The Market’s Response to Unanticipated Events
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Raymond M. Brooks Oregon State University Department of Accounting, Finance, and Information Management 200 Bexell Hall Corvallis, Oregon 97331 541-737-3687
[email protected] Ajay Patel Wake Forest University Babcock Graduate School of Management Winston-Salem, North Carolina 27109 336-758-5575
[email protected] and Tie Su Department of Finance University of Miami P.O. Box 248094 Coral Gables, Florida 33124-6552 (305) 284-1885
[email protected] Original Draft: February 1998 Current Draft: December 1999
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We would like to thank Laura Starks for helpful suggestions, Ron Howren for computer support, and Eric Schuster and Bob Hebert for gathering the sample. Ajay Patel thanks the Babcock Research Fellowship Program for partial support of this project. The usual disclaimer applies.
The Market’s Response to Unanticipated Events Abstract We examine the reaction of prices, volume, spreads, and trading location when firms experience unanticipated events. These events are totally unanticipated by the market in terms of both timing and content. Due to the distinct nature of these news announcements, trading patterns are somewhat different from those documented for scheduled or partially anticipated announcements. We find that the response time is clearly longer than that reported in prior event studies of anticipated events. Moreover, selling pressure, wider spreads and higher volume remain significant for over an hour. In addition, we find an immediate price reaction at the opening call market. However, for events that occur while the market is open, price reaction, the increase in volume and selling pressure, and wider spreads take eight to fourteen minutes to ‘catch up’to those for the overnight events. Therefore, while trading may not be necessary to process new public information it may take time to settle differences of opinion.
The Market’s Response to Unanticipated Events One of the major premises of finance is that markets are efficient. In general, any publicly available information, including macroeconomic information, which might be used to predict stock prices, should quickly be impounded in prices. It is only new information, more specifically, new and unpredictable information, which moves prices. While this argument is well accepted in finance, many studies that examine the market reaction to new information examine announcements that have a predictable component, for example, the timing and/or anticipated amount of earnings. Researchers typically select a proxy for the anticipated portion of the news announcement and then test the market’s reaction to the unanticipated portion of the announcement. The process of separating the anticipated and unanticipated portions of news announcements is critical to conclusions that can be drawn about price changes, speed of adjustment, and trading activities. We avoid this separation problem by looking at what we deem fully unanticipated events. We collect a set of unanticipated events and then examine the equity market’s reaction to these events. Some of these events take place when the market is open, while others take place while the market is closed. For events that occur while the market is open, there is an immediate opportunity to trade on the information. For the other events, there is a period of no trading before the information can be impounded in prices. However, one of the unique features about the equity market is the structure of the opening of the markets at the New York and American stock exchanges. Both markets begin trading with a call auction, and then switch to a continuous trading process. Thus an interesting question arises: How does the opening call market handle unanticipated information differently from the continuous trading market? We partition our sample into events that take place while the market is closed, and while the market is open. We 1
find different speeds of adjustment and trading processes for these partitioned events. This partition also lets us examine whether trading is necessary for the resolution of uncertainty with new public information. Our general findings are that unanticipated events have an immediate impact on prices. What is different from previous studies (Dann, Mayers and Raab (1977), Patell and Wolfson (1984), Jennings and Starks (1985), and Ederington and Lee (1993, 1995)) is the speed of the adjustment. Prior studies have documented that the price reaction takes place within one to fifteen minutes. In our study, the initial price reaction takes over twenty minutes; however, prices tend to reverse over the following two hours. Since the events we examine are negative news events to the firm, the reversal pattern is consistent with earlier findings that the market overreacts to bad news (DeBondt and Thaler (1985, 1987) and Brown, Harlow and Tinic (1988)). In addition, we find evidence of an increase in selling pressure, trading volume, and in quoted dollar spreads following these announcements. In fact, selling pressure and volume remain significantly higher for over two hours. Dollar spreads are significantly wider for over an hour. We also document that for those events that occur while the market is open, the increase in volume, selling pressure and dollar spread takes about eight to ten minutes to "catch-up" to those events that occur while the market is closed. Prices for events that occur during the day, however, take at least fourteen minutes to reach the levels for events that occur while the market is closed. Once changes in volume, selling pressure and prices for daytime events “catch up” to those for overnight events, subsequent changes in price, volume, and trading pressure are similar for at least the next three hours. Only spreads behave differently; the size of the spread remains much wider for those events that take place while the market is open. This pattern continues to exist for at least the next three hours. This extended pattern of wider spreads for daytime events
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suggests an inventory control problem for the specialist. Finally, we find a three-minute delay in reaction to the daytime events. This short delay may be necessary to analyze the impact of new information or clean out stale quotes from the limit order book. Overnight events do not have this delay once trading opens, suggesting that trading may not be necessary to resolve uncertainty about new public information. The remainder of this paper is organized as follows: The next section presents a short literature review. Section II describes the data, sample, and variables examined in the study. Section III outlines the procedures. Section IV discusses the results. The last section presents a short summary and conclusion.
I. Literature Review Inquiry of financial market efficiency and the speed with which markets adjust to new information date back to Fama’s (1965) introduction of an efficient market, and to the event study methodology by Fama, Fisher, Jensen and Roll (1969) (henceforth FFJR). Empirical research since FFJR has amply documented that the stock market reacts only to unanticipated information. However, as the surveys of Fama (1970, 1991) and LeRoy (1989) indicate, the stock market overreacts to new information [DeBondt and Thaler (1985, 1987) and Brown, Harlow and Tinic (1988)], underreacts to earnings announcements [Bernard and Thomas (1990)], and is too volatile given economic fundamentals [Shiller (1981)]. Therefore, while academics generally agree that markets are fairly efficient, the debate on market efficiency is kept alive by the discovery of market anomalies. Research into how quickly markets adjust to new information using intraday data dates back to at least Dann, Mayers and Raab (1977) who examine the market’s reaction to
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announcements of block trades. They find that a trader would have to react within five minutes of the announcement to earn a positive return, and that transaction prices adjust completely fifteen minutes after block trades. Several studies have examined the stock market’s response to dividend and earnings announcements. Patell and Wolfson (1984) and Jennings and Starks (1985) find that the ability to earn excess returns lasts no longer than ten to fifteen minutes. However, volatility remains high for several hours following the announcement. Greene and Watts (1996) examine the market’s response to earnings announcements made during trading and nontrading hours on the NYSE and NASDAQ. Their results indicate that information is impounded in prices differently on the two exchanges. Specifically, for earnings announcements during nontrading hours, the first postannouncement trade (i.e., the opening trade) on the NYSE impounds most of the price response. In contrast, the price response is spread evenly over several of the initial post-announcement trades following announcements during trading hours. Greene and Watts attribute the speed of adjustment at the opening trade on NYSE following announcements during nontrading hours to 1
the opening procedure used by specialists to determine the opening price. On the NASDAQ, however, the first post-announcement trade impounds most of the price response regardless of whether the announcement occurs during trading or nontrading hours. Moreover, the price adjustment at the first post-announcement trade is greater on the NASDAQ than on the NYSE for both sets of events. Cao, Ghysels and Hatheway (2000) examine NASDAQ market makers’ activities during the pre-opening period. They find that price discovery on the NASDAQ during the pre-opening 1
In footnote 7, Greene and Watts (1996) explain how specialists announce trial clearing prices before settling on an opening price. Based on Greene and Watts’findings, the use of these “indications” allows for price discovery in a manner similar to the pre-opening process on NASDAQ.
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period is conducted via price signaling as opposed to the auction process used at the opening on the NYSE, or the continuous market used during the trading day. Moreover, they also shed light on the faster speed of adjustment to overnight news announcements on the NASDAQ, when compared to the NYSE. Their results indicate that the pre-opening period facilitates greater price discovery than the call auction process on the NYSE resulting in faster price adjustment for NASDAQ stocks at the open. Ederington and Lee (1993, 1995) examine the impact of scheduled macroeconomic news releases on the interest rate and foreign exchange markets. They find that, in contrast to the stock market, prices react within ten seconds and that the major price adjustment occurs within one minute of the scheduled news releases. Their findings suggest that the interest rate and foreign exchange markets react much faster to public announcements than the stock market. More recently, Fleming and Remolona (1999) look at the impact of scheduled macroeconomic news releases on the U.S. Treasury Market. They find that the arrival of public information results in a two-stage adjustment process. In the brief first stage, prices react immediately, trading volume drops, and bid-ask spreads widen. In the longer second stage, volume surges, volatility persists, while spreads remain wide. Their first-stage results are consistent with the market’s response to the arrival of public information, while the second-stage findings indicate disagreement among investors as to the information content of the public announcement. Liquidity and volatility return to their normal levels once the market reaches a consensus. Even though researchers have generally been interested in the speed and magnitude of price adjustment to public announcements, some event studies have used abnormal returns to signify the arrival of new information. These studies begin with a change in abnormal returns and then investigate the market’s efficiency by examining reversing trends in the market, the
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overreaction hypothesis. Bremer and Sweeny (1991) find that following large price decreases, prices rebound in subsequent periods, but following large price increases, prices remain at the new level. The implied conclusion in this study is that the market does not efficiently handle bad news. While previous studies of stock, interest rate and foreign exchange markets indicate that information is incorporated in prices fairly rapidly; each of the studies examines an event that is partially anticipated by the market. For example, the market largely anticipates the timing of earnings and dividend announcements. Moreover, the market also partially anticipates the amount of the earnings or dividend announcement. Similarly, both Ederington and Lee (1995), and Fleming and Remolona (1999) examine the market’s reaction to scheduled announcements of macroeconomic news. The macroeconomic news announcements that they examine are also partially anticipated by the market. Since the events are partially anticipated, spreads, volume and volatility may, and probably do, change during the pre-announcement period in anticipation of the event. In fact, Fleming and Remolona document a widening of the spread prior to the release of 2
the macroeconomic news. Moreover, the market’s adjustment to the news release should be fairly rapid for these partially anticipated events as traders plan strategies for different potential scenarios. Then, as one of the scenarios is revealed at the announcement, traders can quickly implement the trading strategy that corresponds to the revealed scenario. Therefore markets can, and apparently do, react quickly to the information released at these anticipated events. In contrast to previous studies that examine partially anticipated events, this study focuses on the market’s adjustment to fully unanticipated firm-specific events. By focusing on unanticipated events, this paper provides new and unique evidence on how financial markets process information, and on the trading activity following the arrival of unanticipated public
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Krinsky and Lee (1996) document a widening of the spread prior to earnings announcements.
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information. Moreover, by comparing the adjustment process for unanticipated announcements that occur when the market is open to those announcements that occur while the market is closed, we provide valuable information about the speed of adjustment and necessity of trading for the resolution of uncertainty.
II. Data, Sample, and Variables Our study utilizes the intraday transaction data on the 1989 through 1992 NYSE and AMEX Trades and Quotes Transaction File prepared by the Institute for the Study of Security Markets (ISSM). Intraday data from the ISSM tape include time stamped transactions, bid quotes, and ask quotes. In addition, the database contains the price and size of each transaction, the support for a quote, opening prices, and closing prices. We use intraday data to calculate returns, classify trades (buy or sell), determine trade size, and measure bid-ask spreads. A sample of event firms is constructed by searching Lexis/Nexis using various key words that highlight unanticipated events. Once a news announcement is identified as a potential “unanticipated” event, the exact time of the event is determined from the Dow Jones Newswire. All firms that have unanticipated events, and have intraday trading data on the ISSM tapes, qualify for the event sample. We examine twenty-two fully unanticipated events ranging from the Exxon Valdez running aground on Blight Reef in Prince William Sound, to an explosion at a Texaco refinery in Los Angeles that injured more than sixteen employees. The events are negative news in that they are unpredictable accidents that occur to a specific firm. Table I contains the list of events examined in this study. Insert Table I About Here
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We also look at the stock market’s reaction for competing firms to see if the unanticipated event has a contagion effect for competitors. A competing firm is from the same industry and is of similar size to its corresponding event firm. Finally, to control for the impact that macroeconomic news announcements may have on the trading process for our sample firms and their competitors, we also examine firms that are of similar size, but outside the industry of our sample firms. The competing firms are used to examine any contagion effects, while the control firms are used to benchmark trading variables not subject to the firm-specific event. In addition, each firm selected must have intraday trading data on the ISSM tapes. Therefore we have an additional twenty-two competing firms, and an additional twenty-two control firms for the study. In this study we use five variables to examine the trading process following announcements of unanticipated events. The first variable is stock price, which includes transaction prices, quoted bid-ask prices, and bid-ask spread midpoints. The second variable is spread in quoted prices, both in relative and nominal terms. The third variable is trading volume measured in total trading dollars and number of shares traded. The fourth variable is stock return volatility, which is computed using percent change in bid-ask spread midpoints. We measure volatility over each ten-minute window around event announcements. Finally, we use Keim’s (1989) trading location measure, which is defined as the transaction price minus the bid price divided by the current bid-ask spread. Trades at the bid receive a value of zero, while trades at the ask receive a value of one. On average, the expected value of the trading location variable is 0.5, indicating that half the trades are above, while half are below the bidask spread midpoint. Selling pressure is indicated when the average trading location is below 0.5, while buying pressure is indicated when the average trading location is above 0.5.
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III. Procedures The event day and time for an unanticipated event is selected based on the time stamp from the Dow Jones Newswire. We then calculate the pre-event averages for the trading price, volume, nominal spread, relative spread, and trading location, where the pre-event period is ten days long, beginning eleven days before the event and ending the day prior to the event. The preevent averages are computed on an hourly basis over the ten-day pre-event period. Table II presents the average values for these measurement variables during the pre-event period for the event firms, the set of competing firms and the set of control firms. Insert Table II About Here
For the pre-event period, the average value of each of the variables examined is similar across the three sets of firms. Moreover, the trading location is near 0.5 for all three samples indicating no buying or selling pressure during the pre-event period. Since prices, volume, and spreads are similar for the three sets of firms, the data suggest that the market does not anticipate the event. If the event were partially anticipated by the market, spreads for the event firm would have widened during the pre-event period relative to the competing and control firms. Next, we calculate the same variables over a full trading day following the event. We examine the variables using 15-minute, 30-minute, 60-minute, 90-minute and 120-minute windows. We also examine minute-by-minute changes in the four variables. Finally we separate the events into those that occur while the market is open, and those that occur while the market is closed.
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IV. Results In contrast to the findings of studies of partially anticipated events, we find that the market does not react "quickly" to announcements of unanticipated events. Table III provides changes at specific time intervals of 15, 30, 60, 90 and 120 minutes, with significance testing for each time 3
interval.
Insert Table III About Here The control firms do not experience a significant change in any of the variables. For the event firms, however, trading prices fall by an average of about eighty cents per share during the first fifteen minutes following the announcement. Prices continue to move downward for another 4
seven minutes, and then start to rebound. After thirty minutes of trading, prices are lower than the pre-event price by nearly seventy cents. By the end of the first hour, prices are still down by about twenty-nine cents per share for event firms. After another hour of trading, prices are essentially back to their pre-event level (eight cents below their pre-event average). Prices continue a slight upward trend for the day, finishing about twelve cents per share higher for the entire trading day. For competing firms, prices fall by about fifteen cents a share during the first fifteen minutes. Then, prices reverse, a positive spillover effect, and drift upward for the next 105 minutes, reaching as high as sixty cents above the pre-event average. The price increase does partially reverse, before settling at about forty-eight cents above the pre-event level at the twohour mark. Prices remain higher for the entire day and continue to drift upward finishing the first 3
The 15-minute results are tested against the pre-event averages computed over the 10-day period using 15minute intervals. Similarly, the 30-, 60-, 90- and 120-minute results are tested against the pre-event averages computed using the appropriate time interval. 4 These results are based on the minute-by-minute examination of the data over the entire trading day. These
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trading day sixty cents per share higher, on average. The price changes are not statistically different from the pre-event level after one-hour of trading, but appear to be economically significant over the first day of trading. The first 30 minutes of trading clearly show both an immediate (negative) reaction to the event and a (positive) spillover effect to competing firms. During the next 90 minutes, event firms experience a price reversal, while prices for competing firms continue to experience an upward drift. Our results are consistent with the overreaction results in Bremer and Sweeny (1991), and Brown, Harlow and Tinic (1988), but price recovery in our study takes longer. Volume, while significantly higher during the first hour of trading for both event firms and competing firms, begins to return to the pre-event level for both event firms and competing firms during the second hour of trading. The control firms do not experience a significant change in volume over the two-hour window, or for the entire trading day. The dollar bid-ask spread increases significantly for event firms over the first hour of trading, and then starts to reverse. However, the spread remains wider throughout the first two hours of trading. The competing firms experience a small increase in dollar spreads, but the changes are insignificant. The control firms, again, experience essentially no changes in dollar spreads. Event firms experience a significant increase in selling pressure as the trading location 5
measure decreases from 0.48 to 0.32 during the first thirty minutes of trading. Concurrently, competing firms experience a slight rise in buying pressure as the trading location measure increases from 0.51 to 0.54. The control firms, however, show no significant change in trading location during the first hour, or through out the first trading day. results have not been presented in the paper to conserve space, but are available from the authors upon request. 5 The estimate of 0.32 can be computed through data in Tables 2 and 3. Table II indicates that the pre-event average trading location for event firms is 0.48, while Table III indicates that the change in trading location for these firms during the first thirty minutes of trading is –0.16. This suggests that the trading location at the end of
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The second hour of trading provides additional evidence on trading pressure and the momentum of trading. For event firms, selling pressure decreases, as the trading location measure is 0.40, moving back towards the pre-event average of 0.48. It returns to normal by the end of the 6
third hour (0.49). For competing firms, buying pressure stays high for the first three hours, but returns to normal by the fourth trading hour. Finally, as one would expect, the control firms exhibit no distinctive pattern of buying or selling pressure throughout the trading day. The small sample size makes it possible to look at each event firm's price reaction over the first two hours. Table IV presents the percentage price reactions for each of the twenty-two event firms at fifteen-minute intervals. Insert Table IV About Here The firm with the largest initial price reaction is ARCO, with a negative price reaction of 7
2.50 percent in the first fifteen minutes. The firm with the smallest negative price reaction is McClatchy Newspapers, with a 0.79 percent reaction in the first fifteen minutes. All twenty-two firms have a negative price reaction over the first fifteen minutes, and the first thirty minutes of trading. It is not until an hour of trading is completed do firms reverse the early trading trends. At the sixty-minute mark, the percentage price change for fifteen of the twenty-two firms is still negative. Interestingly, it is not the firms with the smallest initial reactions that are the first to reverse.
the first thirty minutes of trading is 0.32 (0.48 – 0.16). 6 These results are based on an examination of the trading process during the six hours following the announcement of the unanticipated event. While these results have not been provided in this paper, they are available from the authors upon request. 7 While the results presented earlier reflect the dollar change in prices (Table III), the results in Table IV reflect the percentage change in price following the event.
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After two hours of trading, thirteen firms still have a negative price reaction, and nine firms have had a complete reversal and are trading with a positive price change. However, of the thirteen with negative price reactions after two hours, six had a complete reversal prior to the two-hour price change measurement. Therefore, only seven firms had negative prices across the entire time window. The size of the initial negative price reaction does not predict which firms will have negative prices across the two-hour window. Therefore, the immediate price reaction is not an indicator of how long it will take for the price to reverse, or even if the price will reverse. In fact, ARCO with the largest initial negative price reaction reverses after ninety minutes of trading, while McClatchy Newspapers with the smallest initial reaction does not reverse over the two-hour window. Next, we partitioned the events into two groups; initial announcements of an event that crossed the newswire while the markets were open (NYSE and AMEX), and those that crossed the newswire while the markets were closed. Figures 1 through 4 show minute-by-minute changes in price, spreads, trading location, and volume for the event sample beginning five minutes prior to and ending twenty minutes following the unanticipated announcement. While the small sample severely restricts many statistical tests, graphical representation of the results appears interesting nonetheless. Insert Figures 1 - 4 About Here Price changes are immediate for the overnight events (events while the market is closed), and suggest that it does not take trading to impact prices. This finding is consistent with the price adjustment in Greene and Watts (1996) following earnings announcements during nontrading
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hours. However, the daytime events (events while the market is open) experience a short threeminute delay before prices move. Prices, following daytime events, take nearly fifteen minutes to have the same price impact as the overnight events. It is important to note that “minute –1” is different for the two partitioned samples. The overnight event “minute –1” is the last minute of trading for the prior day, while the daytime event “minute –1” is one minute before the announcement during the trading day. One explanation for the short three-minute price reaction delay is trading against stale quotes. A large majority of the trades in the first three minutes following the event are small volume trades transacted at the bid quote. Applying Occam's razor, the simplest explanation would be that market-sell orders are being matched to limit orders that remain on the limit-order book immediately following the event. Therefore, the initial three-minute delay has minimal price movement as the first trades are simply removing stale bid quotes. Once the stale quotes are removed, transactions start to move prices. Volume immediately jumps up for the overnight events. But again, it takes time to build up for the daytime events. After about eight to ten minutes of trading, the daytime events show similar trading volume as the overnight events. Selling pressure, as measured by the trading location variable, behaves almost identically to the volume variable. After about eight to ten minutes, the location variable is similar for both daytime and overnight samples. The fourth measure is the bid-ask spread. For the overnight events, the bid-ask spread jumps in the first minute and remains at this same level over the next twenty minutes of trading. The daytime events, again, take about eight to ten minutes to reach this same level, but continue to widen over the next five to six minutes. The spreads remain wider for the next two hours.
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Why do spreads remain wider for daytime events? One of the features of the equity markets is the difference between the opening trade and continuous trading throughout the day. At the opening trade, the specialist examines the overnight order flow, the current limit order book, and opens the stock at a price that incorporates the order information. The specialist may, or may not, take an inventory position to increase the number of orders crossed at the opening, based on the order flow. Thus, when an event takes place while the market is closed, the specialist can utilize the order flow to pick the opening price and, simultaneously, his inventory position. When a daytime event takes place in the continuous market, the specialist is acting in a different trading environment. In both cases, the specialist is charged with maintaining an orderly market, but now may need to take the opposite side of a market order to complete this charge. Thus, as selling pressure and volume increases, the specialist is probably participating in more and more trades to maintain an orderly market. This is consistent with Madhavan and Sofianos (1998) who find that the specialist participates more actively when bid-ask spreads are wide, and previous price movements are volatile. Knowing that his posted bid price will probably be the highest bid, the specialist increases the spread (lowers his bid) to minimize his long and growing inventory position. In addition, it will take the specialist longer to unwind this long inventory position throughout the trading day, so he needs to maintain a wider bid-ask spread. Hence, the spread is wider, and stays wider longer for daytime events. A wider spread is a reflection of the 8
inventory cost component of the bid-ask spread.
Ten-minute volatility changes for the partitioned event firms are presented in Figure 5. Volatility increases in the first ten minutes by a magnitude of 50 percent. The daytime event 8
The NYSE does allow the specialist to request a trading halt for a firm with a large order imbalance, or for a firm with an important announcement. However, we did not detect any trading halts for our sample of events.
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volatility increase has a slight lag in adjustment over the first twenty minutes, but is nearly the same for the next three hours. This increased volatility pattern is another indication of the price uncertainty faced by the specialist. High volatility following the events indicates that there is uncertainty about the impact of the events on the firms, or inventory control problems for the specialist, or both. One potential reaction by the specialist is to widen the bid-ask spread during this period of greater uncertainty. The slight lag for daytime events (over the first twenty minutes) is consistent with the wider spreads for daytime events. A specialist has time to evaluate an event that took place overnight prior to opening the market. With the overnight order flow, there is less uncertainty about the impact of the event on prices and spreads when trading begins. Insert Figure 5 About Here Finally, overnight events do not have this delay in price, volume, selling pressure, or spreads, once trading opens suggesting that trading may not be necessary to resolve uncertainty about new public information. Cao, Ghysels, and Hatheway (2000) also document price discovery without trading. They show how quote behavior in the pre-opening of the top 50 NASDAQ stocks influences price changes between the close and opening prices. For announcements of unanticipated events while the market is closed, price discovery on NYSE takes place via overnight order flow before the market opens, and the specialist announcing trial clearing prices (“indications”) prior to settling on an opening price, instead of any formal pre-opening price quotations.
V. Summary and Conclusion We examine twenty-two fully unanticipated news events and measure the equity market’s reaction to these events using prices, volume, trading location, spreads, and volatility. We find
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three interesting results. First, the reaction to unanticipated information is not impounded into prices as quickly as suggested by previous research on scheduled events. However, the market's response to unanticipated events that take place while the market is closed suggests that trading is not necessary for prices to react. When the market has time to digest the information prior to the opening of the trading day, the reaction is immediate in price, volume, selling pressure, and bidask spreads. When the news comes to the market while the market is open, the market reacts slower than previous research would indicate on price, volume, selling pressure, and bid-ask spreads. Second, consistent with partially anticipated events, the market overreacts to bad news, but not good news. The initial price reaction drifts downward for at least fifteen to twenty minutes but, on average, completely reverses over the next hour and a half for event firms. The competing firms apparently receive a positive spillover from the bad news, but this price increase is not reversed in later trading. Finally, daytime events induce wider bid-ask spreads, and longer duration of wider spreads when compared to overnight events. We suspect that this is due to the inventory cost faced by the specialist for managing an orderly market across daytime events. Another interesting finding is an apparent trading pattern that locks in a loss. We find that selling continues well past the initial negative price reaction, and throughout the price reversal phase. The persistent selling pressure should continue to lower prices. We also find that volume remains significantly higher during the price reversal stage after the first twenty-minute negative reaction. This result provides an interesting conclusion for efficient markets. It seems many traders are closing and locking the proverbial barn door after the horses are gone and before the horses choose to wander back into the barn, thus selling to lock in a loss.
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References Bernard, V. J. and J. Thomas, 1990, “Evidence That Stock Prices Do Not Fully Reflect the Implications of Current Earnings for Future Earnings,” Journal of Accounting and Economics 13, 305-340. Bremer, M., and R. Sweeny, 1991, "The Reversal of Large Stock-Price Decreases," Journal of Finance 46, 747-754. Brown, K., W. Harlow and S. Tinic, 1988, "Risk Aversion, Uncertain Information and Market Efficiency," Journal of Financial Economics 14, 6-13. Cao, C., E. Ghysels, and F. Hatheway, 2000, "Price Discovery without Trading: Evidence from the Nasdaq Pre-opening," Forthcoming Journal of Finance. Dann, L., D. Mayers, and R. Raab, 1977, “Trading Rules, Large Blocks and the Speed of Adjustment,” Journal of Financial Economics (January), 3-22. DeBondt, W. F. M. and R. H. Thaler, 1985, “Does the Stock Market Overreact?” Journal of Finance 40, 793-805. DeBondt, W. F. M. and R. H. Thaler, 1987, “Further Evidence on Investor Overreaction and Stock Market Seasonality,” Journal of Finance 42, 557-581. Ederington, L. H. and J. H. Lee, 1993, “How Markets Process Information: News Releases and Volatility,” Journal of Finance 48, 1161-1191. Ederington, L. H. and J. H. Lee, 1995, “The Short-Run Dynamics of the Price Adjustment to New Information,” Journal of Financial and Quantitative Analysis 30, 117-134. Fama, E. F., 1965, “The Behavior of Stock Market Prices,” Journal of Business 38 (January), 34-105. Fama, E. F., 1970, “Efficient Capital Markets: A Review of Theory and Empirical Work,” Journal of Finance 25, 383-417. Fama, E. F., 1991, “Efficient Capital Markets: II,” Journal of Finance 46, 1575-1617. Fama, E. F., L. Fisher, M. C. Jensen and R. Roll, 1969, “The Adjustment of Stock Prices to New Information,” International Economic Review 10, 1-21. Fleming, M. and E. Remolona, 1999, “Price Formation and Liquidity in the U.S. Treasury Market: The Response to Public Information,” Journal of Finance 54, 1901-1927. Greene, J. T., and S. G. Watts, 1996, “Price Discovery on the NYSE and the NASDAQ: The Case of Overnight and Daytime News Releases,” Financial Management 25, 19-42.
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Jennings, R. and L. Starks, 1985, “Information Content and the Speed of Stock Price Adjustment,” Journal of Accounting Research 23, 336-350. Keim D. B., 1989, “Trading Patterns, Bid-Ask Spreads, and Estimated Security Returns: The Case of Common Stock at Calendar Turning Points,” Journal of Financial Economics 25, 75-98. Krinsky, I. And J. Lee, 1996, “Earnings Announcements and the Components of the Bid-Ask Spread,” Journal of Finance 51, 1523-1535. LeRoy, S. F., 1989, “Efficient Capital Markets and Martingales,” Journal of Economic Literature 27, 1583-1621. Madhavan, A. and G. Sofianos, 1998, "An Empirical Analysis of NYSE Specialist Trading," Journal of Financial Economics 48, 189-210. Patell, J. and M. Wolfson, 1984, "The Intraday Speed of Adjustment to Stock Price Earnings and Dividend Announcements," Journal of Financial Economics 13, 223-252. Shiller, R. J., 1981, “Do Stock Prices Move Too Much to Be Justified by Subsequent Changes in Dividends,” American Economic Review 71, 121-136.
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Table I Unanticipated Firm Events, Time of Event, Competing Firm and Control Firm The sample of event firms is constructed by searching Lexis/Nexis for unanticipated events using various key words, while the exact time of the event is obtained from the Dow Jones Newswire. The time of the event is based on Eastern Standard Time. A competing firm is from the same industry and is of similar size to its corresponding event firm. The control firm is of similar size, but outside the industry of the event firm. 1
Event Firm Exxon Chevron McClatchy Newspapers UAL Quantum Chemical USAir Phillips Petroleum ARCO Gillette UAL USAir Dominion Resources Conoco People's Natural Gas 2 Amoco 2 Enron 2 Union Carbide Burlington Northern Texaco Panhandle Eastern Pipeline TimeWarner McDonnell Douglas
Date 3/24/89 4/10/89 4/16/89 7/19/89 9/12/89 9/21/89 10/23/89 7/5/90 1/25/91 3/4/91 2/2/91 8/27/91 9/4/91 1/17/92 4/5/92 4/5/92 4/5/92 6/30/92 10/8/92 10/9/92 12/20/92 12/21/92
Time Competitor 5:20 a.m. Chevron 5:57 p.m. Amoco 7:33 a.m. Lee Enterprises 5:26 p.m. Delta 10:06 a.m. Rohm Haas 7:32 a.m. Delta 2:38 p.m. Unocal 7:31 a.m. Hanson PLC 3:17 p.m. Warner Lambert 9:00 a.m. Delta 10:20 a.m. Delta 3:50 p.m. Virginia Electric 9:03 a.m. Mobil 6:11 p.m. Questar 8:56 a.m. Chevron 8:56 a.m. Vastar 8:56 a.m. Dow Chemical 11:34 a.m. Consolidated Rail 8:44 a.m. Amoco 12:22 p.m. El Paso Nat. Gas 7:05 a.m. Disney 7:30 a.m. Boeing
1
Control IBM AT&T Harvard Industries Weyerhaeuser Quaker Oats Owens Illinois Deere & Co. Harris Corp. Dial Corp. Sun Co. Capital Cities ABC ALCOA Travelers Insurance Payless Cashways BellSouth Reebok American Stores Pitney Bowes NYNEX National Western Life Ins. Golden West Financial USF&G
Two firms, U.S. Air and United Airlines, have more than one event. One event impacted three firms. A fire and explosion on April 5, 1992 impacted refineries for Amoco, Enron and Union Carbide that had common boundaries for their facilities. 2
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Table II Pre-event Averages for Event, Competitor, and Control Firms The sample of event firms is constructed by searching Lexis/Nexis for unanticipated events. A competing firm is from the same industry and is of similar size to its corresponding event firm. The control firm is of similar size, but outside the industry of the event firm. Price is defined as the average transactions price on an hourly basis; Volume is measured as the average number of shares traded on an hourly basis; Location, based on Keim (1989), is defined as the transaction price minus the bid price standardized by the current bid-ask spread, and is also computed on an hourly basis; the Dollar Spread is the average nominal spread calculated using all quotes available on an hourly basis; Relative Spread is the average of the nominal spread standardized by the most recent transaction price. The pre-event period is defined as the ten days beginning eleven days before and ending one day prior to the unanticipated event. The pre-event averages are the average hourly estimates for the variables under study over the pre-event period.
Pre-Event Average (Standard Deviation) Variable
Event Firms
Competitor Firms
Control Firms
Price
$45.66 ($33.15)
$47.86 ($20.17)
$46.00 ($26.12)
Volume
30,322 (25,758)
46,392 (55,917)
49,968 (54,970)
0.48 (0.20)
0.51 (0.17)
0.50 (0.16)
$0.29 ($0.13)
$0.31 ($0.14)
$0.30 ($0.13)
0.85 (0.64)
0.80 (0.58)
0.86 (0.49)
Location
Dollar Spread
Relative Spread
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Table III Changes in Price, Volume, Location and Spreads over the First Two Hours of Trading Following the Announcement of an Unanticipated Event Price is defined as the average transactions price; Volume is measured as the average number of shared traded; Location, based on Keim (1989), is defined as the transaction price minus the bid price standardized by the current bid-ask spread; the $ Spread is the average nominal spread calculated using all quotes available; % Spread is the average nominal spread standardized by the most recent transaction price. Average values for these variables are computed over 15-, 30-, 60, 90- and 120minute intervals following the announcement of the unanticipated event. These average values are compared to that over a similar time interval during the pre-event period defined as the ten days beginning eleven days before and ending one day prior to the unanticipated event. The change in price over the first 15 minutes following the event is defined as the average value over the first 15 minutes following the event minus the average price during the pre-event period over each 15-minute interval. Changes for the 30-, 60-, 90- and 120-minute intervals are computed similarly using the appropriate time interval.
Variable Sample
Average Changes in Variables 15 Min.
30 Min.
60 Min.
90 Min.
120 Min.
-0.82** -0.15 0.05
-0.69* 0.30 0.04
-0.29 0.28 -0.12
-0.18 0.48 -0.19
-0.08 0.05 0.15
1700*** 1936*** 700*
1703*** 2037*** 559
1317*** 1915*** 740*
624** 1039** -455
355* 427* 8
-0.14** 0.03 0.03
-0.16** 0.03 0.03
-0.07* 0.03 0.01
-0.09* 0.05 -0.02
-0.12* 0.05 0.01
0.14** 0.06 0.03
0.08** 0.03 0.04
0.09** 0.03 -0.02
0.02 0.07* -0.04
0.06 -0.01 0.01
0.07 -0.02 0.05
0.11 -0.02 0.04
0.08 -0.04 -0.09
0.05 0.00 -0.03
0.20 -0.09 0.00
Price Event Competitor Control
Volume Event Competitor Control
Location Event Competitor Control
$ Spread Event Competitor Control
% Spread Event Competitor Control
*, **, *** indicate significance at the 0.10, 0.05, and 0.01 levels, respectively. 22
Table IV Percent Price Change by Firm at Fifteen Minutes, Thirty Minutes, Sixty Minutes, Ninety Minutes, and One Hundred and Twenty Minutes The sample of event firms is constructed by searching Lexis/Nexis for unanticipated events, while the exact time of the event is obtained from the Dow Jones Newswire. Open indicates that the unanticipated announcement occurred while the market was open, while Closed indicates that the announcement occurred while the market was closed for trading. The percentage price changes are computed relative to the last trade prior to the news announcement. The firms are listed in descending order based on the magnitude of the percentage price change over the first 15 minutes of trading following the unanticipated announcement. Firm ARCO Burlington McDonnell Amoco U.S. Air People's Panhandle Quantum Exxon Enron Chevron UAL U.S. Air Gillette Dominion UAL Texaco Phillips TimeWarner Conoco McClatchy Average
Open/Closed Closed Open Closed Closed Closed Closed Closed Open Closed Closed Closed Closed Open Open Open Open Closed Open Closed Open Closed
15 Minutes -2.50 -2.43 -2.40 -2.27 -2.11 -2.04 -1.99 -1.86 -1.86 -1.78 -1.65 -1.60 -1.48 -1.36 -1.35 -1.31 -1.18 -1.14 -1.14 -0.82 -0.79
30 Minutes -0.70 -0.62 -2.37 -2.01 -1.05 -2.22 -0.76 -1.40 -2.10 -1.67 -1.06 -0.88 -1.54 -1.11 -1.35 -0.84 -1.44 -0.68 -2.12 -2.08 -0.50
60 Minutes -0.71 0.40 -1.45 -0.10 -0.98 -0.11 -1.59 0.06 -1.29 -1.44 -0.40 -1.07 -1.12 -0.05 0.37 0.30 -0.96 0.28 -1.38 0.26 -0.97
90 Minutes 0.06 0.36 -0.17 -1.19 0.06 -1.18 0.31 -0.28 -1.06 0.52 -0.93 -1.13 -0.15 -0.55 -1.09 0.61 -0.02 0.67 -0.91 -0.31 -0.78
120 Minutes -0.63 -1.04 -1.04 0.01 0.78 0.40 0.13 0.54 -0.95 -0.16 -0.68 -0.04 0.09 -0.12 -0.10 -0.92 0.62 0.21 -0.24 -0.19 -0.52
-1.67
-1.38
-0.60
-0.37
-0.16
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Figure 1 Price Change by Minute for Event Firms.
Price Changes ($)
Following the event, the ‘price’ is defined as the bid-ask spread midpoint. An average ‘price’ is computed for each minute beginning 5 minutes before the event, ending 20 minutes following the event. The change in price is the average price during each minute surrounding the event less the average price during the pre-event period. The average price during the pre-event period is also computed on a minute-by-minute basis. Overnight events occur while the market is closed, while daytime events occur while the market is open.
0.5 0 -0.5 -1 -5
0
5
10
15
20
Event Time in Minutes Overnight Events
Daytime Events
Figure 2 Volume Changes by Minute for Event Firms
Volume Changes
Average volume is computed for each minute beginning 5 minutes before the event, ending 20 minutes following the event. The change in volume is average volume during each minute surrounding the event less average volume during the pre-event period. The average volume during the pre-event period is computed on a minute-by-minute basis. Overnight events occur while the market is closed, while daytime events occur while the market is open.
2500 2000 1500 1000 500 0 -500 -1000 -5
0
5
10
15
Event Time in Minutes
Overnight Events
24
Daytime Events
20
Figure 3 Trading Location Changes by Minute for Event Firms
Location Changes
Keim’s (1989) trading location measure is defined as the transaction price minus the bid price divided by the current spread. Following the event, the bid-ask spread midpoint is used instead of the transaction price. The average trading location measure is computed for each minute beginning 5 minutes before the event, ending 20 minutes following the event. Change in location is the average location during each minute surrounding the event less the average location during the pre-event period. The average location during the pre-event period is computed on a minute-by-minute basis. Overnight events occur while the market is closed, while daytime events occur while the market is open.
0.1 0.05 0 -0.05 -0.1 -0.15 -0.2 -5
0
5
10
15
20
Event Time in Minutes Overnight Events
Daytime Events
Figure 4 Spread Changes by Minute for Event Firms
Spread Changes ($)
The average bid-ask spread is computed for each minute beginning 5 minutes before the event, ending 20 minutes following the event. The change in spread is the average spread during each minute surrounding the event less the average spread during the pre-event period. The average spread during the pre-event period is also computed on a minute-by-minute basis. Overnight events occur while the market is closed, while daytime events occur while the market is open.
0.3 0.2 0.1 0 -0.1 -5
0
5
10
15
Event Time in Minutes Overnight Events
25
Daytime Events
20
Figure 5 Trading Volatility by Ten-Minute Intervals for Event Firms The sample of event firms is constructed by searching Lexis/Nexis for unanticipated events, while the exact time of the event is obtained from the Dow Jones Newswire. Volatility is defined as the standard deviation of returns during each ten-minute window. Returns are computed during each ten-minute window using the bid-ask spread midpoint.
0.6
0.5
0.4
0.3
0.2 -6
0
6
12
10-Minute Time Intervals Night
26
Day
18
