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The sensitivity of VPIN to the choice of trade classification algorithm

Author

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  • Pöppe, Thomas
  • Moos, Sebastian
  • Schiereck, Dirk

Abstract

The VPIN metric (Easley et al. 2012b) aims to detect and predict the toxicity of order flow. This paper examines the sensitivity and robustness of VPIN to the choice of trade classification scheme, which is the major input used to compute VPIN. We compare deterministic trade-by-trade classification approaches with results computed using a newly proposed heuristic approach, bulk volume classification. We find substantial differences for all levels of aggregation: trade classification, order imbalance, VPIN and identifying “toxic periods”. We also find that the detection of toxic periods does not yield consistent results in more than 60% of cases. But regression analysis can identify volume and return volatility as parameters that contribute to higher levels of sensitivity.

Suggested Citation

  • Pöppe, Thomas & Moos, Sebastian & Schiereck, Dirk, 2016. "The sensitivity of VPIN to the choice of trade classification algorithm," Journal of Banking & Finance, Elsevier, vol. 73(C), pages 165-181.
    • Handle: RePEc:eee:jbfina:v:73:y:2016:i:c:p:165-181
    DOI: 10.1016/j.jbankfin.2016.08.006
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    Citations

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    Cited by:

    1. Frömmel, Michael & D'Hoore, Dick & Lampaert, Kevin, 2021. "The Accuracy of Trade Classification Systems on the Foreign Exchange Market: Evidence from the RUB/USD Market," Finance Research Letters, Elsevier, vol. 42(C).
    2. Allen Carrion & Madhuparna Kolay, 2020. "Trade signing in fast markets," The Financial Review, Eastern Finance Association, vol. 55(3), pages 385-404, August.
    3. Abad, David & Massot, Magdalena & Pascual, Roberto, 2018. "Evaluating VPIN as a trigger for single-stock circuit breakers," Journal of Banking & Finance, Elsevier, vol. 86(C), pages 21-36.
    4. Jangkoo Kang & Kyung Yoon Kwon & Wooyeon Kim, 2020. "Flow toxicity of high‐frequency trading and its impact on price volatility: Evidence from the KOSPI 200 futures market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 40(2), pages 164-191, February.
    5. Imtiaz Mohammad Sifat & Azhar Mohamad, 2019. "Circuit breakers as market stability levers: A survey of research, praxis, and challenges," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 24(3), pages 1130-1169, July.
    6. Michael Frömmel & Eyup Kadioglu, 2023. "Impact of trading hours extensions on foreign exchange volatility: intraday evidence from the Moscow exchange," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-23, December.
    7. Sifat, Imtiaz Mohammad & Mohamad, Azhar, 2020. "A survey on the magnet effect of circuit breakers in financial markets," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 138-151.
    8. Prodromou, Tina & Westerholm, P. Joakim, 2022. "Are high frequency traders responsible for extreme price movements?," Economic Analysis and Policy, Elsevier, vol. 73(C), pages 94-111.
    9. Ulze, Markus & Stadler, Johannes & Rathgeber, Andreas W., 2021. "No country for old distributions? On the comparison of implied option parameters between the Brownian motion and variance gamma process," The Quarterly Review of Economics and Finance, Elsevier, vol. 82(C), pages 163-184.

    More about this item

    Keywords

    Capital markets; Information asymmetry; Volume-synchronized probability of informed trading; VPIN; Trade classification;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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