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Need for speed: Hard information processing in a high‐frequency world

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  • S. Sarah Zhang

Abstract

I study the role of high‐frequency traders (HFTs) and non‐high‐frequency traders (nHFTs) in transmitting hard price information from the futures market to the stock market using an index arbitrage strategy. Using intraday transaction data with HFT identification, I find that HFTs process hard information faster and trade on it more aggressively than nHFTs. In terms of liquidity supply, HFTs are better at avoiding adverse selection than nHFTs. Consequently, HFTs enhance the linkage between the futures and stock markets, and significantly contribute to information efficiency in the stock market by reducing the delay between the stock and the futures markets.

Suggested Citation

  • S. Sarah Zhang, 2018. "Need for speed: Hard information processing in a high‐frequency world," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(1), pages 3-21, January.
  • Handle: RePEc:wly:jfutmk:v:38:y:2018:i:1:p:3-21
    DOI: 10.1002/fut.21861
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    References listed on IDEAS

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    Cited by:

    1. Zhou, Hao & Elliott, Robert J. & Kalev, Petko S., 2019. "Information or noise: What does algorithmic trading incorporate into the stock prices?," International Review of Financial Analysis, Elsevier, vol. 63(C), pages 27-39.
    2. Foucault, Thierry & Moinas, Sophie, 2018. "Is Trading Fast Dangerous?," TSE Working Papers 18-881, Toulouse School of Economics (TSE).
    3. Donald B. Keim & Massimo Massa & Bastian von Beschwitz, 2018. "First to \"Read\" the News: New Analytics and Algorithmic Trading," International Finance Discussion Papers 1233, Board of Governors of the Federal Reserve System (U.S.).
    4. Mestel, Roland & Murg, Michael & Theissen, Erik, 2018. "Algorithmic trading and liquidity: Long term evidence from Austria," Finance Research Letters, Elsevier, vol. 26(C), pages 198-203.

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