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Assessing Measures of Order Flow Toxicity via Perfect Trade Classification

  • Torben G. Andersen

    ()

    (Northwestern University and CREATES)

  • Oleg Bondarenko

    ()

    (University of Illinois at Chicago)

The VPIN, or Volume-synchronized Probability of INformed trading, metric is introduced by Easley, Lopez de Prado and O'Hara (ELO) as a real-time indicator of order flow toxicity. They find the measure useful in predicting return volatility and conclude it may help signal impending market turmoil. The VPIN metric involves decomposing volume into active buys and sells. We use the best-bid-offer (BBO) files from the CME Group to construct (near) perfect trade classification measures for the E-mini S&P 500 futures contract. We investigate the accuracy of the ELO Bulk Volume Classification (BVC) scheme and find it inferior to a standard tick rule based on individual transactions. Moreover, when VPIN is constructed from accurate classification, it behaves in a diametrically opposite way to BVC-VPIN. We also find the latter to have forecast power for short-term volatility solely because it generates systematic classification errors that are correlated with trading volume and return volatility. When controlling for trading intensity and volatility, the BVC-VPIN measure has no incremental predictive power for future volatility. We conclude that VPIN is not suitable for measuring order flow imbalances.

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File URL: ftp://ftp.econ.au.dk/creates/rp/13/rp13_43.pdf
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Paper provided by School of Economics and Management, University of Aarhus in its series CREATES Research Papers with number 2013-43.

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Length: 46
Date of creation: 11 2013
Date of revision:
Handle: RePEc:aah:create:2013-43
Contact details of provider: Web page: http://www.econ.au.dk/afn/

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  1. Lee, Charles M C & Ready, Mark J, 1991. " Inferring Trade Direction from Intraday Data," Journal of Finance, American Finance Association, vol. 46(2), pages 733-46, June.
  2. Joachim Grammig & Erik Theissen, 2002. "Estimating the Probability of Informed Trading - Does Trade Misclassification Matter?," Bonn Econ Discussion Papers bgse37_2002, University of Bonn, Germany.
  3. Chakrabarty, Bidisha & Li, Bingguang & Nguyen, Vanthuan & Van Ness, Robert A., 2007. "Trade classification algorithms for electronic communications network trades," Journal of Banking & Finance, Elsevier, vol. 31(12), pages 3806-3821, December.
  4. Aitken, Michael & Frino, Alex, 1996. "The accuracy of the tick test: Evidence from the Australian stock exchange," Journal of Banking & Finance, Elsevier, vol. 20(10), pages 1715-1729, December.
  5. Tauchen, George E & Pitts, Mark, 1983. "The Price Variability-Volume Relationship on Speculative Markets," Econometrica, Econometric Society, vol. 51(2), pages 485-505, March.
  6. Finucane, Thomas J., 2000. "A Direct Test of Methods for Inferring Trade Direction from Intra-Day Data," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 35(04), pages 553-576, December.
  7. Asquith, Paul & Oman, Rebecca & Safaya, Christopher, 2010. "Short sales and trade classification algorithms," Journal of Financial Markets, Elsevier, vol. 13(1), pages 157-173, February.
  8. Wu, Kesheng & Bethel, E. Wes & Gu, Ming & Leinweber, David & Rübe, Oliver, 2013. "A big data approach to analyzing market volatility," Algorithmic Finance, IOS Press, vol. 2(3-4), pages 241-267.
  9. Andersen, Torben G, 1996. " Return Volatility and Trading Volume: An Information Flow Interpretation of Stochastic Volatility," Journal of Finance, American Finance Association, vol. 51(1), pages 169-204, March.
  10. David Easley & Marcos M. López de Prado & Maureen O'Hara, 2012. "Flow Toxicity and Liquidity in a High-frequency World," Review of Financial Studies, Society for Financial Studies, vol. 25(5), pages 1457-1493.
  11. Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
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