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A faster estimation method for the probability of informed trading using hierarchical agglomerative clustering

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  • Quan Gan
  • Wang Chun Wei
  • David Johnstone

Abstract

The probability of informed trading (PIN) is a commonly used market microstructure measure for detecting the level of information asymmetry. Estimating PIN can be problematic due to corner solutions, local maxima and floating point exceptions (FPE). Yan and Zhang [ J. Bank. Finance , 2012, 36 , 454-467] show that whilst factorization can solve FPE, boundary solutions appear frequently in maximum likelihood estimation for PIN. A grid search initial value algorithm is suggested to overcome this problem. We present a faster method for reducing the likelihood of boundary solutions and local maxima based on hierarchical agglomerative clustering (HAC). We show that HAC can be used to determine an accurate and fast starting value approximation for PIN. This assists the maximum likelihood estimation process in both speed and accuracy.

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  • Quan Gan & Wang Chun Wei & David Johnstone, 2015. "A faster estimation method for the probability of informed trading using hierarchical agglomerative clustering," Quantitative Finance, Taylor & Francis Journals, vol. 15(11), pages 1805-1821, November.
    • Handle: RePEc:taf:quantf:v:15:y:2015:i:11:p:1805-1821
    DOI: 10.1080/14697688.2015.1023336
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    Cited by:

    1. Ersan, Oguz & Alıcı, Aslı, 2016. "An unbiased computation methodology for estimating the probability of informed trading (PIN)," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 43(C), pages 74-94.
    2. Yang, Haijun & Xue, Feng, 2021. "Analysis of stock market volatility: Adjusted VPIN with high-frequency data," International Review of Economics & Finance, Elsevier, vol. 75(C), pages 210-222.
    3. Griffin, Jim & Oberoi, Jaideep & Oduro, Samuel D., 2021. "Estimating the probability of informed trading: A Bayesian approach," Journal of Banking & Finance, Elsevier, vol. 125(C).
    4. Ke, Wen-Chyan & Chen, Hueiling & Lin, Hsiou-Wei William, 2019. "A note of techniques that mitigate floating-point errors in PIN estimation," Finance Research Letters, Elsevier, vol. 31(C).
    5. Chu-Lan Michael Kao & Emily Lin, 2023. "A new PIN model with application of the change-point detection method," Review of Quantitative Finance and Accounting, Springer, vol. 61(4), pages 1513-1528, November.
    6. Emily Lin & Chu-Lan Michael Kao & Natasha Sonia Adityarini, 2021. "Data-driven tree structure for PIN models," Review of Quantitative Finance and Accounting, Springer, vol. 57(2), pages 411-427, August.
    7. 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.
    8. David Abad & M. Fuensanta Cutillas†Gomariz & Juan Pedro Sánchez†Ballesta & José Yagüe, 2018. "Does IFRS Mandatory Adoption Affect Information Asymmetry in the Stock Market?," Australian Accounting Review, CPA Australia, vol. 28(1), pages 61-78, March.
    9. Marzagão, Thiago, 2021. "Insider trading in Brazil's stock market," OSF Preprints fu9mg, Center for Open Science.
    10. Cosmin Octavian Cepoi & Victor Dragotă & Ruxandra Trifan & Andreea Iordache, 2023. "Probability of informed trading during the COVID-19 pandemic: the case of the Romanian stock market," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-27, December.

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