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Research handbook on insider trading
In most capital markets, insider trading is the most common violation of securities law. It is also the most well known, inspiring countless movie plots and attracting scholars with a broad range of backgrounds and interests, from pure legal doctrine to empirical analysis to complex economic theory. This volume brings together original cutting-edge research in these and other areas written by leading experts in insider trading law and economics.
Book
Decoding Inside Information
Exploiting the fact that insiders trade for a variety of reasons, we show that there is predictable, identifiable \"routine\" insider trading that is not informative about firms' futures. A portfolio strategy that focuses solely on the remaining \"opportunistic\" traders yields value-weighted abnormal returns of 82 basis points per month, while abnormal returns associated with routine traders are essentially zero. The most informed opportunistic traders are local, nonexecutive insiders from geographically concentrated, poorly governed firms. Opportunistic traders are significantly more likely to have SEC enforcement action taken against them, and reduce trading following waves of SEC insider trading enforcement.
Journal Article
Insider trading laws to counter financial crime: a comparative study of Mauritius, UK and US laws
Purpose
The rise in business activities coupled with free trade liberalisation across countries has entailed an increase in securities transaction as well as insider trading (IT). In fact, IT is characterised by the influence and usage of some prior knowledge concerning sensitive information of a corporate body which results in a financial benefit to the insider trader. The practice of IT is not only unethical but also illegal and this statement is witnessed by the mushrooming of laws across the globe categorising IT as an offence. However, the type of punishment varies in different countries depending on various factors. Consequently, the purpose of this paper is to assess the adequacy and efficiency of IT laws in the context of a developing country being Mauritius.
Design/methodology/approach
To achieve the research objective, the Mauritian laws on IT were compared with the corresponding laws of some developed countries like the USA and the UK. As such, a qualitative research method was adopted. In particular, the black letter approach was used to examine the relevant laws of Mauritius, UK and USA on IT. A comparative analysis was conducted concerning IT laws for each country with the view of suggesting recommendations for Mauritian stakeholders to adopt to enhance the existing legal and regulatory framework on IT.
Findings
It was found that Mauritian IT laws are largely inspired from both the US and UK corresponding legislation. However, Mauritian laws need to be strengthened by imposing some more severe penalties in terms of fines and terms of imprisonment like the USA has established. The Mauritian Financial Services Commission as the regulator also needs to play a more active role in disseminating particularities of IT laws, offences and penalties to the civil society at large.
Originality/value
At present, this study will be among the first academic writings on the efficiency of IT laws in Mauritius and also, because existing literature is quite scarce on assessing the adequacy of IT legislation in developing countries, this research aims at filling in the gap in literature. The study is carried out with the aim of combining a large amount of empirical, theoretical and factual information that can be of use to various stakeholders and not only to academics.
Journal Article
Informed Options Trading Prior to Takeover Announcements: Insider Trading?
We quantify the pervasiveness of informed trading activity in target companies’ equity options before the announcements of 1,859 U.S. takeovers between 1996 and 2012. About 25% of all takeovers have positive abnormal volumes, which are greater for short-dated, out-of-the-money calls, consistent with bullish directional trading before the announcement. Over half of this abnormal activity is unlikely due to speculation, news and rumors, trading by corporate insiders, leakage in the stock market, deal predictability, or beneficial ownership filings by activist investors. We also examine the characteristics of option trades litigated by the Securities and Exchange Commission (SEC) for alleged illegal insider trading. Although the characteristics of such trades closely resemble the patterns of abnormal option volume in the U.S. takeover sample, we find that the SEC litigates only about 8% of all deals in it.
This paper was accepted by Lauren Cohen, finance.
Journal Article
An Insider Data Leakage Detection Using One-Hot Encoding, Synthetic Minority Oversampling and Machine Learning Techniques
Insider threats are malicious acts that can be carried out by an authorized employee within an organization. Insider threats represent a major cybersecurity challenge for private and public organizations, as an insider attack can cause extensive damage to organization assets much more than external attacks. Most existing approaches in the field of insider threat focused on detecting general insider attack scenarios. However, insider attacks can be carried out in different ways, and the most dangerous one is a data leakage attack that can be executed by a malicious insider before his/her leaving an organization. This paper proposes a machine learning-based model for detecting such serious insider threat incidents. The proposed model addresses the possible bias of detection results that can occur due to an inappropriate encoding process by employing the feature scaling and one-hot encoding techniques. Furthermore, the imbalance issue of the utilized dataset is also addressed utilizing the synthetic minority oversampling technique (SMOTE). Well known machine learning algorithms are employed to detect the most accurate classifier that can detect data leakage events executed by malicious insiders during the sensitive period before they leave an organization. We provide a proof of concept for our model by applying it on CMU-CERT Insider Threat Dataset and comparing its performance with the ground truth. The experimental results show that our model detects insider data leakage events with an AUC-ROC value of 0.99, outperforming the existing approaches that are validated on the same dataset. The proposed model provides effective methods to address possible bias and class imbalance issues for the aim of devising an effective insider data leakage detection system.
Journal Article
Do Prices Reveal the Presence of Informed Trading?
Using a comprehensive sample of trades from Schedule 13D filings by activist investors, we study how measures of adverse selection respond to informed trading. We find that on days when activists accumulate shares, measures of adverse selection and of stock illiquidity are lower, even though prices are positively impacted. Two channels help explain this phenomenon: (1) activists select times of higher liquidity when they trade, and (2) activists use limit orders. We conclude that, when informed traders can select when and how to trade, standard measures of adverse selection may fail to capture the presence of informed trading.
Journal Article
Application of Machine Learning and Deep Learning Techniques for Enhanced Insider Threat Detection in Cybersecurity: Bibliometric Review
Insider threats remain a persistent challenge in cybersecurity, as malicious or negligent insiders exploit legitimate access to compromise systems and data. This study presents a bibliometric review of 325 peer-reviewed publications from 2015 to 2025 to examine how machine learning (ML) and deep learning (DL) techniques for insider threat detection have evolved. The analysis investigates temporal publication trends, influential authors, international collaboration networks, thematic shifts, and algorithmic preferences. Results show a steady rise in research output and a transition from traditional ML models, such as decision trees and random forests, toward advanced DL methods, including long short-term memory (LSTM) networks, autoencoders, and hybrid ML–DL frameworks. Co-authorship mapping highlights China, India, and the United States as leading contributors, while keyword analysis underscores the increasing focus on behavior-based and eXplainable AI models. Symmetry emerges as a central theme, reflected in balancing detection accuracy with computational efficiency, and minimizing false positives while avoiding false negatives. The study recommends adaptive hybrid architectures, particularly Bidirectional LSTM–Variational Auto-Encoder (BiLSTM-VAE) models with eXplainable AI, as promising solutions that restore symmetry between detection accuracy and transparency, strengthening both technical performance and organizational trust.
Journal Article
The Need to Legalize and Regulate Insider Trading - An Analysis
Insider trading is perceived as a problem across capital mar-kets. The Securities Exchange Board of India (SEBI) created the SEBI (Prohibition of Insider Trading) Regulations, 2015, which criminalizes insider trading. However, insider trading laws have faced several problems at the implementation and enforcement stage. This article considers these problems from the viewpoint of the economic rationale that insider trading should be permitted in capital markets and thus le-galized. These economic arguments have largely been ig-nored by regulators who have continued to come down hard upon insider trading, despite limited success. Moreover, due to concerns related to privacy, insider trading investigations may face greater hurdles in the future. This article takes these factors and economic arguments into consideration and balances them against the regulators' concerns to suggest that insider trading be not only prima facie legalized, but also regulated when there is a breach of fiduciary duties or when there is a dissemination of positive information.
Journal Article