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Communication Technology and Exchanging Financial Assets: A Historical Perspective

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Listed:
  • Elizabeth B. Booth
  • G. Geoffrey Booth
  • John P. Broussard

Abstract

In recent years high frequency trading (HFT) in the financial markets has gained the attention of practitioners, regulators and even the general public. The existence of this type of trading is the direct result of the development of computer technology that permits information to be processed and trades made faster than humans can think. The purpose of this paper is to place the discussion of the role of this type of trading in a historical context. We accomplish this task by examining two abrupt technological changes that dramatically increased the speed at which information can be communicated and acted upon, i.e., the change of transportation based communication to telegraphy (electromagnetic) and telegraphy to computer (electronic). This examination permits us to consider more broadly the role of financial markets in society. We argue that, as they have in the past when confronted with extreme change, high frequency traders and investors will learn to co-exist but only if new rules of the game are established and new data analysis and risk management techniques developed.

Suggested Citation

  • Elizabeth B. Booth & G. Geoffrey Booth & John P. Broussard, 2014. "Communication Technology and Exchanging Financial Assets: A Historical Perspective," Business and Economic Research, Macrothink Institute, vol. 4(2), pages 308-322, December.
    • Handle: RePEc:mth:ber888:v:4:y:2014:i:2:p:308-322
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    References listed on IDEAS

    as
    1. Brownlees, C.T. & Gallo, G.M., 2006. "Financial econometric analysis at ultra-high frequency: Data handling concerns," Computational Statistics & Data Analysis, Elsevier, vol. 51(4), pages 2232-2245, December.
    2. Geoffrey Booth, G. & Kallunki, Juha-Pekka & Lin, Ji-Chai & Martikainen, Teppo, 2000. "Internalization and stock price clustering: Finnish evidence," Journal of International Money and Finance, Elsevier, vol. 19(5), pages 737-751, October.
    3. David L. Ikenberry & James P. Weston, 2008. "Clustering in US Stock Prices after Decimalisation," European Financial Management, European Financial Management Association, vol. 14(1), pages 30-54, January.
    4. Alain P. Chaboud & Benjamin Chiquoine & Erik Hjalmarsson & Clara Vega, 2014. "Rise of the Machines: Algorithmic Trading in the Foreign Exchange Market," Journal of Finance, American Finance Association, vol. 69(5), pages 2045-2084, October.
    5. Jonathan Brogaard & Terrence Hendershott & Ryan Riordan, 2014. "High-Frequency Trading and Price Discovery," The Review of Financial Studies, Society for Financial Studies, vol. 27(8), pages 2267-2306.
    6. Rachel E. Kranton & Deborah F. Minehart, 2001. "A Theory of Buyer-Seller Networks," American Economic Review, American Economic Association, vol. 91(3), pages 485-508, June.
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    More about this item

    Keywords

    High Frequency Trading; Communication; Information; Microstructure; Technology; Speed;
    All these keywords.

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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