. Chin (1997) claims that a transportation algorithm should be used to compute the maximum possible profit when multiple transactions occur within rolling 6-month windows. Due to the sheer number of transactions, we only consider the consecutive transactions for simplicity.

  5. We take into account the varying number of days in different months to get an accurate value for the number of months between the two transactions in a pair.

  6. The Pearson's product-moment correlation coefficient value of 0.12 indicates positive correlation between profit and number of shares traded (\(p < 0.01\)).

  7. The dollar volume of a stock is a measure of its liquidity on a given day, and it is computed by multiplying the volume of the stock (i.e., total number of shares traded) on a day with the market closing price of the stock on the same day.

  8. The scenarios leading to a ratio >1 are very unrealistic, e.g., on a given day all the trades for a company’s stock should be performed by a single insider; the dataset confirms our belief.

  9. The multiple testing problem arises when testing multiple hypotheses simultaneously. In this setting, the likelihood of observing an erroneous significant result purely by chance increases with the number of tests performed (Witte and Witte 2009).

  10. http://www.crsp.uchicago.edu/.

References

  • Aboody D, Kasznik R (2000) CEO stock option awards and the timing of voluntary corporate disclosures. J. Account. Econ. 29:73–100

    Article  Google Scholar 

  • Adamic, L, Brunetti C, Harris JH, Kirilenko AA (2010) Trading networks. Social Science Research Network Working Paper Series. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1361184

  • Agrawal A., Cooper T (2008) Insider trading before accounting scandals. Social Science Research Network Working Paper Series. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=929413

  • Agrawal A, Nasser T (2011) Insider trading in takeover targets. Social Science Research Network Working Paper Series. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1517373

  • Akoglu L, McGlohon M, Faloutsos C (2010) Oddball: spotting anomalies in weighted graphs. In: Proceedings of the Pacific-Asia conference on advances in knowledge discovery and data mining

  • Bay S, Kumaraswamy K, Anderle MG, Kumar R, Steier DM (2006) Large scale detection of irregularities in accounting data. In: Proceedings of the IEEE international conference on data mining

  • Bernhardt D, Hollifield B, Hughson E (1995) Investment and insider trading. Rev Financ Stud 8(2):501–543

    Article  Google Scholar 

  • Bizjak J, Lemmon M, Whitby R (2009) Option backdating and board interlocks. Rev Financ Stud 22(11):4821–4847

    Article  Google Scholar 

  • Brockman P, Li X, Price SM (2013) Do managers put their money where their mouths are? Evidence from insider trading after conference calls. Social Science Research Network Working Paper Series. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2200639

  • Brockman P, Martin X, Puckett A (2010) Voluntary disclosures around CEO stock option exercises. J Corp Financ 16:120–136

    Article  Google Scholar 

  • Chakrabarti D (2004) Autopart: parameter-free graph partitioning and outlier detection. In: Proceedings of the European conference on principles of, data mining and knowledge discovery, pp 112–124

  • Cheng Q, Lo K (2006) Insider trading and voluntary disclosure. J Account Res 44:815–848

    Article  Google Scholar 

  • Chin A (1997) Accurate calculation of short-swing profits under section 16(b) of the Securities Exchange Act of 1934. Del J Corp Law 22(32):587–599

    Google Scholar 

  • Cohen L, Malloy C, Pomorski L (2012) Decoding inside information. J Financ 67(3):1009–1044

    Article  Google Scholar 

  • Donoho S (2004) Early detection of insider trading in option markets. In: Proceedings of the ACM SIGKDD conference on knowledge discovery and data mining

  • Eberle W, Holder L (2007): Discovering structural anomalies in graph-based data. In: Proceedings of the IEEE international conference on data mining workshops, pp 393–398

  • Edelson R, Whisenant S (2009) A study of companies with abnormally favorable patterns of executive stock option grant timing. Social Science Research Network Working Paper Series. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1326517

  • Fan W, Yu PS, Wang H (2004) Mining extremely skewed trading anomalies. In: Proceedings of the extending database technology conference

  • Goldberg HG, Kirkland JD, Lee D, Shyr P, Thakker D (2003) The NASD securities observation, new analysis and regulation system (SONAR). In: Proceedings of the conference on innovative applications of artificial intelligence

  • Hamilton J (2011) The SEC’s new approach to fraud. Accessed March 22, 2013. http://www.businessweek.com/magazine/the-secs-new-approach-to-fraud-12082011.html

  • Khandani AE, Lo AW (2011) What happened to the quants in August 2007? Evidence from factors and transactions data. J Financ Mark 14(1):1–46

    Article  Google Scholar 

  • Kirkos E, Spathis C, Manolopoulos Y (2007) Data mining techniques for the detection of fraudulent financial statements. Expert Syst Appl 32(4):995–1003

    Article  Google Scholar 

  • Lakonishok J, Lee I (2001) Are insider trades informative? Rev Financ Stud 14(1):79–111

    Article  Google Scholar 

  • Lorie JH, Niederhoffer V (1968) Predictive and statistical properties of insider trading. J Law Econ 11(1):35–53

    Article  Google Scholar 

  • McGlohon M, Bay S, Anderle MG, Steier DM, Faloutsos C (2009) SNARE: a link analytic system for graph labeling and risk detection. In: Proceedings of the ACM SIGKDD international conference on knowledge discovery and data mining

  • Moonesinghe H, Tan PN (2008) Outrank: a graph-based outlier detection framework using random walk. Int J Artif Intell Tools 17(01):19–36

    Article  Google Scholar 

  • Odean T (1998) Are investors reluctant to realize their losses? J Financ 53(5):1775–1798

    Article  Google Scholar 

  • Pandit S, Chau DH, Wang S, Faloutsos C (2007) NetProbe: a fast and scalable system for fraud detection in online auction networks. In: Proceedings of the international conference on world wide web

  • Ruiz EJ, Hristidis V, Castillo C, Gionis A, Jaimes A (2012) Correlating financial time series with micro-blogging activity. In: Proceedings of the ACM international conference on web search and data mining, pp 513–522

  • Summers SL, Sweeney JT (1998) Fraudulently misstated financial statements and insider trading: an empirical analysis. Account Rev 73(1):131–146

  • Sun J, Qu H, Chakrabarti D, Faloutsos C (2005) Neighborhood formation and anomaly detection in bipartite graphs. In: Proceedings of the IEEE international conference on data mining

  • Tamersoy A, Xie B, Lenkey SL, Routledge BR, Chau DH, Navathe SB (2013) Inside insider trading: Patterns & discoveries from a large scale exploratory analysis. In: Proceedings of the IEEE/ACM international conference on advances in social networks analysis and mining

  • The U.S. Security and Exchange Comission Electronic Data Gathering, Analysis, and Retrieval (EDGAR) System: http://www.sec.gov/edgar.shtml

  • Witte RS, Witte JS (2009) Statistics, 9 edn. Wiley

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Authors and Affiliations

  1. College of Computing, Georgia Institute of Technology, Atlanta, GA, USA

    Acar Tamersoy, Elias Khalil, Bo Xie, Duen Horng Chau & Shamkant B. Navathe

  2. Division of Economic and Risk Analysis, U.S. Securities and Exchange Commission, Washington, DC, USA

    Stephen L. Lenkey

  3. Tepper School of Business, Carnegie Mellon University, Pittsburgh, PA, USA

    Bryan R. Routledge

Authors
  1. Acar Tamersoy
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  2. Elias Khalil
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  3. Bo Xie
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  4. Stephen L. Lenkey
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  5. Bryan R. Routledge
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  6. Duen Horng Chau
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  7. Shamkant B. Navathe
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Correspondence to Acar Tamersoy.

Additional information

This is an extended version of the IEEE/ACM ASONAM 2013 paper “Inside Insider Trading: Patterns & Discoveries from a Large Scale Exploratory Analysis” Tamersoy et al. (2013). The Securities and Exchange Commission, as a matter of policy, disclaims responsibility for any private publication or statement by any of its employees. The views expressed herein are those of the author and do not necessarily reflect the views of the Commission or of the author’s colleagues on the staff of the Commission.

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Tamersoy, A., Khalil, E., Xie, B. et al. Large-scale insider trading analysis: patterns and discoveries. Soc. Netw. Anal. Min. 4, 201 (2014). https://doi.org/10.1007/s13278-014-0201-9

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