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A dynamic intraday measure of the probability of informed trading and firm-specific return variation

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  • Chang, Sanders S.
  • Chang, Lenisa V.
  • Wang, F. Albert

Abstract

A central question in financial economics is how private information is incorporated into asset prices. A common method of measuring private information is the PIN measure, which uses statistical estimation of a sequential trade model of the trading process to estimate the probability of informed trading. A notable limiting feature of PIN is that one must aggregate very fine intraday data over very long macro horizons in order to estimate it. In this paper, our aim is to develop and implement a dynamic intraday measure of the probability of informed trading that circumvents this aggregation issue and allows for the measurement of information based trading activity at much higher frequencies. We then apply our dynamic intraday measure of the probability of informed trading to examine the relationship between private information and firm-specific return variation.

Suggested Citation

  • Chang, Sanders S. & Chang, Lenisa V. & Wang, F. Albert, 2014. "A dynamic intraday measure of the probability of informed trading and firm-specific return variation," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 80-94.
    • Handle: RePEc:eee:empfin:v:29:y:2014:i:c:p:80-94
    DOI: 10.1016/j.jempfin.2014.02.003
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    Cited by:

    1. Kitamura, Yoshihiro, 2016. "The probability of informed trading measured with price impact, price reversal, and volatility," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 42(C), pages 77-90.
    2. Chang, Sanders S. & Wang, F. Albert, 2015. "Adverse selection and the presence of informed trading," Journal of Empirical Finance, Elsevier, vol. 33(C), pages 19-33.
    3. Elaut, Gert & Frömmel, Michael & Lampaert, Kevin, 2018. "Intraday momentum in FX markets: Disentangling informed trading from liquidity provision," Journal of Financial Markets, Elsevier, vol. 37(C), pages 35-51.
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    5. Keunbae Ahn, 2021. "Predictable Fluctuations in the Cross-Section and Time-Series of Asset Prices," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 1-2021, January-A.
    6. Ping-Chen Tsai & Chi-Ming Tsai, 2021. "Estimating the proportion of informed and speculative traders in financial markets: evidence from exchange rate," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 16(3), pages 443-470, July.
    7. 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.
    8. Petchey, James & Wee, Marvin & Yang, Joey, 2016. "Pinning down an effective measure for probability of informed trading," Pacific-Basin Finance Journal, Elsevier, vol. 40(PB), pages 456-475.
    9. Cheng, Feiyang & Chiao, Chaoshin & Wang, Chunfeng & Fang, Zhenming & Yao, Shouyu, 2021. "Does retail investor attention improve stock liquidity? A dynamic perspective," Economic Modelling, Elsevier, vol. 94(C), pages 170-183.
    10. Wang, Yaqi & Wang, Chunfeng & Sensoy, Ahmet & Yao, Shouyu & Cheng, Feiyang, 2022. "Can investors’ informed trading predict cryptocurrency returns? Evidence from machine learning," Research in International Business and Finance, Elsevier, vol. 62(C).

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    More about this item

    Keywords

    Informed trading; Private information; Price discovery; High-frequency; Firm-specific return variation; Price non-synchronicity;
    All these keywords.

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G19 - Financial Economics - - General Financial Markets - - - Other

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