Estimating the Probability of Informed Trading - Does Trade Misclassification Matter?
Estimating the Probability of Informed Trading - Does Trade Misclassification Matter?
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DOI：
10.2139/ssrn.887221
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发表时间：
2006-02
期刊：
影响因子：
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通讯作者：
Ekkehart Boehmer;J. Grammig;Erik Theissen
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文献类型：
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作者：
Ekkehart Boehmer;J. Grammig;Erik Theissen
Easley/Kiefer/O‘Hara/Paperman(1996)(EKOP)提出了一种经验方法，该方法允许估计知情交易的概率，并随后被用于解决市场微观结构中的一系列问题。估算所需的数据是买方和卖方发起的交易数量。这些信息通常必须通过应用像Lee/Ready(1991)提出的贸易分类算法来推断。众所周知，这些算法是不准确的。在本文中，我们进行了大量的模拟，结果表明，在应用EKOP方法时，不准确的交易分类会导致对知情交易概率的偏向估计。这一估计是向下倾斜的，偏差的大小与所涉股票的交易强度有关。使用EKOP方法论审视以往的实证研究，我们得出结论，这种偏差可能会严重影响经验微结构研究的结果。
Easley / Kiefer / O'Hara / Paperman (1996) (EKOP) have proposed an empirical methodology that allows to estimate the probability of informed trading and that has subsequently been used to address a wide range of issues in market microstructure. The data needed for estimation is the number of buyer- and seller-initiated trades. This information often has to be inferred by applying trade classification algorithms like the one proposed by Lee / Ready (1991). These algorithms are known to be inaccurate. In this paper we perform extensive simulations to show that inaccurate trade classification leads to biased estimation of the probability of informed trading when applying the EKOP methodology. The estimate is biased downward and the magnitude of the bias is related to the trading intensity of the stock in question. Scrutinizing prior empirical studies using the EKOP methodology, we conclude that the bias may severely affect the results of empirical microstructure studies.