Using ETFs To Conceal Insider Trading 

By | February 24, 2023

Insiders are continually implementing new illegal trading strategies to avoid detection by regulators. Conventional illegal insider trading takes place in stocks. However, these strategies are evolving and also include trading using family member accounts or other securities such as options.  

In 2021, in the first case of its kind, the SEC charged an employee of a biopharmaceutical company (Medivation, MDVN) for illegal insider trading. What was interesting about this case is that the employee purchased options in a related biopharmaceutical company (Incyte, INCY) rather than directly trading the acquisition target (MDVN). The insider is alleged to have made more than $100,000 in profits. The case effectively alleges that the insider predicted that the positive news for MDVN from the acquisition bid would also have a positive effect on INCY’s stock price. Mehta et al. (2020) term the illegal insider trading of economically related firms as shadow trading. 

In a recent paper, we identify and measure a new type of shadow trading in which insiders trade exchange-traded funds (ETF) to benefit from mergers and acquisitions (M&A) announcements. ETFs are an attractive instrument for insiders to conceal their trading – they are cost-effective, very liquid, and allow insiders to trade strategically and hide the price impact of their trades. ETFs also allow insiders to get exposure to a firm releasing price sensitive news where a related firm’s value may also increase from the same news, and to date, law enforcement agencies have not prosecuted any instances of insider trading in ETFs (to the best of our knowledge). Our research therefore has implications for law enforcement agencies to broaden the scope of their surveillance and enforcement activities. 

Summary of approach 

A key challenge in identifying abnormal trading activities that might indicate the presence of insider trading is that in a large enough sample, purely by chance, abnormal observations will occur. A similar challenge occurs in other settings, for example, in studies that attempt to separate fund manager skill and luck. In such settings, incorrect conclusions can be drawn, as fund managers can beat their benchmarks through luck (not skill), even across multiple periods. Similarly, some ETFs will, by chance, happen to have abnormal volume prior to an M&A announcement. 

Our approach addresses this challenge with statistical techniques known as “bootstrapping.” Drawing on the funds management and microstructure literature (e.g., Kosowski et al., 2006; Putniņš and Barbara, 2020), we design a bootstrap approach that quantifies the amount of shadow trading in ETFs beyond statistical chance. In our approach, we compare the trading activities between a treatment sample of ETFs that are the most likely to be used by insiders for shadow trading prior to M&A announcements and a control sample created through random sampling.  

We focus on M&A announcements as they result in a large stock price impact for target firms, are unscheduled, and are frequent, providing the cleanest setting and a meaningful dataset for examining insider trading using ETFs. For example, Patel and Putniņš (2022) estimate that direct illegal insider trading in stocks occurs prior to 20% of M&A announcements. 

We subject the statistical models to a suite of robustness tests to ensure the results are not driven by specific modelling choices or methodological issues. From these tests we draw similar conclusions to our main findings. We also undertake falsification tests where we examine whether our methodology identifies shadow trading during times when it is not expected.  

Prevalence of shadow trading using ETFs  

We find strong evidence of shadow trading in ETFs prior to price-sensitive news. Using a percentile test, where we compare the trading activity of ETFs likely to be traded by insiders (i.e., treatment sample) and the trading activity of ETFs under normal conditions (i.e., control sample), on average, we find abnormal increases in ETF volume in the five-days prior to the release of M&A news in 3-6% of same-industry ETFs that are likely to be traded by insiders. For these ETFs, multivariate OLS regressions confirm that these increases in ETF volume prior to M&A news are economically large, with abnormal volume equal to 31% of the full sample standard deviation of abnormal volume.  

In dollar terms, the magnitude of shadow trading in ETFs totals $2.75 billion during our 13-year sample period, or approximately $200 million per year. As we only examine shadow trading prior to M&As and not other price-sensitive news releases like earnings, analyst upgrades/downgrades, and product releases, our estimates for shadow trading in ETFs is a lower bound estimate. 

We find that shadow trading in ETFs increases from 2009-2013 to 2014-2019, consistent with the increasing popularity of ETFs as an investment vehicle and growth in ETF liquidity over time. For example, between 2009-2013 and 2014-2019, we find that shadow trading occurs in 2-5% and 7-14% of ETFs, and shadow trading activity totals $150 million and $360 million per year, respectively.  

Where is shadow trading most prevalent? 

We find that the likelihood of shadow trading is higher for ETFs with higher levels of liquidity (e.g., Admati and Pfleiderer, 1988; Ben-David et al., 2022). Higher levels of ETF liquidity allow individuals to trade strategically and hide their private information as well as earn larger profits from their information. As ETFs are several times more liquid than the underlying stocks, it is easier for insiders to reduce the price impact of their trades.  

The likelihood of shadow trading also increases when the stock price impact of information is larger, as the potential profits from trading ETFs are larger. This occurs when the target firm is a large constituent of the ETF portfolio or when the target firm experiences large cumulative abnormal returns around the M&A announcement date.  

We find higher levels of shadow trading in stocks with greater levels of information asymmetry. Insiders can generate larger trading profits as they have a greater information advantage over other investors, in particular in smaller target stocks. 

Furthermore, we find that shadow trading in ETFs is most prevalent in the healthcare, technology, and industrials sectors. This is not surprising given that insiders are more likely to engage in shadow trading when they have a greater information advantage over other investors and when the value of their private information results in larger trading profits. For example, these sectors are associated with higher levels of trade secrecy and target firms have larger changes in value from acquisition bids. For these three industries, we find that shadow trading occurs in 2-12% of ETFs, and shadow trading activity totals more than $2 billion during our sample period. 

Overall, the key drivers of shadow trading are consistent with rational crime theories where insiders are more likely to illegally trade when the magnitude of their trading profits outweigh the penalties from being detected and prosecuted by regulators.  


Our findings have implications for law enforcement agencies. If only the traditional form of illegal insider trading in stocks is considered, then the total amount of insider trading that takes place may be vastly underestimated as it fails to account for a large amount of shadow trading.  

We identify new characteristics of insider trading strategies. Through a better understanding of where and when insiders choose to trade, our results may help guide and broaden surveillance efforts to reduce insider trading in stocks and other securities and improve the integrity of financial markets.  

Our findings suggest that current insider trading legislation and case law may need to be reviewed and updated to successfully prosecute insiders for shadow trading in ETFs. 


Elza Eglīte is a student at the Stockholm School of Economics Riga. 

Dans Štaermans is a Senior Economist at Latvijas Banka.  

Vinay Patel is an Associate Professor at the University of Technology Sydney. 

Tālis Putniņš is a Professor of Finance at the University of Technology Sydney.  

The post is adapted from their paper, “Using ETFs to conceal insider trading,” available on SSRN. 

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