The 49 reference contexts in paper Alexander Kurov, Alessio Sancetta, Georg H. Strasser, Marketa Halova Wolfe (2015) “Price Drift before U.S. Macroeconomic News: Private Information about Public Announcements?” / RePEc:boc:bocoec:881

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    These announcements are quintessential updates to public information on the economy and fundamental inputs to asset pricing. More than a half of the cumulative annual equity risk premium is earned on announcement days
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    (Savor & Wilson, 2013) and
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    the information is almost instantaneously reflected in prices once released (Hu, Pan, & Wang, 2013). To ensure fairness, no market participant should have access to this information until the official release time.
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    The second group does separate the pre- and post-announcement effects but concludes that the pre-announcement effect is small or non-existent. Our results differ from those in previous research for four reasons. First, some studies measure the pre-announcement effect in small increments of time. For example,
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    Ederington and Lee (1995)
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    use 10-second returns in the [t−2min,t+ 10min] window around 18 U.S. macroeconomic announcements from 1988 to 1992, and report that significant price moves occur only in the post-announcement interval in the Treasury, Eurodollar and DEM/USD futures markets.
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    However, if the pre-announcement drift is gradual (which is the case in our data), it will not be detected in such small increments. Our approach uses a longer pre-announcement interval and uncovers the price drift. Second, other studies consider only short pre-announcement intervals.
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    Andersen et al. (2007),
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    for example, include ten minutes before the official release time. In a sample of 25 U.S. announcements from 1998 to 2002, they find that global stock, bond and foreign exchange markets react to announcements only after their official release time.
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    Abnormal returns and order imbalances (measured as the difference between buyer- and seller-initiated trading volumes divided by the total trading volume) in the “correct” direction are found before the FOMC meetings but not before the other announcements.
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    Bernile et al. (2015)
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    suggest these findings are consistent with information leakage.1 Our study differs from Hautsch et al. (2011) and Bernile et al. (2015) in two important aspects. First, our methodology and an expanded set of announcements allow us to show that pre-announcement informed trading is limited neither to FOMC announcements nor to the last minute before the official release time.
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    Abnormal returns and order imbalances (measured as the difference between buyer- and seller-initiated trading volumes divided by the total trading volume) in the “correct” direction are found before the FOMC meetings but not before the other announcements. Bernile et al. (2015) suggest these findings are consistent with information leakage.1 Our study differs from
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    Hautsch et al. (2011) and Bernile et al. (2015)
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    in two important aspects. First, our methodology and an expanded set of announcements allow us to show that pre-announcement informed trading is limited neither to FOMC announcements nor to the last minute before the official release time.
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    Second, instead of assuming information leakage, we consider other possible sources of informed trading around public announcements. The corporate finance literature regards price drift before public guidance issued by company management as de facto evidence of information leakage (for example,
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    Sinha and Gadarowski (2010) and Agapova and Madura (2011)).
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    We address the information leakage explanation by examining two aspects of the announcement release process: organization type and release procedures.2 With respect to organization type, we focus on the difference between public and private entities.
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    The third category, previously not documented in academic literature, involves an unusual pre-release procedure used in three announcements: Instead of being pre-released in lock-up rooms, these 1Beyond these studies that investigate responses to announcementsconditionalon the surprise,
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    Lucca and Moench (2015)
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    reportunconditionalexcess returns in equity index futures during 24 hours prior to the FOMC announcements. They do not find excess returns for nine U.S. macroeconomic announcements or in Treasury securities and money market futures. 2Macroeconomic announcement leakage has been documented in other countries.
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    For example, Andersson, Overby, and Sebesty ́en (2009) analyze news wires and present evidence that the German employment report is regularly known to investors prior to its official release. Information leakage has also occurred in other settings, for example, in the London PM gold price fixing
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    (Caminschi & Heaney, 2013).
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    announcements are electronically transmitted to journalists who are asked not to share the information with others. These three announcements are among the seven announcements with strong drift. Leaked information is only one possible cause of informed trading.
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    These three announcements are among the seven announcements with strong drift. Leaked information is only one possible cause of informed trading. We aim to consider any information produced by informed investors and impounded into prices through trading
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    (French & Roll, 1986).
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    3Some traders may be able to collect proprietary information or analyze public information in a superior way to forecast announcements better than other traders. This knowledge can then be utilized to trade in the “correct” direction before announcements.
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    Explanations for the drift are tested in Section 5 and a brief discussion concludes in Section 6. 2 Methodology We assume that efficient markets react only to the unexpected component of news announcements (“the surprise”),Smt. The effect of news announcements on asset prices can then be analyzed by standard event study methodology
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    (Balduzzi et al., 2001).
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    LetRt+τt−τdenote the continuously compounded asset return around the official release timetof announcementm, defined as the first difference between the log prices at the beginning and at the end of the intraday event window [t−τ,t+τ].
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    compounded asset return around the official release timetof announcementm, defined as the first difference between the log prices at the beginning and at the end of the intraday event window [t−τ,t+τ]. The reaction of asset returns to the surprise is captured by the ordinary least squares regression Rt+τt−τ=γ0+γmSmt+εt,(1) whereγ0captures the unconditional price drift around the release time
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    (Lucca & Moench, 2015)
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    andεtis an i.i.d. error term reflecting price movements unrelated to the announcements. The standardized surprise,Smt, is based on the difference between the actual announce3In the corporate finance literature on trading around company earnings announcements, Campbell et al. (2009) and Kaniel et al. (2012) also remain agnostic about the source of informed trading by institutional and individual i
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    squares regression Rt+τt−τ=γ0+γmSmt+εt,(1) whereγ0captures the unconditional price drift around the release time (Lucca & Moench, 2015) andεtis an i.i.d. error term reflecting price movements unrelated to the announcements. The standardized surprise,Smt, is based on the difference between the actual announce3In the corporate finance literature on trading around company earnings announcements,
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    Campbell et al. (2009) and Kaniel et al. (2012)
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    also remain agnostic about the source of informed trading by institutional and individual investors, respectively. ment,Amt, released at timetand the market’s expectation of the announcement before its release,Em,t−τ[Amt].4We standardize the difference by the standard deviation of the respective announcement,σm, to convert them to equal units.
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    The coefficients are not significant suggesting that markets indeed do not react to theexpectedcomponent of news announcements. 5Survey-based forecasts have been shown to outperform forecasts using historical values of macroeconomic variables (see, for example,
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    Pearce and Roley (1985)).
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    6For example, for one particular GDP release in 2014, only three out of 86 professional forecasters updated their forecasts during the 48 hours before the announcement. lease time on June 3, 2013 (Javers, 2013b).
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    of news announcements. 5Survey-based forecasts have been shown to outperform forecasts using historical values of macroeconomic variables (see, for example, Pearce and Roley (1985)). 6For example, for one particular GDP release in 2014, only three out of 86 professional forecasters updated their forecasts during the 48 hours before the announcement. lease time on June 3, 2013
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    (Javers, 2013b).
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    Scholtus, van Dijk, and Frijns (2014) compare the official release times to the actual release times and show that such accidental early releases are rare and occur only milliseconds before the official release time.
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    Therefore, using five seconds before the official release time as the pre-announcement interval cutoff suffices to ensure that none of the accidental early releases fall into the pre-announcement interval.7 3 Data We start with 23 macroeconomic announcements from
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    Andersen et al. (2003)
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    which is the largest set of announcements among the previous seminal studies.8We augment this set by seven announcements that are frequently discussed in the financial press: Automatic Data Processing (ADP) Employment, Building Permits, Existing Home Sales, the Institute for Supply Management (ISM) Non-Manufacturing Index, Pending Home Sales, and the Preliminary an
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    Bloomberg calculates the consensus forecast as the median of individual forecasts and continuously updates 7Results with the [t−30min,t] window are similar, suggesting that the extra drift in the last five seconds before the announcement is not substantial. 8The National Association of Purchasing Managers index analyzed in
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    Andersen et al. (2003)
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    is currently called ISM Manufacturing Index. We do not report results for the Capacity Utilization announcement because it is always released simultaneously with the Industrial Production announcement and the surprise components of these two announcements are strongly correlated with a correlation coefficient of +0.8.
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    We classify each event as “good” or “bad” news based on whether the surprise has a positive or negative effect on the stock and bond markets using the coefficients in Table 2. Following 12The ratio exceeding 100 percent in the ISM Non-Manufacturing Index is due to a partial reversal of the pre-announcement price drift after the release time.
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    Bernile et al. (2015),
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    we invert the sign of returns for negative surprises.13CARs are then calculated in the [t−60min,t+ 60min] window for each of the “strong drift”, “some drift” and “no drift” categories defined in Table 4.14The CARs in Figure 1 reveal what happens around the announcements.
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    The Appendix Figure A1 shows CARs for the individual announcements. 16The drift in both the stock and bond markets is particularly pronounced before large surprises. See Appendix Figure A2 for more detail. 17The results are shown in the Internet Appendix Table B1. The methodology using CARs to calculate the proportions follows
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    Sinha and Gadarowski (2010) and Agapova and Madura (2011)
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    in the corporate finance literature. In contrast to the Table 5 methodology that takes into account both the sign and the size of the surprise, the CAR methodology takes only the sign into account. Figure 1: Cumulative Average Returns E-mini S&P 500 Futures10-year Treasury Note Futures (a) Announcements with no evidence of drift 0.25 0.00 0.02 ‐60‐40‐200 204060 0.20 0.15 ‐0.02 0.10 ‐0.04 0.05
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    The informed traders could also be strategizing the timing in an attempt to “hide” their trades. Trading on private information is easier when trading volume is high because it is likelier that informed trades will go unnoticed
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    (Kyle, 1985).
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    Interestingly, five out of the seven drift announcements are released at 10 a.m. following a large increase in trading volume in the E-mini S&P 500 futures market (and a smaller one in the 10-year Treasury note futures market) at the opening of the stock market and the beginning of open outcry trading in the S&P 500 futures market at 9:30.18 4.3 Order Flow Imbalances and Profits to Informed Tr
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    note futures market) at the opening of the stock market and the beginning of open outcry trading in the S&P 500 futures market at 9:30.18 4.3 Order Flow Imbalances and Profits to Informed Trading Evidence of informed trading is not limited to prices but visible in order imbalances as well. We use data on the total trading volume and the last trade price in each one-second interval. Following
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    Bernile et al. (2015),
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    we classify the trading volume as buyer- or seller-initiated using the tick rule. Specifically, the trade volume in a one-second interval is classified as buyer-initiated (seller-initiated) if the price for that interval is higher (lower) than the last different price.19Figure 2 plots cumulative order imbalances for the same time window as Figure 1.
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    For example, Boyd, Hu, and Jagannathan (2005) report that from 1957 to 2000 higher unemployment pushed the stock market up during expansions but drove it down during contractions.
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    Andersen et al. (2007)
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    show that the stock market reaction to macroeconomic announcements differs across the business cycle with good economic news causing a negative response in expansions but a positive response in contractions.
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    Andersen et al. (2007) show that the stock market reaction to macroeconomic announcements differs across the business cycle with good economic news causing a negative response in expansions but a positive response in contractions.
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    Andersen et al. (2007)
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    argue that in expansions the discount factor component of the equity valuation prevails compared to the cash flow component due to anti-inflationary monetary policies. This state-dependence suggests that the pre-2008 and post-2008 periods should differ, and our results confirm this. 23We estimate equation (1) for the [t−30min,t−1min] window with minute-by-minute data.
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    This control variable is usually insignificant and the results from Section 4.1 maintain, which is consistent with the CARs in Figure 1 remaining near zero until 30 minutes before release time. Second, we employ a time-series approach following, for example,
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    Andersen et al. (2003)
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    where all announcements are embedded in a single regression. Here, the returnsRtare the first differences of log prices within a fixed time grid. We model this return, separately for each market, as a linear function of lagged surprises of each announcement to capture the impact that an announcement may have on the market in the following periods, lead values of each announcement surprise to cap
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    year Treasury note futures are equal to zero. 4.5.2 Data Snooping When testing multiple hypotheses, increasing the number of hypotheses leads to the rejection of an increasing number of hypotheses with probability one, irrespective of the sample size. Failure to adjust thep-values can be viewed as data snooping. To rule out this possibility in our joint tests for 18 announcements, we use the
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    Holm (1979)
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    step-down procedure. This procedure adjusts the hypothesis rejection criteria to control the probability of encountering one or more type I errors, the familywise error rate (see, for example, Romano and Wolf (2005)).
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    To rule out this possibility in our joint tests for 18 announcements, we use the Holm (1979) step-down procedure. This procedure adjusts the hypothesis rejection criteria to control the probability of encountering one or more type I errors, the familywise error rate (see, for example,
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    Romano and Wolf (2005)).
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    Based on this conservative approach, five announcements ranked at the top of Table 3 show a significant drift (ISM Manufacturing, ISM Non-Manufacturing and Pending Home Sales at 1%, Existing Home Sales at 5%, and CB Consumer Confidence Index at 10% significance levels).28 28We report these results in the Internet Appendix Table B2 along with a description of the data snooping robustness
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    Shortening the pre-announcement window generally results in lower coefficients (and lower standard errors), which is typical for intraday studies where the ratio between signal (i.e., response to the news announcement) and noise increases as the event window shrinks and fewer other events affect the market. With regards to thepost-announcementwindow length, previous studies (for example,
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    Hu et al. (2013))
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    report that information is almost instantaneously reflected in prices once released. However, a joint test of significance of price moves in the [t+ 10min,t+ 30min] window for all 30 announcements (available upon request) shows some evidence of continuing adjustment.
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    An equality of these two information sets would require, first, that there is no information in the market beyond public information, and, second, that the public information is fully captured by the 29See Internet Appendix Table B3. Bloomberg consensus forecast. A popular explanation for a failure of the first requirement is information leakage. The corporate finance literature (for example,
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    Sinha and Gadarowski (2010) and Agapova and Madura (2011))
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    considers price drift before public guidance issued by company management as de facto evidence of information leakage. Bernile et al. (2015) also point to information leakage as the cause of informed trading before the FOMC announcements.
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    The corporate finance literature (for example, Sinha and Gadarowski (2010) and Agapova and Madura (2011)) considers price drift before public guidance issued by company management as de facto evidence of information leakage.
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    Bernile et al. (2015)
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    also point to information leakage as the cause of informed trading before the FOMC announcements. But at least one alternative explanation exists. Some traders may collect proprietary information which allows them to forecast announcements better than other traders.
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    We start with private information obtained by leakage and follow with private information obtained by proprietary data collection. 5.1.1 Information Leakage Insider trading based on leaked information can seriously impair markets. It reduces risk sharing and the informational efficiency of prices in the long run
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    (Brunnermeier, 2005).
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    The U.S. macroeconomic data is generally considered closely guarded as federal agencies restrict the number of employees with access to the data, implement computer security measures, and take other actions to prevent premature dissemination.
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    The U.S. macroeconomic data is generally considered closely guarded as federal agencies restrict the number of employees with access to the data, implement computer security measures, and take other actions to prevent premature dissemination. The procedures of the DOL, for example, are described in
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    Fillichio (2012).
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    The last documented case of a U.S. government employee fired for data leakage dates far back. In 1986, one employee of the Commerce Department was terminated for leaking the Gross National Product data (Wall Street Journal, 1986).
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    The last documented case of a U.S. government employee fired for data leakage dates far back. In 1986, one employee of the Commerce Department was terminated for leaking the Gross National Product data
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    (Wall Street Journal, 1986).
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    However, the possibility of leakage in more recent times still exists. In this section, we examine two aspects of the release process that may affect leakage: organization type and release procedures.
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    Some data providers have been known to release information to exclusive groups of subscribers before making it available to the public. For example, Thomson Reuters created a high-speed data feed for paying subscribers where the Consumer Sentiment Index prepared by the University of Michigan was released two seconds earlier
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    (Javers, 2013c).
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    30This timing difference creates profit opportunities for high-frequency traders (Y. Chang, Liu, Suardi, & Wu, 2014) and might entail an extremely fast price discovery (Hu et al., 2013). However, the CAR graphs in Section 4.2 show that for the strong drift announcements the information enters the market approximately half an hour before the release time.
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    For example, Thomson Reuters created a high-speed data feed for paying subscribers where the Consumer Sentiment Index prepared by the University of Michigan was released two seconds earlier (Javers, 2013c).30This timing difference creates profit opportunities for high-frequency traders (Y. Chang, Liu, Suardi, & Wu, 2014) and might entail an extremely fast price discovery
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    (Hu et al., 2013).
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    However, the CAR graphs in Section 4.2 show that for the strong drift announcements the information enters the market approximately half an hour before the release time. The pre-announcement drift that we uncover is, therefore, not confined to high-frequency trading.
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    Department of Labor (DOL) official responsible for lock-up 30Although Thomson Reuters argued that it had the right to provide tiered-services, the Security Exchange Commission started an investigation. Thomson Reuters suspended the practice following a probe by the New York Attorney General in July of 2013
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    (Javers, 2013a).
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    31The pre-release period is 60 minutes in the Bureau of Economic Analysis announcements and 30 minutes in the Bureau of Labor Statistics, Bureau of Census, Conference Board (until 2013), Employment and Training Association, and National Association of Realtors announcements.
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    We were unable to determine the pre-release period length for the Federal Reserve Board. 32Note that the pre-release variable does not capture leakage that might occur outside of the lock-up, for example, via staff that prepares and disseminates the information or the government officials that receive the information ahead of time
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    (Javers, 2012).
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    Factors that might affect the likelihood of leakage include the number of individuals involved in the release process and the length of time from data collection to release. However, this information is not publicly available and we were unable to obtain it from all organizations. security highlights challenges that new technologies create for preventing premature dissemination from these lock-up
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    However, this information is not publicly available and we were unable to obtain it from all organizations. security highlights challenges that new technologies create for preventing premature dissemination from these lock-up rooms
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    (Fillichio, 2012).
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    News media were allowed to install their own computer equipment in the DOL’s lock-up room without the DOL staff being able to verify what exactly the equipment does (Fillichio, 2012; Hall, 2012). A wire service accidentally transmitted the data during the lock-up period.
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    is not publicly available and we were unable to obtain it from all organizations. security highlights challenges that new technologies create for preventing premature dissemination from these lock-up rooms (Fillichio, 2012). News media were allowed to install their own computer equipment in the DOL’s lock-up room without the DOL staff being able to verify what exactly the equipment does
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    (Fillichio, 2012; Hall, 2012).
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    A wire service accidentally transmitted the data during the lock-up period. Cell phones were supposed to be stored in a designated container but one individual accessed and used his phone during the lock-up (Fillichio, 2012).
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    A wire service accidentally transmitted the data during the lock-up period. Cell phones were supposed to be stored in a designated container but one individual accessed and used his phone during the lock-up
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    (Fillichio, 2012).
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    In addition, although the lock-up rooms are designed for media outlets that are in the journalism business, other entities have exploited the loose definition of what constitutes a media outlet and obtained access to the lock-up rooms.
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    In addition, although the lock-up rooms are designed for media outlets that are in the journalism business, other entities have exploited the loose definition of what constitutes a media outlet and obtained access to the lock-up rooms.
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    Mullins and Patterson (2013)
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    write about the “Need to Know News” outlet. After the DOL realized that this entity was in the business of transmitting data via high-speed connections to financial firms, the DOL removed its access to its lock-up room.
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    After the DOL realized that this entity was in the business of transmitting data via high-speed connections to financial firms, the DOL removed its access to its lock-up room. Attesting to the fact that ensuring a secure pre-release is a formidable task, the DOL has been reported to consider eliminating the lock-up room
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    (Mullins, 2014).
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    In addition, our survey uncovers a third type of release procedures that has not been documented in academic literature. Three announcements are pre-released to journalists electronically.
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    Confidence Index, Pending Home Sales and Industrial Production) 33We also estimate this model controlling for forecastability of the surprise using three variables: publication lag, number of professional forecasters, and standard deviation of individual forecasts. The publication lag might matter if more forecasting effort goes into more up-to-date announcements, given the evidence in
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    Gilbert et al. (2015)
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    that earlier announcements move markets more. A higher average number of professional forecasters might make it more difficult to produce a superior forecast for announcements. The average standard deviation of individual forecasts measures the dispersion of beliefs among professional forecasters.
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    A thorough analysis of individual trader data would be needed to fully examine the leakage question.34 5.1.2 Proprietary Information In addition to information leakage, private information can be created by market participants generating their ownproprietaryinformation by collecting data related to macroeconomic announcements. In the context of company earnings announcements,
    Exact
    Kim and Verrecchia (1997)
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    interpret this pre-announcement information as “private information gathered in anticipation of a public disclosure.” If this proprietary information is never published, it remains a noisy private signal of the official announcement and has similar effects as leakage in Brunnermeier (2005).
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    In the context of company earnings announcements, Kim and Verrecchia (1997) interpret this pre-announcement information as “private information gathered in anticipation of a public disclosure.” If this proprietary information is never published, it remains a noisy private signal of the official announcement and has similar effects as leakage in
    Exact
    Brunnermeier (2005).
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    The nature of proprietary information usually makes it impossible for researchers to verify its existence. However, proprietary data that is released to researchers or the public later provides an opportunity to explore the role of proprietary information in the pre-announcement price drift.
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    The definition of a surprise in equation (2) requires information of market expectations, Em,t−τ[Am,t], to become operational. Section 4 uses the consensus forecast, a common approach in the literature
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    (Balduzzi et al., 2001).
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    However, the calculation of this consensus forecast by Bloomberg is not innocuous: Bloomberg equal-weights the individual forecasts, which is not optimal in general. We, therefore, use the individual forecasts attempting to construct a forecast that outperforms the Bloomberg consensus forecast.36If the surprises are predictable with individual forecasts but most traders rely on the consensus for
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    Nine announcements show significance of the slope coefficient at 10% level.39 The forecast error in predicting the next surprise is then ̃Smt−Pmt. We compare this forecast error with a no-surprise benchmark where the forecast error is based onPmt= 0. Using the Diebold-Mariano test
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    (Diebold & Mariano, 1995; Diebold, 2015),
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    we test the null hypothesisH0:E [ S ̃mt−Pmt ]2 =E [ S ̃mt ]2 against the alternative hypothesisH1: [ S ̃mt−Pmt ]2 < E [ S ̃mt ]2 . E Table A1 in the Appendix shows the results. The improvement over the zero surprise forecast is significant at 10% level for five of the 18 market-moving announcements.
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    not help explain the drift. 39These announcements are Advance Retail Sales, CB Consumer Confidence Index, CPI, Durable Goods Orders, Existing Home Sales, GDP Advance, Industrial Production, Pending Home Sales and PPI. Detailed results are reported in the Internet Appendix B.3. 40We also conducted the same tests using more complicated methods of combining the individual forecasts similar to
    Exact
    Brown et al. (2008) and
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    more advanced econometric techniques such as the complete subset regression of Elliott, Gargano, and Timmermann (2013). The results (available upon request) show that we can improve on the Bloomberg consensus forecast in six announcements but the conclusions are not qualitatively different because the improvements in forecastability of the surprise do not help
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    Forecasting with Internet Activity DataHere, we use internet search engine activity data. This data reflects interest in acquiring information and several recent studies have shown that it is useful for forecasting numerous variables (for example,
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    Choi and Varian (2012)
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    for unemployment claims, consumer confidence and automobile sales, and Da, Engelberg, and Gao (2011) for stock prices). The data is publicly available from Google via the Google Trends service since January 2004.
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    jump on the bandwagon” with informed traders by observing the trading activity and returns before the announcement.41However, it is important to recognize that the markets that we examine are very liquid. The order imbalances we observe before these announcements are sizable but represent only a small fraction of the overall trading activity. For example, 41For example,
    Exact
    Brunnermeier (2005)
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    shows that leakage makes prices before the news announcement more informative. the average trading volume in the 30-minute window before drift announcements is about 177,000 and 62,000 contracts in the E-mini S&P 500 and 10-year Treasury note futures, respectively.
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  49. Start
    93376
    Prefix
    Further improvements in forecasting may be due to resource-intensive legwork creating original proprietary datasets that proxy the data underlying public announcements. 42See, for example,
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    Kyle (1985) and Admati and Pfleiderer (1988)
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    for a theoretical exposition of how informed speculators trade strategically to avoid revealing their information in the price. The small number of market-moving announcements makes it difficult to definitely rule out either information leakage or superior forecasting.
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