 Speed, algorithmic trading, and market quality around macroeconomic news announcementsMartin Scholtus, Dick van Dijk and Bart Frijns Journal of Banking & Finance, 2014, vol. 38, issue C, 89-105 Abstract: This paper documents that speed is crucially important for high-frequency trading strategies based on U.S. macroeconomic news releases. Using order-level data on the highly liquid S&P 500 ETF traded on NASDAQ from January 6, 2009 to December 12, 2011, we find that a delay of 300ms or more significantly reduces returns of news-based trading strategies. This reduction is greater for high impact news and on days with high volatility. In addition, we assess the effect of algorithmic trading on market quality around macroeconomic news. In the minute following a macroeconomic news arrival, algorithmic activity increases trading volume and depth at the best quotes, but also increases volatility and leads to a drop in overall depth. Quoted half-spreads decrease (increase) when we measure algorithmic trading over the full (top of the) order book. Keywords: Macroeconomic news; High-frequency trading; Latency costs; Market activity; Event-based trading (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:jbfina:v:38:y:2014:i:c:p:89-105 DOI: 10.1016/j.jbankfin.2013.09.016 Access Statistics for this article Journal of Banking & Finance is currently edited by Ike Mathur More articles in Journal of Banking & Finance from Elsevier |
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