High-Frequency Measures of Informed Trading and Corporate Announcements

Michael J. Brennan is Professor Emeritus of Finance at the UCLA Anderson School of Management, and Professor of Finance at the University of Manchester Business School; Sahn-Wook Huh is Associate Professor of Finance at the University (SUNY) at Buffalo School of Management; and Avanidhar Subrahmanyam is Professor of Finance at the UCLA Anderson School of Management. This post is based on their recent article, forthcoming in The Review of Financial Studies.

While the activities of privately informed traders have been studied extensively, it remains a challenge to obtain empirical evidence on trading by informed investors because of the difficulty of determining when trades result from private information. In this article, we use comprehensive transactions datasets to analyze informed trading around three unscheduled corporate announcements (M&As, SEOs, and dividend initiations), as well as around pre-scheduled earnings announcements. We also examine the links between our informed-trading measures and stock returns around the announcements.

We calculate the posterior probability of informed trading in a stock for each day, given the observed buy and sell transactions for that day. The probability measures are based on PIN, the probability of informed trading in the model of Easley, Kiefer, O’Hara, and Paperman (EKOP) (1996), which we extend by distinguishing between the probabilities of informed buying and informed selling and by calculating the probabilities for each day around the announcement.

While the focus of most studies has been on informed trading prior to an announcement, Kim and Verrecchia (1994) and others have shown theoretically that informed trading may also occur after public announcements. We find strong evidence of informed trading after, as well as before, announcements of merger bids, dividend initiations, SEOs, and quarterly earnings. Informed trading before announcements reduces the price impact of the announcement itself.

More significantly, the estimated probabilities of informed buying and selling following merger bid announcements contain information about whether the bid will be withdrawn or a competing bid emerge, as well as about subsequent stock returns to target shareholders. We also find evidence of informed trading following announcements of dividend initiations and SEOs. This implies that some market participants are faster or better able to interpret the information in public announcements.

Similarly, our analysis of the conditional probabilities around quarterly earnings announcements yields evidence of informed trading both before and after the announcements. High probabilities of informed trading prior to the announcement reduce the stock price response to earnings surprises, suggesting that the informed trading that we identify impounds information about the forthcoming earnings news into the stock price before the announcement. The conditional probabilities after the earnings announcement contain information about future stock returns, confirming that these probabilities are also related to informed trading.

In sum, our contribution is (i) to calculate metrics for informed buying and selling separately on a daily basis, (ii) to examine how they behave across a variety of scheduled and unscheduled corporate announcements, and (iii) to investigate whether these measures predict corporate outcomes and stock returns before and after the announcements. To the best of our knowledge, such a comprehensive investigation of informed-trading metrics around corporate events and their relation to future stock returns has not been conducted in the literature.

While we acknowledge that the ability of PIN to identify informed trading has been disputed in the literature, the totality of our evidence accords with the ability of the model to identify occasions of informed trading before and after public announcements, and to identify informed trading that is predictive of subsequent events and security returns. Our evidence of post-announcement informed trading offers support for frameworks such as Kim and Verrecchia (1994, 1997) in which different agents have different interpretations of public information signals, or models in which different agents have different abilities to process the information in public announcements. The fact that we are able to identify informed trading after public announcements points to caution in interpreting trading on information as trading on private information.

The complete article is available for download here.

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