Bridge over ocean
4 March 2021 Financial Analysts Journal

Active Trading in ETFs: The Role of High-Frequency Algorithmic Trading (Summary)

  1. Simon Constable

This is a summary of “Active Trading in ETFs: The Role of High-Frequency Algorithmic Trading,” by Archana Jain, Chinmay Jain, and Christine X. Jiang, published in the Second Quarter 2021 issue of the Financial Analysts Journal.

Listen to an audio version of this summary.

High-frequency algorithmic trading adds to ETF market stability by reducing the discrepancy between fund prices and net asset values (NAVs).

What’s the Investment Issue?

Authorized participants (APs) are seen as an essential market force in keeping the prices of exchange-traded funds (ETFs) aligned with their net asset values (NAVs). However, sometimes mispricing occurs, and that has caused concern about market integrity and stability.

The concern centers around the intersection of two phenomena. First, the ETF market has grown exponentially over the last decade or so, to around $9 trillion. Second, there has been an increase in high-frequency algorithmic trading (AT), which now accounts for half of US stock trading volume.

The specific worry is that AT may exacerbate the mispricing of ETFs, which could undermine the role of the AP and thereby destabilize the market. The authors study the effect of AT on mispricing.

How Do the Authors Tackle the Issue?

The authors analyze the trading activity of a sample of 578 ETFs over the period January 2012 through June 2018 to determine the extent of AT in the ETF market and whether higher AT results in lower levels of mispricing.

They use well-recognized proxies for AT:

  • The cancel rate (the number of initiated but subsequently canceled orders as a portion of total non-hidden trades)
  • The percentage of odd lot volume
  • The ratio of trade volume to order volume
  • The trade size (lit trade volume divided by lit trades)
  • Order fragmentation (1 minus the Herfindahl–Hirschman Index of order volume), which captures the level of competition between traders and the level of aggressive quotes

          The authors also compare the level of AT versus some key ETF characteristics, such as assets under management, volatility, turnover, and age of the fund.

The authors determine the absolute level of mispricing, the length of time those deviations last, and when those deviations occur. They then analyze whether changing levels of AT result in differences in mispricing, both the extent and the persistence.

What Are the Findings?

The authors find that high-frequency AT is prevalent in the ETF market and that it reduces the size of the mispricing as well as its persistence.

The authors demonstrate that there is a beneficial effect on the size of the mispricing, both directly and indirectly, through smaller bid–ask spreads. Mispricing is higher and more persistent among ETFs that are smaller and more volatile. Conversely, the mispricing and persistence are lower for funds that have been around for a long time and those that are larger and less volatile.

The results demonstrate that AT significantly reduces the persistence of the mispricing among ETFs, which “is consistent with the fact that spread mainly affects intraday arbitrage. Persistence of deviations is determined primarily by arbitrage by APs at the end of day and, therefore, is less impacted by spread.” 

What Are the Implications for Investors and Investment Managers?

This study shows that “investors can have confidence that in the presence of high-frequency trading, the ETF price is close to NAV.” For regulators, it shows that “in the ETF market, high-frequency AT plays a positive and necessary role in promoting ETF price efficiency and market stability.”

We’re using cookies, but you can turn them off in Privacy Settings.  Otherwise, you are agreeing to our use of cookies.  Accepting cookies does not mean that we are collecting personal data. Learn more in our Privacy Policy.