Algorithmic quoting, trading, and market quality in agricultural commodity futures markets

Zhepeng Hu, Serra Teresa, Philip Garcia

Research output: Contribution to journalArticlepeer-review


This paper investigates the effect of algorithmic trading activity, as measured by quoting, on the corn, soybean, and live cattle commodity futures market quality. Using the CME’s limit-order-book data and a heteroscedasticity-based identification approach, we find more intensive algorithmic quoting (AQ) is beneficial to multiple dimensions of market quality. AQ improves pricing efficiency and mitigates short-term volatility, but its effects on liquidity costs are somewhat mixed. Increased AQ significantly narrows effective spreads in the corn and soybean markets, but not in the less traded live cattle futures market. The narrowing in effective spreads emerges from a reduction in adverse selection costs as more informed traders lose their market advantage. There also is evidence that liquidity provider revenues increase with heightened AQ activity in the corn futures market, albeit the effect is not statistically significant in the soybean and live cattle futures markets. The increased revenue points to a tradeoff between the dimensions of market quality, and the need for continued assessment and monitoring of algorithmic trading activity.

Original languageEnglish (US)
Pages (from-to)6277-6291
Number of pages15
JournalApplied Economics
StatePublished - 2020


  • Algorithmic trading
  • commodity futures
  • identification through heteroscedasticity
  • market quality

ASJC Scopus subject areas

  • Economics and Econometrics


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