research-article

Inside insider trading: patterns & discoveries from a large scale exploratory analysis

Authors:

Stephen L. Lenkey,

Bryan R. Routledge,

Duen Horng Chau,

Shamkant B. NavatheAuthors Info & Claims

ASONAM '13: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

Pages 797 - 804

https://doi.org/10.1145/2492517.2500288

Published: 25 August 2013 Publication History

Abstract

How do company insiders trade? Do their trading behaviors differ based on their roles (e.g., CEO vs. CFO)? 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 (SEC). We analyzed 12 million transactions by 370 thousand insiders spanning 1986 to 2012, the largest reported in academia. We explore the temporal and network-centric aspects of the trading behaviors of insiders, and make surprising and counter-intuitive discoveries. We study how the trading behaviors of insiders differ based on their roles in their companies, the transaction types, the company 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 towards these dynamics.

References

[1]

D. Bernhardt, B. Hollifield, and E. Hughson, "Investment and insider trading," The Review of Financial Studies, vol. 8, no. 2, pp. pp. 501--543, 1995. {Online}. Available: http://www.jstor.org/stable/2962280

[2]

J. Hamilton, The SEC's New Approach to Fraud, 2011 (accessed March 22, 2013), http://www.businessweek.com/magazine/the-secs-new-approach-to-fraud-12082011.html.

[3]

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

[4]

L. Cohen, C. Malloy, and L. Pomorski, "Decoding inside information," The Journal of Finance, vol. 67, no. 3, pp. 1009--1044, May 2012.

[5]

Q. Cheng and K. Lo, "Insider trading and voluntary disclosure," Journal of Accounting Research, vol. 44, pp. 815--848, 2006.

[6]

P. Brockman, X. Martin, and A. Puckett, "Voluntary disclosures around ceo stock option exercises," Journal of Corporate Finance, vol. 16, pp. 120--136, 2010.

[7]

D. Aboody and R. Kasznik, "Ceo stock option awards and the timing of voluntary corporate disclosures," Journal of Accounting and Economics, vol. 29, pp. 73--100, 2000.

[8]

P. Brockman, X. Li, and S. M. Price, "Do managers put their money where their mouths are? evidence from insider trading after conference calls," Social Science Research Network Working Paper Series, 2013.

[9]

H. G. Goldberg, J. D. Kirkland, D. Lee, P. Shyr, and D. Thakker, "The nasd securities observation, new analysis and regulation system (sonar)," in Proceedings of the Conference on Innovative Applications of Artificial Intelligence, 2003.

[10]

S. Donoho, "Early detection of insider trading in option markets," in Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2004.

Digital Library

[11]

M. McGlohon, S. Bay, M. G. Anderle, D. M. Steier, and C. Faloutsos, "Snare: a link analytic system for graph labeling and risk detection," in Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2009, pp. 1265--1274.

Digital Library

[12]

S. Pandit, D. H. Chau, S. Wang, and C. Faloutsos, "Netprobe: a fast and scalable system for fraud detection in online auction networks," in Proceedings of the 16th international conference on World Wide Web. ACM, 2007, pp. 201--210.

Digital Library

[13]

S. Bay, K. Kumaraswamy, M. G. Anderle, R. Kumar, and D. M. Steier, "Large scale detection of irregularities in accounting data," in Data Mining, 2006. ICDM'06. Sixth International Conference on. IEEE, 2006, pp. 75--86.

Digital Library

[14]

W. Fan, P. S. Yu, and H. Wang, "Mining extremely skewed trading anomalies," in Proceedings of the Extending Database Technology Conference, 2004, pp. 801--810.

[15]

J. Bizjak, M. Lemmon, and R. Whitby, "Option backdating and board interlocks," The Review of Financial Studies, vol. 22, no. 11, pp. pp. 4821--4847, 2009. {Online}. Available: http://www.jstor.org/stable/40468375

[16]

L. Adamic, C. Brunetti, J. H. Harris, and A. A. Kirilenko, "Trading networks," Social Science Research Network Working Paper Series, 2010.

[17]

A. E. Khandani and A. W. Lo, "What happened to the quants in august 2007? evidence from factors and transactions data," Journal of Financial Markets, vol. 14, no. 1, pp. 1--46, 2011.

[18]

T. Odean, "Are investors reluctant to realize their losses?" The Journal of Finance, vol. 53, no. 5, pp. pp. 1775--1798, 1998. {Online}. Available: http://www.jstor.org/stable/117424

[19]

D. J. Watts and S. H. Strogatz, "Collective dynamics of 'small-world' networks," Nature, vol. 393, no. 6684, pp. 440--442, 1998.

[20]

R. Edelson and S. Whisenant, "A study of companies with abnormally favorable patterns of executive stock option grant timing," Social Science Research Network Working Paper Series, 2009.

Cited By

Esen MTuncalı Yaman T(2021)Approaches to Detect Securities Fraud in Capital MarketsHandbook of Research on Theory and Practice of Financial Crimes10.4018/978-1-7998-5567-5.ch017(313-331)Online publication date: 2021
https://doi.org/10.4018/978-1-7998-5567-5.ch017
Seth TChaudhary V(2020)A Predictive Analytics Framework for Insider Trading Events2020 IEEE International Conference on Big Data (Big Data)10.1109/BigData50022.2020.9377791(218-225)Online publication date: 10-Dec-2020
https://doi.org/10.1109/BigData50022.2020.9377791
Li BMa ZWang T(2020)Find Trade Patterns in China’s Stock Markets Using Data Mining AgentsBehavioral Predictive Modeling in Economics10.1007/978-3-030-49728-6_11(171-179)Online publication date: 6-Aug-2020
https://doi.org/10.1007/978-3-030-49728-6_11
Show More Cited By

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Inside insider trading: patterns & discoveries from a large scale exploratory analysis

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cover image ACM Conferences

ASONAM '13: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

August 2013

1558 pages

ISBN:9781450322409

DOI:10.1145/2492517

General Chairs:
Jon Rokne
University of Calgary, Calgary, AB, Canada
,
Christos Faloutsos
Carnegie Mellon University, Pittsburgh, PA

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 25 August 2013

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ASONAM '13

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SIGKDD

ASONAM '13: Advances in Social Networks Analysis and Mining 2013

August 25 - 28, 2013

Ontario, Niagara, Canada

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Overall Acceptance Rate 116 of 549 submissions, 21%

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Cited By

Esen MTuncalı Yaman T(2021)Approaches to Detect Securities Fraud in Capital MarketsHandbook of Research on Theory and Practice of Financial Crimes10.4018/978-1-7998-5567-5.ch017(313-331)Online publication date: 2021
https://doi.org/10.4018/978-1-7998-5567-5.ch017
Seth TChaudhary V(2020)A Predictive Analytics Framework for Insider Trading Events2020 IEEE International Conference on Big Data (Big Data)10.1109/BigData50022.2020.9377791(218-225)Online publication date: 10-Dec-2020
https://doi.org/10.1109/BigData50022.2020.9377791
Li BMa ZWang T(2020)Find Trade Patterns in China’s Stock Markets Using Data Mining AgentsBehavioral Predictive Modeling in Economics10.1007/978-3-030-49728-6_11(171-179)Online publication date: 6-Aug-2020
https://doi.org/10.1007/978-3-030-49728-6_11
Pienta RAbello JKahng MChau D(2015)Scalable graph exploration and visualization: Sensemaking challenges and opportunities2015 International Conference on Big Data and Smart Computing (BIGCOMP)10.1109/35021BIGCOMP.2015.7072812(271-278)Online publication date: Feb-2015
https://doi.org/10.1109/35021BIGCOMP.2015.7072812
Tamersoy AKhalil EXie BLenkey SRoutledge BChau DNavathe S(2014)Large-scale insider trading analysis: patterns and discoveriesSocial Network Analysis and Mining10.1007/s13278-014-0201-94:1Online publication date: 8-Jun-2014
https://doi.org/10.1007/s13278-014-0201-9

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