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Algorithmic Trading and Liquidity: Long Term Evidence from Austria

Author

Listed:
  • Roland Mestel

    (Institute of Banking and Finance, Karl-Franzens-University Graz)

  • Michael Murg

    (Institute of Banking and Insurance, University of Applied Sciences FH Joanneum)

  • Erik Theissen

    (Institute of Banking and Finance, University of Graz
    Finance Area, University of Mannheim)

Abstract

We analyze the relation between algorithmic trading and liquidity using a novel data set from the Austrian equity market. Our sample covers almost 4.5 years, it identifies the market share of algorithmic trading at the stock-day level, and it comes from a market that has hitherto not been analyzed. We address the endogeneity problem using an instrumental variables approach. Our results indicate that an increase in the market share of algorithmic trading causes a reduction in quoted and effective spreads while quoted depth and price impacts are unaffected. They are consistent with algorithmic traders on average acting as market makers.

Suggested Citation

  • Roland Mestel & Michael Murg & Erik Theissen, 2018. "Algorithmic Trading and Liquidity: Long Term Evidence from Austria," Working Paper Series, Social and Economic Sciences 2018-03, Faculty of Social and Economic Sciences, Karl-Franzens-University Graz.
    • Handle: RePEc:grz:wpsses:2018-03
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    References listed on IDEAS

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    Cited by:

    1. Jarosław Duda & Henryk Gurgul & Robert Syrek, 2020. "Modelling bid-ask spread conditional distributions using hierarchical correlation reconstruction," Statistics in Transition New Series, Polish Statistical Association, vol. 21(5), pages 99-118, December.
    2. Hung, Pi-Hsia & Lien, Donald, 2019. "Trading aggressiveness, order execution quality, and stock price movements: Evidence from the Taiwan stock exchange," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 60(C), pages 231-251.
    3. Ramos, Henrique Pinto & Perlin, Marcelo Scherer, 2020. "Does algorithmic trading harm liquidity? Evidence from Brazil," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    4. Alex Frino & Dionigi Gerace & Masud Behnia, 2021. "The impact of algorithmic trading on liquidity in futures markets: New insights into the resiliency of spreads and depth," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(8), pages 1301-1314, August.

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    More about this item

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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