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Estimating the Probability of Informed Trading

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  • Ken Nyholm

Abstract

Using a new empirical model, I estimate the probability of trades being generated by privately informed traders. Inference is drawn on a trade‐by‐trade basis using data samples from the New York Stock Exchange (NYSE). The modeling setup facilitates in‐depth analysis of the estimated probability of informed trading at the intraday level and for stocks with different levels of trading activity. The most important empirical results are: (a) the intradaily pattern of the inferred probability of informed trading is highly correlated with the intradaily pattern of observed quoted spreads, (b) differences in the magnitude of quoted spreads across volume categories are not exclusively related to differences in the level of informed trading, and (c) private information is incorporated faster in the quotes for high‐volume stocks than in the quotes for low‐volume stocks.

Suggested Citation

  • Ken Nyholm, 2002. "Estimating the Probability of Informed Trading," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 25(4), pages 485-505, December.
    • Handle: RePEc:bla:jfnres:v:25:y:2002:i:4:p:485-505
    DOI: 10.1111/1475-6803.00033
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    Cited by:

    1. David Abad & Antonio Rubia, 2005. "Modelos De Estimacion De La Probabilidad De Negociacion Informada: Una Comparacion Metodologica En El Mercado Español," Working Papers. Serie EC 2005-12, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
    2. David Abad & Antonio Rubia, 2004. "Estimating The Probability Of Informed Trading: Further Evidence From An Order-Driven Market," Working Papers. Serie AD 2004-38, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
    3. 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.
    4. Giovanni Luca & Giampiero Gallo, 2009. "Time-Varying Mixing Weights in Mixture Autoregressive Conditional Duration Models," Econometric Reviews, Taylor & Francis Journals, vol. 28(1-3), pages 102-120.
    5. 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.
    6. 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.
    7. Cosmin Octavian Cepoi & Filip Mihai Toma, 2016. "Estimating Probability of Informed Trading on the Bucharest Stock Exchange," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 66(2), pages 140-160, April.
    8. Alzahrani, Ahmed A. & Gregoriou, Andros & Hudson, Robert, 2013. "Price impact of block trades in the Saudi stock market," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 23(C), pages 322-341.

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