Disclosure: The authors report no conflicts of interest.Editor's note:Submitted 6 July 2020 Accepted 10 December 2020 by Stephen J. Brown This article was externally reviewed using our double-blind peer-review process. When the article was accepted for publication, the authors thanked the reviewers in their acknowledgments. Marius Zoican and one anonymous reviewer were the reviewers for this article.In the study reported here, we explored high-frequency algorithmic trading and its effect on exchange-traded funds (ETFs). Using the cancel rate, the trade-to-order ratio, percentage odd-lot volume, and trade size as proxies for algorithmic trading, we found that more algorithmic trading in ETFs results in smaller and less persistent deviations of fund prices from their net asset values (NAVs). Arbitrage strategies adopted by algorithmic traders directly help reduce the magnitude and persistence of ETF price deviations from NAVs. Also, algorithmic trading improves ETF liquidity by lowering spreads and facilitates arbitrage.
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机译：披露：提交人报告说没有利益冲突。请注意：2020年7月6日提交的2020年12月10日由Stephen J. Brown本文使用我们的双盲同行评审过程进行了外部审查。当文章被接受出版时，提交人感谢审稿人的致谢。 Marius Zoican和一个匿名评论家是这篇文章的审稿人。在此处报告的研究中，我们探讨了高频算法交易及其对交易交易基金（ETF）的影响。使用取消率，权衡率，百分比乘积百分比和交易大小作为算法交易的代理，我们发现ETF中的更多算法交易导致来自其净资产的资金价格较小，持久持续偏差值（NAV）。算法交易者采用的套利策略直接帮助降低ETF价格偏差的幅度和持久性。此外，算法交易通过降低差距来提高ETF流动性，并有助于套利。
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