Insider Trading
Does Complex Regulation Create Insider Trading Opportunities?

Over the past two decades, policymakers have increasingly passed broad-reaching and complex regulation. Using the 2017 Tax Cuts and Jobs Act (TCJA) as a setting of such regulation, we find that this complex set of changes to U.S. tax law is associated with increased market uncertainty, information asymmetry, and insider trade profitability. These findings are consistent with insiders’ differential abilities to process information about the new regulation. Insider profits are concentrated in firms where analysts struggle to predict the effect of the TCJA and among insiders with greater ex-ante ability to understand tax law. External constraints are associated with lower insider trade profitability, and we also find evidence that insider profits are associated with specific provisions of the TCJA. Overall, our evidence suggests that complex regulation can create uncertainty that firm insiders exploit for their own gain and that the legislative process can inadvertently reward firm insiders.

Campbell, John L. and Davidson, Owen and Goldman, Nathan C. and Holt, Matthew, Does Complex Regulation Create Insider Trading Opportunities? (December 17, 2024). Available at SSRN: https://ssrn.com/abstract=5061737 or http://dx.doi.org/10.2139/ssrn.5061737

Trading Behavior of Multi-Firm Insiders

This study considers the trading behavior of insiders affiliated with multiple firms and information contained in these transactions. We leverage a unique dataset that captures the tenure and firm affiliations of corporate insiders and examine the impact of insiders transitioning from single to multi-firm affiliations on the informativeness and outcomes of their trades. Our findings reveal that while insider transactions generally predict future returns, trades executed by multi-firm insiders, especially those involving sales in incumbent firms and purchases in new affiliations, tend to diverge from being purely information-driven. These transactions are more closely aligned with liquidity needs and portfolio rebalancing. This study extends the existing literature on the informational nature of insider trading by highlighting the unique motivations behind multi-firm insider transaction. We also demonstrate the importance of considering an insider’s extent of affiliations and the specific timing of their trades to fully understand the informativeness of insider trading activities.

Gao, Chao and Johnston, Mitchell and Wan, Yun Qing, Trading Behavior of Multi-Firm Insiders. Available at SSRN: https://ssrn.com/abstract=5052469 or http://dx.doi.org/10.2139/ssrn.5052469

The Veracity of Insider Trading Signals in Financially Distressed Firms

We show that insider trading behaviour provides a credible signal of future share return performance within a sample of firms undergoing financial distress. We argue that when firms are in distress the incentive for insiders to employ their trading to send a false positive signal is high, however we find that distressed firms associated with insider buying have significantly better future share returns than firms associated with no insider trading or insider selling. We employ credit rating downgrades as confirmation of the distressed state, and we investigate the reasons why the positive signal associated with insider purchases deviates from the negative downgrade signal of the rating agencies. Our analysis shows that this is not due to a lack of rating agency information; there are very few rating downgrade reversals. Our analysis also leads us to dismiss the possibility that this is due to different implications of risk for shareholders and bondholders. We conclude that insider purchases are partly driven by an over-reaction to bad news in the period of distress, which subsequently reverses; such mispricing would not be expected to be related to informed credit rating actions. We also find that the share return recovery is partial, and this leaves open the possibility that it is inadequate to reach the rating upgrade hurdle. While on average outside investors would benefit from retaining their shares in distressed firms associated with insider buying, we highlight a subset of firms where this is not the case.

Hill, Paula and Korczak, Adriana and Wang, Shuo, The Veracity of Insider Trading Signals in Financially Distressed Firms. Available at SSRN: https://ssrn.com/abstract=4999680 or http://dx.doi.org/10.2139/ssrn.4999680

Insider Filing Violations and Illegal Information Delay

We document that a significant number of insiders violate the Securities and Exchange Commission (SEC) reporting requirements by filing open market transactions after the legally required deadline. Prior to Sarbanes-Oxley (SOX), 29% of transactions fell outside the required reporting window. Following SOX, 8% are delinquent. Violations cluster in periods of high information asymmetry, incentivizing insiders to keep trades private, and earn abnormal returns. Collectively, these findings suggest a subgroup of insiders personally benefit from violating SEC disclosure requirements. Evidence also suggests blockholders provide governance for violations. Guilty insiders experience a reduction in board seats and an increased likelihood of turnover.

Cline, Brandon N. and Houston, Caleb, Insider Filing Violations and Illegal Information Delay (January 31, 2022). Journal of Financial and Quantitative Analysis, volume 58, issue 5, 2023 [10.1017/S0022109022000953], Available at SSRN: https://ssrn.com/abstract=4954568 or http://dx.doi.org/10.1017/S0022109022000953

Broken Windows: SEC Enforcement of Delinquent Insider Filings

The SEC mandates insiders report trading activity by a deadline. Although insiders disregard this requirement 222,613 times from 1988 to 2023, only 0.5% of the violations prompt SEC enforcement action. Comparing enforced to unenforced filing violations we show the SEC pursues insiders who persistently violate the requirement. Evidence also suggests that targeted enforcement has a deterrence on future reporting violations and on other questionable trading practices, such as blackout and stealth trading. We illustrate the deterrence is not limited to the insider experiencing enforcement, and that the strength of the deterrence varies by the insiders’ connection to the enforcement action.

Houston, Caleb and Cline, Brandon N. and Posylnaya, Valeriya, Broken Windows: SEC Enforcement of Delinquent Insider Filings (September 01, 2024). Available at SSRN: https://ssrn.com/abstract=4954555 or http://dx.doi.org/10.2139/ssrn.4954555

Government Investment and Insider Trading Profitability: Evidence from the Bipartisan Infrastructure Law

This study examines whether government investment is associated with corporate insiders’ trading profitability. By exploiting the Bipartisan Infrastructure Law (BIL) introduced in the U.S. in November 2021 as a quasi-natural experiment, we provide novel evidence that insiders experience increased profits through trading when government investments take place. The increased insider trading profitability is amplified in industries that received funds directly from the BIL, for firms with relatively higher R&D, higher product market competition, and at the growth stage of their corporate lifecycle. Overall, we provide new evidence that insiders can exploit government funds for personal benefits through insider trading.

Petmezas, Dimitris and Redor, Etienne and Wang, Shaoyi and Xiong, Nan, Government Investment and Insider Trading Profitability: Evidence from the Bipartisan Infrastructure Law. Available at SSRN: https://ssrn.com/abstract=5011470 or http://dx.doi.org/10.2139/ssrn.5011470

Market Manipulation
A machine learning ensemble approach to predict financial markets manipulation

Forecasting spoofing manipulation in financial markets is the cornerstone of regulators to improve trading surveillance systems. This paper presents a novel data-driven methodology for predicting market conditions associated with episodes of spoofing manipulation. Our approach diminishes the significance of model selection by combining forecasts from various conventional machine learning techniques. We implement the forecasting method using a distinctive dataset of suspicious spoofing orders identified on the Moscow Stock Exchange. Our research demonstrates that examining the limit order book and prior alleged spoofing events produces a reliable manipulation prediction metric for short-term intervals in a high-frequency data context. The Real-Time Spoofing Probability measure proposed is shown to be an effective real-time indicator of risk to trade in manipulative environment, which can be used by exchanges, market participants and regulators in surveillance systems.

Franus, Tatiana and Marchese, Malvina and Payne, Richard G., A machine learning ensemble approach to predict financial markets manipulation (December 08, 2023). Available at SSRN: https://ssrn.com/abstract=5051180 or http://dx.doi.org/10.2139/ssrn.5051180

Option Market Manipulation

We investigate the behavior of options markets in response to potential stock market manipulations, with a specific focus on cross-product manipulation. Unlike existing literature, we rely on a dataset of manipulated stock prices, thereby avoiding the joint hypothesis problem. Key findings reveal a discernible increase in trading volume preceding manipulation events, with a preference for short-term, out-of-the-money options. The study also highlights inefficiencies in the options market during manipulation periods and examines factors influencing traders’ strategies. Results are not explained by volatility, firm-specific information, and do not indicate day-of-week effects.

Cumming, Douglas J. and Ji, Shan and Sala, Carlo, Option Market Manipulation (November 03, 2024). Available at SSRN: https://ssrn.com/abstract=5022346 or http://dx.doi.org/10.2139/ssrn.5022346

Manipulating Algorithmic Markets

This paper develops a new methodology for causal price impact in high-frequency financial markets to study a widespread form of market manipulation and its consequences. I identify directly from data when a trader takes both sides of the same transaction but instead of letting orders cross uses a compliance tool to prevent legal exposure. This functionality is offered by every major exchange and in US futures markets its default use option allows the tool to be exploited strategically. This form of self-trading can effectively signal demand at artificial prices and result in disproportionate liquidity removal from markets. I introduce a source of variation that generates systematic differences in information exposure to traders. This leverages an institutional feature of electronic limit order books where as-good-as random delays between when a trade happens and the market learns about it can be used to assign treatment. By comparing trades occurring almost at the same time facing an identical information set, except for the news about a reference trade, I implement an empirical approach that estimates dynamic responses robust to microstructure noise and confounders. My findings show that self-trading successfully moves prices in the direction that benefits the trader, both by making liquidity providers revise quotes and enticing others to trade. I then use these estimates to quantify the role of self-trading in flash events: brief moments of substantial price increases or declines. Using a causal attribution framework, I separate information shocks-price adjustments based on news-from manipulative price impact to be able to assess the role of each factor individually and in combination. I find that almost 10% of flash events in US futures markets are driven by attracting others to trade in the direction consistent with profitable self-trading.

Tremacoldi-Rossi, Pedro, Manipulating Algorithmic Markets (November 18, 2024). Available at SSRN: https://ssrn.com/abstract=5025393 or http://dx.doi.org/10.2139/ssrn.5025393

High-Frequency Traders: How the Sec Can Tighten Regulation While Maintaining the Benefits of a Competitive Market

In 2010, the so-called “Flash Crash” of the U.S. stock market brought the overlooked practice of high-frequency trading into the spotlight for the first time. Initial efforts to study and curtail the practice, including a transaction fee pilot attempted by the Securities and Exchange Commission in 2018, have been unsuccessful. After outlining the substantial benefits market participants gain from the activities of high frequency traders, this article argues that there are three potent and readily available tools for limiting the harmful excesses of those traders: (i) aggressively bring market manipulation charges under § 9(a)(2) of the Exchange Act against those who attempt to manipulate the market; (ii) to bring enforcement actions under § 78f(b)(5) of the Exchange Act against national exchanges that fail to “protect investors and the public interest” by giving special benefits to those traders; and (iii) utilize § 19 of the Exchange Act to oversee exchange colocation rules designed to benefit those traders which do not reflect fair access and transparency. With these proposals implemented, markets will continue to function at historically low costs for all investors with the aid of healthy competition between high-frequency traders.

Sanders, John I., High-Frequency Traders: How the Sec Can Tighten Regulation While Maintaining the Benefits of a Competitive Market (June 24, 2024). American University Business Law Review, Volume 13, Issue 2, Pp. 315-348; 2024, Available at SSRN: https://ssrn.com/abstract=4982863 or http://dx.doi.org/10.2139/ssrn.4982863

High-Frequency Spoofing, Market Fairness and Regulation

Recent years have seen a number of cases of spoofing subjected to criminal prosecution by market authorities. In an artificial market setting, we study the feedback loop created between spoofing strategies and market dynamics. We analyze the impact on market quality and fairness and test regulatory measures to discourage market manipulation. Our results show that spoofing does not significantly affect market quality measures, but by inducing losses for other traders, it affects fairness. In addition, we show that spoofers are particularly attracted to uncertain environments (macroeconomic announcements) and high capitalization, liquid securities. Finally, we find that introduction of a random market order execution delay is an effective way to make spoofing unprofitable and enhance market integrity.

ORIOL, Nathalie and Ladley, Daniel and Veryzhenko, Iryna, High-Frequency Spoofing, Market Fairness and Regulation. Available at SSRN: https://ssrn.com/abstract=5031420 or http://dx.doi.org/10.2139/ssrn.5031420

The Role of AI and Machine Learning in Fraud Detection: Enhancing Risk Management in Corporate Finance

Artificial intelligence (AI) and machine learning (ML) have become critical tools in fraud detection, transforming the landscape of corporate finance by providing more robust and dynamic risk management solutions. This paper explores how AI/ML technologies are revolutionizing fraud prevention by leveraging real-time data analysis to detect suspicious activities, reducing the financial risk posed by fraud. Key techniques for integrating AI/ML into existing financial systems are discussed, highlighting how these technologies can analyse large datasets to identify unusual patterns indicative of fraud. Case studies of successful implementations are presented, demonstrating the ability of AI to combat various types of fraud, including identity theft, insider trading, and cyber-attacks. Moreover, the paper delves into the challenges that come with adapting AI/ML to different forms of financial fraud, emphasizing the complexities of ensuring accuracy across a broad range of fraud schemes. Additionally, regulatory implications and the future of AI-driven risk management are examined, as businesses and regulators work to balance innovation with compliance and privacy concerns. The paper argues that, while AI/ML hold great potential for enhancing fraud detection, strategic planning and regulatory frameworks are essential for addressing both technological and operational challenges.

Nweze, Michael & Avickson, Eli & Ekechukwu, Gerald. (2024). The Role of AI and Machine Learning in Fraud Detection: Enhancing Risk Management in Corporate Finance. International Journal of Research Publication and Reviews. 5. 10.55248/gengpi.5.1024.2902.