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.

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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
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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Published: 25 August 2013

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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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