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Rumors and pre-announcement trading: why sell target stocks before acquisition announcements?

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Abstract

We track trading activity in the days preceding acquisition announcements for target firms and find that abnormally high trading volume precedes significant price movement. Using additional intraday data, we find increased active-selling in target stocks before acquisition announcements that offsets increased active-buying. This is unexpected because sellers often lose money when an acquisition is announced. After ruling out alternative explanations, we find evidence that sellers are rational investors who trade on the market’s perceived overreaction to takeover rumors. While sellers lose money when a rumor precedes an actual announcement, in most cases rumors fail to materialize into public announcements. We provide evidence that the significant pre-announcement volume we document reflects the market’s processing of highly uncertain information in takeover rumors.

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Notes

  1. The takeover premium is the excess amount offered by the acquirer above the pre-announcement target market price. Previous work documents significant stock price increases for target companies before and at the announcement of takeovers. See, for example, Jensen and Ruback (1983), Keown and Pinkerton (1981), Jarrell and Poulsen (1989), Eyssell and Arshadi (1993), and Draper and Paudyal (1999). Andrade et al. (2001) show that between 1973 and 1998 target firms gained 23.8% in the window beginning 20 days before the announcement and ending on the effective date of the acquisition. Even in Taiwan, where there is less cultural acceptance of acquisitions, the announcement return is significantly positive (Tsung-Ming and Hoshino 2000).

  2. Most research on trading volume and price change suggests, at minimum, a positive correlation between volume and unsigned returns (e.g., Crouch 1970; Harris 1986). Further, some research suggests a positive relationship between volume and signed price change (i.e., low volume suggests a price drop, and high volume suggests a price increase), e.g., Harris (1986). These papers are reviewed and summarized by Karpoff (1987).

  3. There is a buy side and a sell side for every transaction; however, the “active” side is determined by the initiating party. Active sellers (buyers) usually initiate the transaction by posting a market sell (buy) order for an immediate transaction. On the other side, passive buyers (sellers) facilitate the transaction, i.e., provide liquidity.

  4. Buy-side transactions can be initiated by potential acquirers building a toehold in targets (Singh 1998), by investors with inside information on the takeover, or by those anticipating a takeover announcement through other means (Gomes 2001). See Jarrell and Poulsen (1989) for possible sources of pre-announcement takeover information.

  5. We require a sample of rumors that is independent of whether an acquisition was subsequently announced to avoid a peek-ahead bias.

  6. However, our paper is unique in that we make a day-by-day comparison of returns versus trading volume. Our more detailed examination allows us to determine the lag between increased volume and share price movement; prior research generally assumed that these events coincided. .

  7. Insider trading is prohibited under sections 10(b) and 14(e) of the Securities Exchange Act of 1934. Section 10(b) extends the definition of insiders from corporate insiders to anyone who obtains material, non-public information from a corporate insider, or from the issuer. An insider must either disclose the material inside information or refrain from trading. Lee and Lu (2008) develop a model of informed trade, and their results suggest that regulation of insider trades can improve market efficiency.

  8. King (2009) examines pre-announcement trading in Canadian firms listed on the Toronto Stock Exchange (TSX). He finds that significant volume increases precede significant daily returns in that market as well.

  9. In addition, heterogeneity of market beliefs may also result from different information sets being available to different investors; for example, see Kim and Verrecchia (1994, 1997).

  10. In our sample period, Nasdaq listed target firms have a median of 7.65 million shares outstanding and the median market capitalization is $84 million. In comparison, the NYSE listed target firms included in our sample have a median market capitalization of $620 million, which is more than seven times of that of Nasdaq target firms.

  11. We use share price and financial statement data as at the most recent month-end before the announcement that falls at least 30 days before the announcement, and allow for a 3-month lag between fiscal year-end and financial statement availability.

  12. There are standard models for returns, such as Fama–French three factor model; however, no such models exist for trading volume. To overcome this problem, we use matching firms. See Barber and Lyon (1997) and Bhojraj and Lee (2002) for a detailed analysis of matching methods. See Loughran and Vijh (1997) and Hertzel et al. (2002) for applications of matching methods in other studies. Size and book-to-market factors are commonly controlled for in US research and in international research (e.g., Chiao et al. 2005).

  13. We also find (not reported) a small but significant positive average day 0 return for matching firms, consistent with Song and Walkling (2000), who show that the target’s rival firms also earn positive returns around the announcement date because of the increased probability that they will become takeover targets themselves. This suggests that our matches are appropriate.

  14. The Lee-Ready algorithm classifies each trade by the price at which the trade occurs. If the trading price is below (above) the midpoint of the bid-ask prices, it is classified as a seller- (buyer-) initiated trade. For trade at the bid-ask midpoint, we classify it as a sell (buy) side trade if the trading price is below (above) its preceding trading price.

  15. The results are very similar if we use the number of shares traded.

  16. Results without truncation show a similar drop in spread after the announcement day, but are noisier. We obtain similar results when truncating the top and bottom 3%. Medians also show a similar post-announcement day drop.

  17. Conrad and Niden (1992) also investigate pre-announcement spread changes for target stocks and find some evidence that spread decreases in the pre-announcement period, implying that inventory effects dominate for target stocks. The contrast between our results and theirs is likely attributable to their small sample size (37 firms) and to methodological differences.

  18. This methodology results in the identification of rumors that become large enough to warrant publication in the Wall Street Journal. Our intuition is that there may be thousands of rumors that we cannot identify using this methodology because they are passed along informally among market participants, e.g., by word of mouth. Our rumor identification procedure is one alternative to collect rumors, and it is by no means an exhaustive list; our requirement of the explicit term “rumor” reduces our sample size to a manageable level. The sample, however, suffices to illustrate the profitability of our shorting strategy.

  19. If we extend the time period further, 24 rumored firms received takeover offers in six months, and 35 of the 159 firms received offers within one year of the rumor date. Our choice of 70 trading days is consistent with the findings of Zivney et al. (1996), who find that the initial peak after rumor publication has significantly reversed by approximately 70 trading days.

  20. Extremely large companies are far more likely to be acquirers than targets, and acquisitions involving large companies in an industry with limited players are more likely to face regulatory scrutiny based on anti-trust rules; therefore a takeover rumor for a larger target is less likely to be believed, or to be overreacted to. However, the size effect found by Palepu does not appear to hold for UK targets (Barnes 1999).

  21. This designation introduces a peek-ahead bias into our results, because one cannot determine if a year is hot or cold until it ends. However, returns are similar if we identify hot periods by the rate of increase in acquisitions from year t-2 to year t-1. A real-life investor would also have a more sensitive measure for hot or cold periods than merely the number of takeovers in a year; for example, a better measure unavailable to us would be the number of published and unpublished rumors in circulation at a given time.

  22. Over our 70-trading-day holding period, the 4.3% average cost translates into an effective cost of 1.2%. After subtracting this 1.2% from the average returns reported in Table 4, we report an average CAR of 3.0%, p = 0.18, average BHAR of 3.3%, p = 0.09, and average CAR of 6.7%, p = 0.04, average BHAR of 6.6%, p = 0.02 for hot years.

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Acknowledgments

We appreciate helpful comments from Warren Bailey, Yaniv Grinstein, Maureen O’Hara, an anonymous referee, and seminar participants at Cornell University, Indiana University, the Financial Management Association Annual Meeting at Denver, and the Western Finance Association Annual Meeting at Vancouver. The paper was previously circulated under the title “Takeover Announcements and Price Discovery.” All remaining errors are our own.

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  1. School of Management, George Mason University, MSN 5F5, Fairfax, VA, 22030, USA

    Yuan Gao

  2. Rawls College of Business, Texas Tech University, Area of Accounting, Box 42101, Lubbock, TX, 79409-2101, USA

    Derek Oler

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Correspondence to Yuan Gao.

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Gao, Y., Oler, D. Rumors and pre-announcement trading: why sell target stocks before acquisition announcements?. Rev Quant Finan Acc 39, 485–508 (2012). https://doi.org/10.1007/s11156-011-0262-z

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