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Information Flow and Pricing Errors: A Unified Approach to Estimation and Testing

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  • George, Thomas J
  • Hwang, Chuan-Yang

Abstract

This study examines whether rates of information flow differ between trading and nontrading periods, and whether the variances of pricing errors differ at the open and close of trading. The approach improves on existing methods by allowing for correlation between pricing errors and information flow, and by conducting inferences at the individual security level. The daytime rate of information flow is about seven times the overnight rate, and the variances of pricing errors at the open are not different from those at the close of trading. This evidence differs from existing results based on return variance ratios. Article published by Oxford University Press on behalf of the Society for Financial Studies in its journal, The Review of Financial Studies.

Suggested Citation

  • George, Thomas J & Hwang, Chuan-Yang, 2001. "Information Flow and Pricing Errors: A Unified Approach to Estimation and Testing," Review of Financial Studies, Society for Financial Studies, vol. 14(4), pages 979-1020.
  • Handle: RePEc:oup:rfinst:v:14:y:2001:i:4:p:979-1020
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    Cited by:

    1. Thomas George & Chuan-Yang Hwang & Tavy Ronen, 2010. "Bootstrap refinements in tests of microstructure frictions," Review of Quantitative Finance and Accounting, Springer, vol. 35(1), pages 47-70, July.
    2. Jan G. de Gooijer & Cees G.H. Diks & Lukasz T. Gatarek, 2009. "Information Flows around the Globe: Predicting Opening Gaps from Overnight Foreign Stock Price Patterns," Tinbergen Institute Discussion Papers 09-107/4, Tinbergen Institute.
    3. Menkveld, Albert J. & Wang, Ting, 2013. "How do designated market makers create value for small-caps?," Journal of Financial Markets, Elsevier, vol. 16(3), pages 571-603.
    4. Rodriguez, E. & Aguilar-Cornejo, M. & Femat, R. & Alvarez-Ramirez, J., 2014. "US stock market efficiency over weekly, monthly, quarterly and yearly time scales," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 413(C), pages 554-564.
    5. Alex Boulatov & Bart Taub, 2014. "Liquidity and the marginal value of information," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), pages 307-334.
    6. Hu, Shing-yang, 2006. "A simple estimate of noise and its determinant in a call auction market," International Review of Financial Analysis, Elsevier, vol. 15(4-5), pages 348-362.
    7. Chang, Rosita P. & Rhee, S. Ghon & Stone, Gregory R. & Tang, Ning, 2008. "How does the call market method affect price efficiency? Evidence from the Singapore Stock Market," Journal of Banking & Finance, Elsevier, vol. 32(10), pages 2205-2219, October.
    8. Ana-Maria Fuertes & Jose Olmo, 2016. "On Setting Day-Ahead Equity Trading Risk Limits: VaR Prediction at Market Close or Open?," Journal of Risk and Financial Management, MDPI, Open Access Journal, vol. 9(3), pages 1-20, September.
    9. Shing-yang Hu & Chang Chan, 2005. "Trading frequency and noise," Applied Financial Economics Letters, Taylor and Francis Journals, vol. 1(4), pages 243-247, July.

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