Đang tải... (xem toàn văn)
... price and/ or volume reaction is affected by the timing of the earnings announcement, such as the day of the week of the announcement (Dellavigna and Pollet 2009) or the time of day of the announcement. .. on the relative summer absence of noise traders and its effect on earnings announcement price reactions and earnings response coefficients (ERC’s), or the market price reaction to a unit of earnings. .. primarily on the characteristics of the audience of investors for the announcement The differential beliefs and behavior of these investors can help dictate the nature of the market response to the announcement
THE FLORIDA STATE UNIVERSITY COLLEGE OF BUSINESS THE EFFECT OF THE SUMMER DOLDRUMS ON EARNINGS ANNOUNCEMENT RETURNS AND ERC’S By GREGORY B. GAYNOR A Dissertation submitted to the Department of Accounting in partial fulfillment of the requirements for the degree of Doctor of Philosophy Degree Awarded: Fall Semester, 2011 UMI Number: 3502846 All rights reserved INFORMATION TO ALL USERS The quality of this reproduction is dependent on the quality of the copy submitted. In the unlikely event that the author did not send a complete manuscript and there are missing pages, these will be noted. Also, if material had to be removed, a note will indicate the deletion. UMI 3502846 Copyright 2012 by ProQuest LLC. All rights reserved. This edition of the work is protected against unauthorized copying under Title 17, United States Code. ProQuest LLC. 789 East Eisenhower Parkway P.O. Box 1346 Ann Arbor, MI 48106 - 1346 Gregory B. Gaynor defended this dissertation on September 15, 2011. The members of the supervisory committee were: Richard Morton Professor Directing Dissertation Thomas Zuehlke University Representative Bruce Billings Committee Member Tim Zhang Committee Member The Graduate School has verified and approved the above-named committee members, and certifies that the dissertation has been approved in accordance with university requirements. ii ACKNOWLEDGEMENTS I would like to acknowledge Rick Morton (chair) for his extensive help throughout this process, as well as my committee members for their helpful comments and guidance. All errors are my own. iii TABLE OF CONTENTS LIST OF TABLES................................... Error! Bookmark not defined. ABSTRACT................................................................... vi 1. INTRODUCTION............................................................ 1 2. BACKGROUND.............................................................. 6 3. 4. 5. 2.1 Noise vs. Sophisticated Traders...................................... 6 2.2 Investor Inattention and Delayed Price Response...................... 9 HYPOTHESIS DEVELOPMENT................................................. 12 3.1 Noise Traders’ Effect on Returns.................................... 12 3.2 Noise Traders’ Effect on the ERC.................................... 12 3.3 Pre-Announcement Period Returns..................................... 14 3.4 The Effect of the Online Period..................................... 14 3.5 Post-Earnings Announcement Drift.................................... 15 3.6 Trading Volume...................................................... 17 3.7 Investor Interest................................................... 17 RESEARCH DESIGN........................................................ 19 4.1 Sample.............................................................. 19 4.2 Measuring Return.................................................... 20 4.3 Measuring Earnings Surprise......................................... 21 4.4 Measuring Trading Volume............................................ 21 4.5 Models.............................................................. 21 4.5.1 Testing of H1-H3................................................ 21 4.5.2 Testing of H4-H6................................................ 23 4.5.3 Testing of H7................................................... 24 4.5.4 Testing of H8................................................... 25 4.5.5 Test of H9 & H10................................................ 25 RESULTS................................................................ 28 5.1 Descriptive Statistics.............................................. 28 5.2 Analysis............................................................ 28 5.2.1 Test of H1....................................................... 28 5.2.2 Test of H2...................................................... 29 5.2.3 Test of H3....................................................... 30 5.2.4 Test of H4 - H6.................................................. 31 6. 5.2.5 Test of H7...................................................... 33 5.2.6 Test of H8...................................................... 35 5.2.7 Test of H9-H10.................................................. 35 CONCLUSION............................................................. 61 REFERENCES................................................................. 64 BIOGRAPHICAL SKETCH........................................................ 67 iv LIST OF TABLES Table 1: Sample and Descriptive Statistics ................................37 Table 2: Descriptive Statistics of Summer vs. Non-Summer and Test of Differences in Mean ..............................................38 Table 3: Test of H2: Regression of Announcement-Period CAR ................41 Table 4: Test of H3: Regression of Pre-Announcement-Period {-10,-1} CAR ...43 Table 5: Test of H4: The Effect of the Online Period on Announcement-Period {0, 2} CAR .......................................................45 Table 6: Test of H5: The Effect of the Online Period on ERC’s .............47 Table 7: Test of H6: The Effect of the Online Period on Pre-AnnouncementPeriod {-10,-1} CAR ..............................................48 Table 8: Test of H7: Regressions of Post-Announcement-Period CAR ...........49 Table 9: Test of H8: Regression of Summer/Non-Summer Differences in Announcement-Period CAR ..........................................56 Table 10: Test of H9 and H10 ...............................................57 v ABSTRACT Conventional wisdom, as well as recent research (Hong and Yu 2009), suggest that trading activity and returns decrease during the summer months, possibly due to decreased market participation by net-buying noise traders. I extend previous research by specifically testing for differences in returns in the period surrounding both summer and non-summer earnings announcements. I document lower abnormal returns surrounding summer earnings announcements compared to non-summer announcements. My results suggest that this difference in abnormal returns is greater in the online-trading period-- an era characterized by increased noise trading. However, I do not find this difference between summer and non-summer announcement-period returns to be related to a firm’s analyst following, market-to-book ratio, or the summer vs. non-summer difference in a firm’s announcement-period trading volume. In addition, I do not find evidence that the summer vs. non-summer difference in announcement-period returns is affected by the level of unexpected earnings revealed in the earnings announcement. vi CHAPTER 1 INTRODUCTION It is a widely-held belief that trading activity decreases during the summer months, spawning the term ―summer doldrums‖ to period. 1 describe this time Hong and Yu (2009) confirm the existence of significantly lower trading volume and returns on U.S. exchanges during the summer (measured as the months of the 3rd calendar quarter (July through September)). They find that trading volume decreases by 8.9% and monthly returns decrease by 1% during the summer. They also document lower summer countries with summer decreases in trading volume. findings of lower summer returns and trading returns for other They attribute their volume to the relative inattention/absence of both institutional investors and noise traders. Much accounting literature has examined the market reaction to earnings announcements (Ball & Brown 1968, Beaver 1968, among many others). Some research has focused on how this price and/ or volume reaction is affected by the timing of the earnings announcement, such as the day of the week of the announcement (Dellavigna and Pollet 2009) or the time of day of the announcement (Doyle and Magilke 2009). However, to my knowledge, my study represents the the first to examine how market reaction to earnings announcements is affected by the pervasive and predictable summer slowdown in trading activity. I extend the work of Hong and Yu (2009) by examining the effect that this summer decrease in investor attention may have on the market reaction to U.S. earnings announcements made during the summer. My study focuses primarily on the relative summer absence of noise traders and its effect on earnings announcement price reactions and earnings response coefficients (ERC’s), or the market price reaction to a unit of earnings surprise. Though noise traders are thought to be unsophisticated, a considerable amount of research suggests that their trading can affect stock returns. Barber and Odean (2008) find that noise traders tend to trade in stocks that 1 Abundant references to the typical summer slowdown in trading activity can be found in the popular literature using a key word search. Examples include: http://blogs.wsj.com/financial-adviser/2010/05/03/the-truth-about-sell-in-may-and-goaway/ http://seekingalpha.com/article/221113-four-tech-titans-with-cash-to-spend http://www.marketwatch.com/story/sp-500-is-on-uncertain-footing-2010-06-01 http://www.marketwatch.com/story/us-stocks-slump-on-european-bank-worries-2010-09-07 http://beginnersinvest.about.com/od/beginnerscorner/qt/summerdoldrums.htm 1 catch their attention. which The personal preferences of noise traders can dictate attention-grabbing stocks they will buy. The authors suggest that contrarian investors may choose to buy out-of favor stocks that catch their eye, while momentum investors may chase recently high-performing (glamour) stocks. Due to noise traders’ aversion/ inability to sell short, they act as net-buyers of these attention-grabbing stocks. Lee (1992) positive finds and magnifying that noise negative reactions traders earnings to are net surprises. positive Consistent with this belief, buyers This surprises can and subsequent have tempering the to both effect of reactions to negative surprises. Frazzini and Lamont (2010) show that stock prices rise around earnings announcements and suggest that this earnings announcement premium is driven by small (noise) investor buying when the announcement catches their attention. Huo, Peng, and Xiong (2009) suggest that individual investor attention can both increase price overreactions in up markets as well as attenuate underreactions to events such as earnings reports. Lamont and Thaler (2003) suggest that the mispricing caused by noise traders may not be fully corrected by arbitragers. Other research argues that increased online trading has lead to greater noise trader participation and greater ERC’s (Ahmed, Schneible, and Stevens 2003). Given this body of research suggesting that the presence of noise traders can affect announcement-period price reactions, it stands to reason that differences in attention levels among these investors between summer and non-summer earnings announcements may produce differences in price reactions around these announcements as well. Specifically, newsworthy events, such as earnings surprises, may catch the attention of fewer net-buying noise traders during the summer. Therefore, I hypothesize and find that there is a less positive price reaction around the time of a summer earnings announcement relative to a non-summer announcement. These results hold for both my full sample as well as sub-samples of positive and negative earnings surprises. I also consider how noise trader inattention and an absence of net buying might affect the market reaction to the earnings news itself. Research suggests there is significant noise-trader buying following both positive and negative earnings surprises (Lee 1992) that catch the attention of noise traders (Barber and Odean 2008). It is reasonable to suggest that the magnitude of an earnings surprise is directly related to the ability of an earnings announcement to catch investors’ attention. Therefore, the relative inattention of net-buying noise traders during the summer may result in a smaller positive price reaction to a unit of positive earnings surprise, 2 especially for large positive surprises. However, this summer decrease in net-buying noise trading may result in a larger downward price reaction to a unit of negative earnings surprise, especially for large negative surprises. Therefore, I hypothesize that summer ERCs will be less positive following a positive earnings surprise, but more positive following a negative earnings surprise. evidence However, my results do not support this view since I do not find of earnings. a differential summer vs. non-summer reaction to unexpected This is consistent with the view that a noise-trader’s decision to trade following an earnings announcement is based primarily on the event itself, as opposed to the level of unexpected earnings. Based upon the predicted and/or actual behavior of noise traders, other types of investors may help create differences between summer and non-summer earnings announcement price reaction and ERC’s. Frazzini and Lamont (2010) provide evidence of a general increase in institutional-investor buying just before earnings announcements. However, this buying is followed by institutional selling beginning just after the announcement, as noise-trader buying emerges. Therefore, Frazzini and Lamont institutional investors front-run noise traders. (2010) conclude that Noise-trader-induced price appreciation immediately following the announcement can make institutionalinvestor pre-announcement investors choose announcement. buyers not buying to profitable sell the even shares if the immediately institutional following the In such situations, these institutional investors act as net- during the combined pre-announcement and announcement periods. Because they may anticipate it to be less profitable, institutional investors may decrease the amount of their pre-announcement buying in the summer since there are fewer net-buying noise traders to boost the stock price following the announcement. I find evidence consistent with this view as I document lower pre-announcement-period abnormal returns in the summer period compared to those of the non-summer period. increased in the (noise) trading. to the effects online period, I conclude that this difference has perhaps, due to the increase in online Therefore, the behavior of other investors may contribute under examination based upon the perceived and/or actual behavior of noise traders. In addition announcement-period to examining event window, price I reaction test for within possible the shorter-term differences in the longer-term price reaction to summer announcements compared to that of nonsummer announcements. investor attention at Research the time suggests of an 3 that earnings in some cases announcement may decreased cause a reduced immediate price reaction (Dellavigna and Pollet 2009). whether decreased followed by increased price drift Consistent with this notion, I investigate investor attention during the summer also causes differences in the delayed price reaction to summer vs. non-summer earnings announcements. I find a direct relationship between announcement-period cumulative abnormal returns (CAR’s) and longer-term, post-announcement CAR’s. In addition, my results suggest that, on average, mean abnormal returns over the longer post-announcement period are lower during the summer than during the non-summer period, even after controlling for announcement-period returns or unexpected earnings. I find some evidence of a summer vs. non-summer difference in post-announcement price drift. Taken together, these results support the belief that post-announcement returns are also affected by the summer absence of net-buying noise traders. Even though the summer slowdown may produce differential effects regardless of whether a firm announces good or bad news, it is reasonable to suggest that not all firms will be affected equally. Specifically, the stocks that experience the largest noise trader participation in the nonsummer period may be associated with the largest change in price reaction and ERC’s during absent. the summer when those net-buying noise traders tend to be All else equal, a decrease in noise trader participation translates to an overall decrease in net-buying trading volume and, consequently, an overall decrease in returns. Therefore, I hypothesize that the difference in announcement-period abnormal returns is directly related to the difference in summer vs. non-summer announcement-period trading volume; however, my results do not support this prediction. Barber and Odean (2008) suggest that noise traders are more likely to trade in stocks that catch their attention. analyst following interest. and market-to-book ratio (MTB) to proxy for I use investor I control for unexpected earnings (UE) because it is reasonable to suggest that the magnitude of an earnings surprise is directly related to the ability of an earnings announcement to catch investors’ attention. after controlling relationship for between unexpected the summer earnings, vs. I do non-summer not find difference a in However, significant a firm’s announcement-period abnormal returns and either its MTB or analyst following. Similarly, I do not find a significant relationship between either MTB or analyst following and the summer vs. non-summer difference in announcementperiod trading volume. It may be the case that overall trading volume is driven, in large part, by forces other than those under consideration (e.g. high-frequency trading). 4 My study contributes to the literature by examining how stock prices impound the information contained in accounting disclosures based upon both the size and composition of the audience for the disclosure. It stands to reason that inattention on the part of sophisticated investors who trade according to estimates of fundamental value may cause the price discovery period to be prolonged. However, the inattention of net-buying noise traders, as appears to be the case in the summer, may actually alleviate upward pressure on prices. 2009, Patell between and firm Wolfson disclosures While previous research (Dellavigna and Pollet 1982, and among others) investor addresses inattention the in relationship other specific situations, little attention has been paid to the differences in trading environment between the summer and non-summer period. Prior research has addressed noise-trader behavior (Barber and Odean 2008; Lee 1992; Ahmed et al. 2003, among others) as well as the effects of the typical summer slowdown in trading activity (Hong and Yu 2009). However, to my knowledge, mine is the first study to examine the effects that the summer inattention of noise traders has on earnings announcement returns and trading volume. Because the summer slowdown is a recurring, predictable phenomenon affecting the vast majority of publicly-traded firms, my results may help investors make better trading decisions, especially during the summer. In addition, my findings may encourage accounting researchers to control for summer effects to better test for other earnings announcement phenomena. The provides rest a of this discussion paper of is organized background as follows. literature. The Chapter 3 next chapter contains the theoretical background for the hypotheses that I test. Chapter 4 provides a discussion analyze of my research design. In Chapter 6 concludes. 5 Chapter 5, I my results. CHAPTER 2 BACKGROUND 2.1 Noise vs. Sophisticated Traders Due to their information content, earnings announcements significant market reaction (Beaver 1968, Ball & Brown 1968). cause a Accounting research has extensively studied this reaction in an effort to determine the factors affecting ERC’s (Collins and Kothari 1989; Easton and Zmijewski 1989), stock price (Atiase 1985), and trading volume (Bamber et al. 1995) following the announcement. While much of the literature focuses on identifying firm characteristics that help determine the nature of the market reaction, my study focuses primarily on the characteristics of the audience of investors for the announcement. The differential beliefs and behavior of these investors can help dictate the nature of the market response to the announcement. According to Beaver (1968), the change in price reflects the average change in investors’ beliefs whereas trading volume reflects the sum of the differences in investors’ reactions to the earnings announcement. Several trading models involving price change and volume have since been constructed (Kim and Verrecchia 1991, 1994, Abarbanell et al. 1995, Kandel and Pearson 1995, among others). An important drawback of many early models of trade in speculative markets is both information identically as the well assumption as the that agents simplifying change is directly related to trading volume. interpret prediction public that price With such assumptions, some models are unable to explain the often-observed situation of heavy trading volume accompanied by little or no price change or vice-versa (Bamber and Cheon 1995). Kandel and Pearson (1995) improve upon much of the prior literature by modeling the scenario in which agents are heterogeneous in their interpretation of news as well as their prior beliefs. allows for the presence of ―noise‖ traders whose naïve Their model trading can significantly alter the price move that would otherwise be caused by the trading of informed investors (Kyle (1985), Hasbrouck (1991)). these noise traders take the opposite side of a trade with The more that an informed investor, the less an informed investor’s trades will change the price of a stock and reveal private information. Consequently, noise trading can both distort the assumed positive relationship between trading volume and price 6 change as suggest well that as cause price noise-trader movement behavior away from helps fundamental drive the value. effects I under consideration in my study. Though to be uninformed, evidence suggests it could introduce an upward price bias. By analyzing the noise intraday trading transaction of small data, Lee investors (1992) finds is thought that noise traders (those placing orders of less than $10,000) are net buyers subsequent to both positive and negative earnings surprises. Consistent with these results, Barber and Odean (2008) suggest that individual investors are net buyers of attention-grabbing stocks following events such as earnings announcements. By analyzing stock transactions in individual brokerage accounts, the authors find that individual investors are net buyers on high volume days, days when stocks are in the news, and days following extremely positive one-day returns. both extremely negative and Huddart, Lang, & Yetman (2009) document increased noise-trader buying when examining the increased trading volume that occurs when stock prices cross either the upper or lower limit of a key trading range—another example of an attention-grabbing event. Barber and Odean (2008) cite the time and resource constraints that noise traders face in selecting stocks to buy. These individuals cannot thoroughly screen the thousands of possible selections; thus, they will more likely purchase a stock that has grabbed their attention. An earnings announcement itself can prompt noise-trader buying, regardless of the earnings news. Stocks that miss an earnings forecast may be favored by bargain-hunting noise traders who take a contrarian view to that of the market. Stocks that meet or beat a forecast may attract buying from momentum investors chasing high-performing (glamour) stocks. Noise traders do not face the same daunting selection task when selling stock since they will most likely sell one of the stocks they already own instead of traders are mechanical selling short. especially impediments Lamont burdened and by Thaler short-sale administered by (2003) suggest constraints regulators. This that noise through the aversion or inability to sell short tends to cause noise traders to be net-buyers who exert upward price pressure on stocks following both positive and negative earnings surprises. This net-buyer effect of noise traders is consistent with Miller (1977), who suggests the holders of a stock will tend to be those who are most optimistic about its prospects and that, given institutional (or self-imposed) potential constraints owners on (potential short-selling, buyers) should 7 any increase result in a in the price set of increase. Lamont and Thaler constraints and/or (2003) suggest that risk, stock liquidity in the prices presence can be arbitragers are unable or unwilling to correct them. of short-sale mispriced because They discuss examples of such clear mispricing, including the well-documented spin-off of Palm by its parent company 3Com in 2000. In this case, despite the fact that there was a simple, relatively risk-free arbitrage opportunity, the shares of the two companies remained wildly mispriced for months because of the short-term constraints on selling short. Frazzini and Lamont (2010) provide additional trading can introduce an upward price bias. around earnings announcements earnings announcement intraday transaction and premium data that to ―quantitatively consistently address that noise They show that stock prices rise this appears evidence which since set(s) of substantial‖ 1927. Using investors are responsible for the premium, the authors use small trades (less than $5,000) as a proxy for individual investors and big trades (over $50,000) as a proxy for institutional investors. They conclude that the earnings announcement premium is driven by noise-trader buying when the announcement catches their attention. They find evidence that large investors are aware of the premium and trade in anticipation of it. They document abnormal net-buying by large investors in the two-week period preceding the earnings announcement. This large-investor buying activity reverses on announcement day and on the two trading days subsequent to the announcement, when noise-trader buying is most intense. Large diminishing premium. what investors would appear otherwise to be be an front-running even-larger noise earnings traders and announcement However, the presence of abnormal returns immediately following announcements indicates that large investors have not eliminated the premium. The premium may continue to exist because of market frictions such as transaction or holding costs (Lamont and Thaler 2003). Evidence suggests that, in addition to affecting returns, noise traders can also affect ERC’s. Ahmed et al (2003) address the assertion that the advent of online trading has increased the ratio of naïve (noise) traders to sophisticated traders. Because sophisticated investors are thought to have more precise information than do noise traders, the online period is thought to be associated information precise prior prior with to a decrease earnings information will in the average announcements. rely more on precision Since the of investors earnings investor with less announcement information, this decreased average precision of prior information in the online period would translate to larger revisions in investor beliefs post8 announcement and, hence, larger ERC’s. Consistent with these views, Ahmed et al (2003) cite increased noise trading for their finding of larger ERC’s in the online period (1996-99) than in the pre-online period (1992-95). The authors combine positive and negative earnings surprises in their analysis, implicitly assuming a symmetric relation. However, it is possible that noise-trader effects on the ERC may depend upon the sign of the earnings surprise. Prior research finds that noise traders are net-buyers following both positive and negative earnings surprises (Lee 1992) in stocks that catch their attention likelihood (Barber that a and stock Odean catches 2008). a It noise stands trader’s to reason attention that is the directly related to the magnitude of the earnings surprise for both positive and negative surprises. This would suggest there may be significant noise-trader buying following both large positive and negative earnings surprises. Stocks that miss an earnings forecast can attract bargain-hunting, contrarian noise traders. Stocks that meet or beat a forecast may induce buying from momentum investors chasing high-performing (glamour) stocks. Evidence indicates not only that noise traders are net-buyers around earnings announcements, but also that this behavior can have a significant effect on returns and/or sophisticated investors. ERC’s because it is not fully counteracted by It follows, then, that returns and/or ERC’s are likely to vary according to the attention levels of noise traders. The next section provides a discussion of evidence supporting this view. 2.2 Investor Inattention and Delayed Price Response Research affect the announcement. suggests size and/or the reaction composition more of of an the earnings investor announcement audience for can the Doyle and Magilke (2009) examine the difference in trading for announcements before-the-market-open (BMO). attract timing In turn, this can affect the price and/or volume reaction to the announcement. volume that investor made after-the-market close (AMC) and It has been suggested that AMC announcements attention because of the larger amount of time, compared to that of BMO announcements, that elapses between the announcement and the resumption of trading in the stock the following morning. evidence consistent with increased noise-trader participation They find for AMC announcements as they document larger abnormal trading volume following an AMC announcement. Dellavigna and Pollet 9 (2009) suggest that investor- attention levels for earnings announcements also vary according to the day of the week the announcement is made. that there weekdays, is less they investor document less Consistent with the traditional view attention immediate on Fridays price compared response and with trading other volume following a Friday earnings announcement. If low investor attention levels at the time of an earnings announcement result in a lower immediate price response to the announcement, it is reasonable to believe that the stock price may continue to ―drift‖ as investors revisit the information contained in the announcement and correct the initial mispricing. Indeed, this reasoning has been used to explain the phenomenon of post-earnings announcement drift (PEAD). PEAD describes the well-documented tendency of post-announcement stock prices to continue to move in the direction of an earnings surprise (Ball and Brown 1968, Bernard and Thomas 1989).2 While Dellavigna and Pollet (2009) find less of an immediate price response to Friday earnings announcements, they find that Friday earnings announcements are associated with more price drift in the post-announcement period (up to 75 trading days after the announcement). They suggest that PEAD represents a delay in the price discovery process caused by investor inattention at the time of the announcement followed by a price drift as investors continue to process the earnings information well after the announcement. Huo, Peng, attention and can This explanation is supported by the evidence of Xiong (2009), who suggest mitigate the drift that associated increased with noise-trader initial earnings announcement underreaction. I build upon the existing literature on investor inattention and PEAD in my study of summer earnings announcements. In addition, I extend research indicating that investor attention is lower during the summer period. Hong and Yu (2009) find that, for the period 1962-2005, monthly share turnover (trading volume divided by shares outstanding) summer than during the rest of the year. is 8.9% lower during the They use intraday transaction data to determine which set(s) of investors are responsible for the decrease in summer trading activity. Consistent with the technique of Frazzini and Lamont (2010), the authors use small trades (less than $5,000) as a proxy for individual investors and institutional investors. big trades (over $50,000) as a proxy for They find a summer decrease in trading activity for 2 Instead of attributing PEAD to investor inattention at the time of the announcement, Bernard and Thomas (1989) suggest that it is caused primarily by an apparent inability of the market to understand the implications of current quarterly earnings for future earnings. 10 both sets of investors. in summer returns for In addition, Hong and Yu (2009) document a decrease a total of 51 countries significant decrease in summer trading volume. which also experience a The authors conclude that the decrease in summer returns is related to the decrease in summer trading volume and that both are caused by inattention on the part of investors, including noise traders. Research indicates that inattention on the part of net-buying noise traders can announcements. affect returns and trading volume following earnings In addition, existing evidence suggests that there is less investor attention during the summer. However, mine is the first study, that I know of, to examine if and how this general summer slowdown affects both the short and long-term reaction to summer earnings announcements. Because the summer slowdown is both pervasive and predictable, insights from my study should be of use to researchers and capital market participants both inside and outside the firm. 11 CHAPTER 3 HYPOTHESIS DEVELOPMENT 3.1 Noise Traders’ Effect on Returns Because traders are of their normally inability net-buyers or of unwillingness stocks. Due to sell short, to time and noise resource constraints, they tend to buy stocks that catch their attention through a newsworthy event such as an earnings announcement (Barber and Odean 2008). Evidence suggests they are net-buyers following both positive (―meet-or-beat‖ forecasts) and negative (―miss‖ forecasts) earnings surprises (Lee 1992). Sophisticated traders may nullify its effects. announcement investors, is the not rationally anticipate noise-trader behavior and But if this noise-trader buying at the time of the fully upward counteracted pressure leads to by an the trading earnings of sophisticated announcement premium (Frazzini and Lamont 2010). Thus, it stands to reason that inattention on the part of noise traders will have a negative effect, on average, on stock returns and trading volume following an earnings announcement (Dellavigna and Pollet 2009). Hong and Yu (2009) suggest that investor attention is lower during the summer for both institutional investors and noise traders. Institutional investors are willing to both buy stocks as well as sell short according to their more sophisticated beliefs regarding fundamental value; hence, their inattention should not have a pronounced asymmetric effect on returns. However, for noise traders, earnings announcements by themselves can trigger buying regardless of the earnings news. Therefore, I suggest that inattention on the part of net-buying noise-traders during the summer results in lower returns immediately following the announcement for both positive and negative earnings surprises. This leads to my first hypothesis: H1: Announcement-period abnormal returns are lower during the summer period than during the non-summer period. 3.2 Noise Traders’ Effect on the ERC Hypothesis 1 predicts an overall shift in the level of announcementperiod returns, irrespective of the earnings news. 12 In addition, I examine how differential underlying levels earnings of investor information. attention Ahmed et impact al. the (2003) pricing suggest of the that an increase in noise trading during the online-trading era has increased the ratio of noise traders to sophisticated investors participating in the stock market. Since noise traders are assumed to have less precise information than sophisticated do traders, they argue that this has resulted in a decrease in the average precision of investor information prior to earnings announcements. The authors conclude that this translates to larger ERC’s as investors revise their beliefs to a larger degree based on the earnings surprise because of their less precise information pre-announcement. Thus, they suggest that noise traders are responsible for their finding that ERC’s have increased in the online period (beginning in 1996) for a combined sample of both positive and negative earnings surprises. If net-buying noise traders tend to increase ERC’s for all earnings surprises, then one might expect that summer noise-trader inattention may produce lower ERC’s for all summer announcements. However, it is possible that noise-trader effects on the ERC depend upon the sign and/or magnitude of the earnings surprise. This is because there may be significant noise-trader buying following both positive and negative earnings surprises (Lee 1992), especially for large surprises in either direction that catch noise traders’ attention (Barber and Odean 2008). It follows that the relative inattention of net-buying noise traders during the summer may result in a smaller positive price reaction to a unit of positive earnings surprise, especially for large positive surprises. net-buying negative may result earnings in a However, this summer decrease in noise trader larger surprise, downward especially price for reaction large to a negative unit of surprises. Therefore, while a strict extension of Ahmed et al. (2003) would predict that summer ERC’s, compared to those of non-summer, are smaller for negative earnings surprises, I expect that the absence of net-buying noise traders causes summer ERC’s to be larger for negative earnings surprises. hypothesize that announcements, are summer earnings associated with announcements, a smaller ERC surprises and a larger ERC for negative surprises. compared for to Thus, I non-summer positive earnings This forms my second hypothesis: H2: For positive (negative) earnings surprises, the ERC is smaller (larger) for summer announcements than for non-summer earnings announcements. 13 3.3 Pre-Announcement Period Returns The relative summer inattention of noise traders may prompt changes in institutional decide to investor profit behavior. from this Sophisticated noise-trader investors behavior by may buying rationally in the pre- announcement period and then selling into the noise–trader buying following the announcement. Consistent with this view, Frazzini and Lamont (2010) find increased institutional-investor buying pre-announcement along with increased institutional-investor selling pressure following the announcement. However, this selling does not completely eliminate the earnings announcement premium. This could be due to market frictions that prevent arbitragers from fully correcting the mispricing caused by noise traders (Lamont and Thaler 2003). If institutional investors front-run anticipated buying of noise traders, then, during the summer, fewer institutional investors may be buying in the pre-announcement traders to be period. buying This in the profit opportunity to exploit. could be announcement because period they and, expect fewer noise consequently, less This would be consistent with the findings of Hong and Yu (2009), who suggest that trading activity decreases in the summer for both noise traders as well as institutional investors. This leads to my third hypothesis: H3: Pre-announcement-period abnormal returns are lower during the summer period than during the non-summer period. 3.4 The Effect of the Online Period The proliferation in recent years of 24-hour news dissemination and online, low-cost trading may have impacted the traditional differences in the summer vs. non-summer periods. Prior research (Ahmed et al. 2003, Barber and Odean 2002) suggests that the emergence of online trading has led to more overall noise trading, where online investors, compared to professional investors, are thought to be less sophisticated and profitable (Barber and Odean 2002). It is reasonable to suggest that the overall increase in noise trading in the online period has created a larger difference between the summer vs. non-summer levels of noise trading. This is because the more noise trading there is in the non-summer period, the larger the potential decrease in noise trading that will occur due to the summer inattention of 14 noise traders. Simply stated, the level of inattention on the part of noise traders is relevant only when they have the ability to trade in the first place. The innovations of the online period have enhanced this ability. My focus is on differences in investor attention between summer and non-summer periods; therefore, I examine whether the effects hypothesized in H1 through H3 are stronger in the online period than in the pre-online period. Since I believe the summer vs. non-summer difference in noise-trading activity has increased in recent years, I believe the effects that I am already testing have become more pronounced in the online period. This leads to my next three hypotheses: H4: The difference between summer and non-summer announcement-period abnormal returns is greater in the online period than in the pre-online period. H5: The difference between summer and non-summer ERC’s is greater in the online period than in the pre-online period. H6: The difference between summer and non-summer pre-announcement-period abnormal returns is greater in the online period than in the pre-online period. Consistent with my analysis of H1- H3, I test H4- H6 by examining positive and negative earnings surprises separately as well as in a combined sample. 3.5 Post-Earnings Announcement Drift As previously discussed, I believe that an absence of net-buying noise traders in the summer results in less positive returns immediately following the earnings announcement. term returns Expectations that As part of my study, I also examine the longer- occur regarding these in the returns post-announcement are less clear. (drift) The period. relative inattention of net-buying noise traders in the summer is expected to result in a less upward-biased price immediately after the earnings announcement. All else equal, any post-earnings announcement drift would be relatively more symmetric than the drift following non-summer announcements, assuming prices gravitate to fundamental values during the summer post-announcement period as they may in the non-summer. However, 15 if there is also relatively less attention on the part of institutional investors who trade according to fundamental value then there may be a greater delay in the price discovery process during the summer. This could exacerbate the price drift in the direction of the earnings surprise (positive and negative) as these investors later revisit the earnings information. Dellavigna and Pollet (2009) find that Friday earnings announcements are associated with significant price drift in both directions as investors re-emerge and begin to correct the initial underreaction as early as the following week. While Friday earnings announcements may be characterized by relatively high levels of inattention on the part of both sets of investors, the results of Dellavigna and Pollet (2009) are likely driven primarily by institutional announcement. investor inattention at the time of a Friday Relative inattention of noise and sophisticated traders are likely to have distinct effects on the nature of the summer PEAD. institutional earnings investor inattention may cause an initial As noted, underreaction in price followed by a symmetrical price drift in the direction of the earnings surprise. Thus, prices might be slower to gravitate to fundamental values in the summer. Alternatively, noise-trader inattention may cause a lack of upward-biased price reaction to the earnings announcement. Assuming that the earnings information is not fully impounded in prices at the time of the announcement, as suggested by prior research, summer announcements might be followed by a more symmetric price drift than for non-summer announcements. Announcement-period inattention on the part of both sets of investors may result in price drift in both directions, with the positive price drift being stronger than the negative drift. Adding to the difficulty in predicting price drift is the uncertainty regarding the timing and extent of the reemergence of investors necessary to create the price drift. Hong and Yu (2009) find that returns for the entire summer period are generally lower than for the non-summer period. This is consistent with there being less summer noise-trader attention during the announcement period as well as the post-announcement period—at least until the end of the summer. In other words, investors may not re-emerge before the end of the summer period to correct any initial underreaction. dominate. Therefore, difference in I expect post-announcement I am not sure which of the effects will that price hypothesis: 16 there is drift. a summer This vs. becomes non-summer my next H7: There is a summer and non-summer difference in longer-term, post- announcement price drift. 3.6 Trading Volume Hong and Yu (2009) find that both trading activity and returns are lower during the summer. Both effects may be caused, at least in part, by the relative inattention of noise traders and, thus, the relatively less upward price pressure during the summer. should be difference positively in total correlated trading with volume Because noise trader attention total between trading the volume, summer and I use the non-summer announcement periods as a proxy for the difference in noise trader attention. I suggest that the greater the difference in summer vs. non-summer attention of noise traders (as proxied by the difference in summer vs. non-summer announcement-period trading volume), the greater the difference in summer vs. non-summer announcement returns as well. However, it may be the case that factors such as high-frequency trading sufficiently distort the relationship between returns and trading volume such that I do not find an association. This leads to my next hypothesis: H8: The returns difference is in increasing summer in and the non-summer difference announcement-period between summer and abnormal non-summer announcement-period trading volume. 3.7 Investor Interest The possibility that the summer slowdown produces an appreciable decrease in noise trader activity is directly related to a stock’s level of non-summer, baseline noise trader participation. Simply put, it is those stocks that noise traders normally trade that should experience a greater effect on volume and returns due to less attention. noise traders will be more likely attention (Barber & Odean 2008). to trade in Evidence suggests that stocks that catch their Salience of a stock is likely reflected by the number of analysts following the stock since it is reasonable to assume that analyst following is positively related to overall investor interest 17 (O’Brien and Bhushan 1990). Therefore, analyst following for a stock should be directly related to both noise trader participation in the baseline, nonsummer period as well as the drop-off in noise trader participation during the summer. This large reduction in noise trader participation may result in a large reduction in both announcement-period abnormal returns and trading volume. This leads to my next hypotheses: H9A: The difference in summer and non-summer announcement-period abnormal returns is increasing in analyst following. H9B: The difference in summer and non-summer announcement-period trading volume is increasing in analyst following. Another ratio (MTB). possible proxy for investor interest is the market-to-book The MTB has been used to differentiate ―growth‖ or ―glamour‖ stocks from ―value‖ stocks. Therefore, all else equal, high-MTB stocks may be more likely to catch the attention of noise traders (Hong and Stein 2007). Therefore, a stock’s MTB may be directly related to both noise trader participation in the baseline, non-summer period, as well as the drop-off in noise trader participation during the summer. This large reduction in noise trader participation for high MTB stocks may result in a large reduction in announcement-period returns and trading volume for these firms. My next hypotheses are as follows: H10A: The difference in summer and non-summer announcement-period abnormal returns is increasing in market-to-book ratio. H10B: The difference in summer and non-summer announcement-period trading volume is increasing in market-to-book ratio. 18 CHAPTER 4 RESEARCH DESIGN 4.1 Sample I use a sample period of 1990-2009 so as to include data from both the pre-online and online periods. the 1990-1995 (1996-2009) Consistent with Ahmed et al. (2003), I define period as the pre-online (online) period. Consistent with prior literature (Hong and Yu 2009), I define the summer period as the months of the 3rd calendar quarter (July through September). My study focuses on the effect that the summer slowdown has on the reaction to an earnings announcement. Therefore, I define an earnings announcement as a summer (non-summer) announcement if the announcement actually took place from July through September (October- June). The summer slowdown is a widespread and consistent phenomenon; therefore, I am able to use a large sample of U.S. stocks subject to data availability. Specifically, I use firm-quarter observations that meet the following requirements: Earnings announcement dates and analyst forecast data available in First Call Price and Trading Volume data available in CRSP Financial data available in Compustat Share price on the trading day before the earnings announcement of at least $5.003 Evidence suggests that the price and/or trading volume reaction to an earnings announcement may be affected by whether or not the announcement pertains to the fourth fiscal quarter (Mendenhall and Nichols 1988). examine quarters. earnings announcement observations from only Thus, I non-fourth fiscal To be able to make summer vs. non-summer firm-year comparisons, I also discard non-fourth quarter observations in the cases in which the summer announcement was the fourth-quarter announcement. Because I am comparing summer vs. non-summer earnings announcements for the same firms, I avoid the potential problems associated with the 3 need to create matched pairs of I impose the stock price requirement because of the potential clientele effects associated with stocks priced below $5.00 (―penny stocks‖). In addition, an abnormally low stock price may be problematic when scaling an earnings surprise by price or computing percentage returns. Fluctuations in returns due to the bid-ask bounce can introduce noise in these situations as well, especially in the predecimalization, larger-spread portion of my sample. 19 similar firms. should not be Also because I am using the same set of firms, my results affected by methodology among exchanges. the 4 differences in trading volume My final sample consists of calculation 156,122 firm- quarter observations. 4.2 Measuring Return To measure the cumulative abnormal return during the earnings announcement event window, I define a variable, CAR, defined as follows: CAR = (Raw Return – Expected Return Using the Market Model over the {t= 300, -46} estimation window) compounded over the event window Note: For variables. the sake of brevity I suppress subscripts when describing Unless otherwise noted, variables are measured for each firm- quarter. I measure CAR across hypothesis is being tested. different time periods depending upon which Using {t = 0} to represent the day of the earnings announcement, I define the pre-announcement and announcement periods as follows: {t= 0, 2} (announcement period), {t= -10, -1} (pre-announcement period). Hypothesis 7 concerns both immediate price response as well as possible post-announcement drift. I continue to use the {0, 2} announcement period event window to measure immediate price reaction. For the post-announcement drift period, I use both the 48-day window beginning three days after the announcement ({3, 50}) as well as the 73-day window beginning three days after the announcement ({3, 75}). I use the {3, 50} event window to capture the delayed price response to an earnings announcement without including the price reaction to the following quarter’s announcement. Consistent with Dellavigna and Pollet (2009), I also measure returns as far forward as 75 trading days. My use of a {3, 75} event window is meant to include the market response to the following quarter’s announcement as well.5 4 Due to the nature of the dealer-oriented market of Nasdaq, compared with the auction-oriented markets of the NYSE and AMEX, Nasdaq has traditionally reported higher trading volume for certain types of transactions. See Atkins and Dyl (1997) for a more complete discussion of this topic. 5 There are approximately 250 trading days per calendar year. Because firms tend to keep the number of days between announcements roughly constant (250/4= 62.5), I measure 75 days from the previous announcement to include the period following the next quarter’s announcement. 20 4.3 Measuring Earnings Surprise Consistent with prior research (Ahmed et al. 2003), I define an unexpected earnings variable, UE, as follows: UE= (Actual EPS – (Latest mean consensus forecast)) / (Closing Price 11 trading days before the announcement) My pre-announcement period starts 10 trading days before the earnings announcement. Therefore, I use the closing price 11 trading days before the announcement in my computation of UE so that it will not be affected by preannouncement returns. I winsorize UE at the 1% and 99% levels to remove the effect of outliers. 4.4 Measuring Trading Volume Consistent with prior research (Doyle and Magilke (2009), Hong and Yu (2009), among others), I measure trading volume as follows: Trading Volume = (Number of shares traded) / SharesOutstanding Where (Number of shares traded) is the average daily number of shares traded over the 3-day {t=0, 2} announcement-period and SharesOutstanding is the number of shares outstanding on the day of the earnings announcement. I scale the number of shares traded by the number of shares outstanding so as to not have my results unduly affected by those companies with the most shares outstanding. My concept of trading volume relates to the proportion of a firm’s shares traded as opposed to an absolute number of shares. 4.5 Models 4.5.1 Testing of H1-H3. To test H1, I first compute {0, 2} announcement-period returns, by firm-year, for both the summer and non-summer periods. I then test for a difference in mean across the two periods. Earnings announcements are attention-grabbing events that spur noise-trader buying regardless of the earnings surprises. It follows that the summer inattention cause, of net-buying noise traders will on average, positive price reaction to all summer earnings announcements. suggested in H1, I predict that the mean of summer less Therefore, as announcement-period returns will be significantly lower than that of non-summer returns. 21 a H2 examines how the differential summer reaction may be related to the news in the earnings announcement. I begin by estimating the following earnings response model for the three-day announcement window: MODEL 1: CAR = B0 + B1Summer + B2UE + B3Summer*UE + E Where: CAR= cumulative abnormal return over the {0, 2} event window Summer= 1 if the earnings announcement occurred July-September, = 0 otherwise UE= unexpected earnings scaled by stock price H2 suggests that non-summer earnings announcements, compared to summer announcements, are associated with a larger ERC surprises and a smaller ERC for negative surprises. for positive earnings Consequently, I test H2 using Model 1 on sub-samples of positive earnings surprises (―meet-or-beat‖ firms) and negative earnings surprises (―miss‖ firms) individually. and Kothari (1989) examine factors that help explain Collins cross-sectional variation in the ERC, including growth, persistence of earnings, riskless interest rates, and systematic risk. I do not control for such variables in Model 1 because I am examining seasonal (summer vs. non-summer) variation, as opposed to cross-sectional variation, and I have no reason to believe that there are systematic summer vs. non-summer differences in these variables. In order for these variables to cause summer vs. non-summer differences in ERC’s, the level of these variables would need to repeatedly shift from nonsummer levels, to summer levels, and back again to non-summer levels within the same year. This seems inconsistent with the normal, more gradual change in these variables, which are often most-effectively calculated using annual data. These annual measurements would be too coarse for my study, which most often considers 3 firm-level observations within a single 9-month period. B2, the announcements coefficient on coefficient and the is on expected interaction UE, to term represents be significantly Summer*UE, effect on the ERC for summer announcements. H2, I predict that B3 the ERC for positive. captures the non-summer B3, the incremental It follows that, consistent with will be significantly negative (positive) for positive (negative) earnings surprises. B1, the coefficient on Summer, captures the difference in returns between summer and non-summer, after controlling for the earnings surprise. Finding B1 to be significantly negative would be consistent with H1. 22 H3 suggests that pre-announcement returns are lower during the summer. To test H3, I estimate Model 1 over the {-10, -1} event window after removing “B2UE” and “B3Summer*UE”. I remove unexpected earnings (UE) from the model since it can be argued that I should not control for them in this situation. This is because the motivation for any single investor to buy in the preannouncement period is increasing in the degree to which he believes the company will beat the analysts’ estimates and, consequently, the stock price will increase following the announcement. Therefore, controlling for unexpected earnings in my test of pre-announcement returns may remove the effect for which I am testing. Consistent with H3, I expect the coefficient on Summer to be significantly negative for the combined sample as well as for each sub-sample. 4.5.2 6 Testing of H4-H6. PostOnline, to Model 1. To test H4-H6, I add a binary variable, PostOnline = 1 if the earnings announcement took place from 1996-2009, = 0 for observations from 1990-1995. I run separate regressions for the combined sample as well as each sub-sample using the following model: MODEL 2: CAR= B0 + B1Summer + B2UE + B3Summer*UE + B4PostOnline B5Summer*PostOnline + B6UE*PostOnline + B7Summer*UE* PostOnline + + E I suggest that the overall increase in noise trading in the online period has created a larger difference between the summer vs. non-summer levels of noise trading. As stated in H4 (H6), this should manifest in a larger difference in summer vs. non-summer announcement-period) abnormal returns. announcement-period (pre- B5, the coefficient on the interaction term Summer*PostOnline, represents the incremental difference in summer vs. non-summer news. returns during the online period, independent of the earnings Consistent with H4 (H6), I predict that B5, will be significantly negative for the combined sample of announcement-period returns (pre- announcement-period returns) as well as for each sub-sample individually. H5 states that the difference increased in the online period. the interaction term in summer vs. non-summer ERC’s has Therefore, I predict that the coefficient on Summer*UE*PostOnline positive and negative earnings surprises. 6 will be significant for both However, I expect the sign of the I also consider H3 while controlling for unexpected earnings (UE) since it may be the case that pre-announcement returns are lower in the summer due to lower unexpected earnings instead of less noise-trader activity. 23 coefficient on the interaction term to be negative for positive earnings surprises and to be positive for negative earnings surprises. This follows directly from my prediction that for positive (negative) earnings surprises, the ERC is larger (smaller) for non-summer announcements than for summer earnings announcements. 4.5.3 Testing of H7. The presence and magnitude of longer-term postannouncement price drift may vary based upon which group(s) of investors are paying attention at the time of the earnings announcement. Institutional- investor inattention may cause an initial underreaction in price followed by greater price drift in the direction of the earnings surprise, i.e., both positive and negative drifts. mean less upward-biased summer Alternatively, noise-trader inattention may announcement returns followed symmetric price drift than following non-summer announcements. by a more Announcement- period inattention on the part of both sets of investors may result in price drift in both directions. Adding to the difficulty in predicting price drift is the uncertainty regarding the timing and extent of the re-emergence of investors necessary to create the price drift. Hong and Yu (2009) find that returns for the entire summer period are generally lower than for the non-summer period. This is consistent with there being less summer noise-trader attention during the announcement period as well as the post-announcement period, at least until the end of the summer. In other words, investors may not re-emerge before the end of the summer period to correct any initial underreaction. remains an difference empirical in the announcement-period. question longer-term I do not whether there price drift expect these is a summer that occurs possibly vs. Thus, it non-summer following conflicting responsible for price drift to perfectly cancel themselves out. the factors Therefore, as stated in H7, I expect there to be a difference between summer and nonsummer price drift. To test H7, I first run regressions of longer-term post-announcement CAR’s for the combined sample as well as each sub-sample using the following models: CAR{3,50} = B0 + B1 Summer + B2CAR{0,2} + B3Summer*CAR{0,2} + E CAR{3,75} = B0 + B1 Summer + B2CAR{0,2} + B3Summer*CAR{0,2} + E 24 In addition, I test the sensitivity of these results by unexpected earnings (UE) in additional regressions using the adding in following models: CAR{3,50} = B0 + B1 Summer + B2UE + B3Summer*UE + E CAR{3,75} = B0 + B1 Summer + B2UE + B3Summer*UE + E B1, the coefficient on Summer, represents the incremental difference in longer-term CAR between the summer and non-summer periods. B2 represents the incremental change in longer-term CAR for a given change in either CAR{0,2} or UE, depending upon the regression in question. Finding B2 to be significantly positive is consistent with longer-term price drift in the same direction as announcement-period CAR or UE. B3, the coefficient on the interaction term, represents the incremental difference in this price drift between the summer and non-summer periods. Since H7 suggests that there is a difference between summer and non-summer price drift, I predict that B3 will be significant for the combined sample as well as each sub-sample individually. 4.5.4 Testing of H8. H8 concerns the possible relationship between a firm’s summer vs. non-summer announcement-period abnormal returns and the difference volume. between its summer vs. non-summer announcement-period trading To test H8, I run the following regression on the combined sample: MODEL: CARDiff = B0 + B1VolumeDiff + E Where: CARDiff = [(Average non-summer announcement-period CAR) – (Summer announcement-period CAR)] by firm-year VolumeDiff = [(Average non-summer announcement-period trading volume) – (Summer announcement-period trading volume)] by firm-year All else equal, a decrease in trading volume may be directly related to a decrease in net-buying noise trader activity and, thus, abnormal returns over the same period. Therefore, I expect that B1, the coefficient on VolumeDiff, will be significantly positive. 4.5.5 Test of H9 & H10. It stands to reason that it is those stocks that noise traders normally trade that should experience the most significant effects during their summer absence. Evidence suggests that noise traders 25 trade in attention-grabbing stocks (Barber & Odean 2008). It is reasonable to assume that MTB and analyst following are positively related to salience and, thus, baseline non-summer noise trader participation. should experience therefore, a a larger larger decrease decrease in in noise trader announcement-period These same stocks participation abnormal and, returns and trading volume during the summer. H9 suggests a direct relationship between a firm’s analyst following and the summer vs. non-summer difference in both its abnormal returns (H9A) and trading volume (H9B). H10 suggests a direct relationship between a firm’s market-to-book ratio and the summer vs. non-summer difference in both its abnormal returns (H10A) and trading volume (H10B). sort the combined sample into decile ranks, by To test H9 & H10, I year, based on both the market-to-book ratio (MTB) and the number of analysts making an EPS estimate for each announcement observation. 7 I use decile ranks because I do not expect the proposed relationships in H9 & H10 to be purely linear. I test H9A and H10A using the following regression in which Analysts and MTB are included both separately and simultaneously: MODEL: CARDiff = B0 + B1Analysts + B2MTB + B3UEDiff + E Where: CARDiff = [(Average non-summer announcement-period CAR) – (Summer announcement-period CAR)] by firm-year; Analysts = the decile rank of the number of analysts making an EPS estimate for the summer announcement in a given year MTB = the decile rank of the market-to-book ratio measured using the closing price 11 trading days before the announcement and measured using book value at the beginning of the fiscal year UEDiff = [(Mean of the non-summer unexpected earnings)] by firm-year. unexpected earnings) – (Summer I include UEDiff because summer vs. non- summer differences in UE can be expected to generate differences in CAR, regardless of Analysts and MTB. Consistent with H9A and H9B, I predict that the coefficients on Analysts and MTB will be significantly positive. I test H9B and H10B using the following regression in which Analysts and MTB are included both separately and simultaneously: 7 Note that the lowest number of analysts in the sample will be one since an ―unexpected earnings‖ variable could not have been calculated without at least one analyst estimate. 26 MODEL: VolumeDiff = B0 + B1Analysts + B2MTB + B3AbsoluteUEDiff + E Where: VolumeDiff = [(Average non-summer announcement-period trading volume) – (Summer announcement-period trading volume)] by firm-year Analysts = the decile rank of the number of analysts making an EPS estimate for the summer announcement in a given year MTB = the decile rank of the market-to-book ratio measured using the closing price 11 trading days before the announcement and measured using book value at the beginning of the fiscal year AbsoluteUEDiff = [(Mean of the absolute values of non-summer unexpected earnings) – (Absolute value of summer unexpected earnings)] by firm-year; In my test of H9B and H10B, I attempt to control for the difference in unexpected earnings because this may affect trading volume. Since the ability of an earnings surprise to catch an investor’s attention might be increasing in the absolute value of unexpected earnings for both positive and negative surprises, I use the summer vs. non-summer difference in absolute value of unexpected earnings (measured as |UEnon-summer| - |UEsummer|) as a control variable. The summer absence of noise traders should most greatly affect the stocks with a large analyst following and/ or market-to-book ratio, causing these stocks to experience the largest difference between summer and nonsummer announcement-period trading volume. Therefore, consistent with H9B and H10B, I expect that the coefficients on both Analysts and MTB will be significantly positive. 27 CHAPTER 5 RESULTS 5.1 Descriptive Statistics In Table 1, Panel A, I describe the formation of my final sample of 156,122 earnings announcement observations from 1990 – 2009 (26,413 observations from the pre-online period; 129,709 from the online period). Panel B, I present a correlation matrix. For all variables, the Pearson and Spearman correlation coefficients are qualitatively similar. of the {-10,-1}, {0,2} , {3,50}, In and {3,75} windows CAR’s over each are significantly correlated with UE (CAR’s {-10,-1} and {0,2} are positively correlated with UE; CAR’s {3,50} and {3,75} are negatively correlated with UE). In addition, all CAR’s are significantly positively correlated with each other, with the exception of CAR{-10,-1} correlated with correlation may each be and CAR{0,2} other. the institutional-investor Among result of buying a in being many explanations, possible the significantly direct negatively this negative relationship pre-announcement between period and institutional-investor selling in the announcement period 5.2 Analysis 5.2.1 Test of H1. Table 2 provides descriptive statistics of variables under consideration. difference in observations. the In addition, it shows the results of a test of the mean of these variables between summer and non-summer Earnings announcements are attention-grabbing events that spur noise-trader buying regardless of the earnings surprises. It follows that the summer inattention of net-buying noise traders will cause, on average, a less positive Therefore, as price reaction suggested in to H1, all I summer predict earnings that the announcements. mean of summer announcement-period returns will be significantly lower than that of nonsummer returns. To Test H1, I test for a difference in mean of CAR {0, 2} between the summer and non-summer periods. Consistent with H1, the non- summer mean of CAR {0, 2} is significantly higher than summer mean of CAR {0, 2} summer period (.000237 vs. -.001503, p-value= .0002) for the combined sample. This difference remains significant for each subsample of Positive Surprises (.010345 vs. .008169, p-value [...]... stated, the level of inattention on the part of noise traders is relevant only when they have the ability to trade in the first place The innovations of the online period have enhanced this ability My focus is on differences in investor attention between summer and non -summer periods; therefore, I examine whether the effects hypothesized in H1 through H3 are stronger in the online period than in the pre-online... attention total between trading the volume, summer and I use the non -summer announcement periods as a proxy for the difference in noise trader attention I suggest that the greater the difference in summer vs non -summer attention of noise traders (as proxied by the difference in summer vs non -summer announcement- period trading volume), the greater the difference in summer vs non -summer announcement returns. .. define the summer period as the months of the 3rd calendar quarter (July through September) My study focuses on the effect that the summer slowdown has on the reaction to an earnings announcement Therefore, I define an earnings announcement as a summer (non -summer) announcement if the announcement actually took place from July through September (October- June) The summer slowdown is a widespread and consistent... observations the In addition, it shows the results of a test of the mean of these variables between summer and non -summer Earnings announcements are attention-grabbing events that spur noise-trader buying regardless of the earnings surprises It follows that the summer inattention of net-buying noise traders will cause, on average, a less positive Therefore, as price reaction suggested in to H1, all I summer. .. that returns and/ or ERC’s are likely to vary according to the attention levels of noise traders The next section provides a discussion of evidence supporting this view 2.2 Investor Inattention and Delayed Price Response Research affect the announcement suggests size and/ or the reaction composition more of of an the earnings investor announcement audience for can the Doyle and Magilke (2009) examine the. .. predict earnings that the announcements mean of summer announcement- period returns will be significantly lower than that of nonsummer returns To Test H1, I test for a difference in mean of CAR {0, 2} between the summer and non -summer periods Consistent with H1, the non- summer mean of CAR {0, 2} is significantly higher than summer mean of CAR {0, 2} summer period (.000237 vs -.001503, p-value= 0002) for the. .. investors continue to process the earnings information well after the announcement Huo, Peng, attention and can This explanation is supported by the evidence of Xiong (2009), who suggest mitigate the drift that associated increased with noise-trader initial earnings announcement underreaction I build upon the existing literature on investor inattention and PEAD in my study of summer earnings announcements... regardless of the earnings surprises It follows that the summer inattention cause, of net-buying noise traders will on average, positive price reaction to all summer earnings announcements suggested in H1, I predict that the mean of summer less Therefore, as announcement- period returns will be significantly lower than that of non -summer returns 21 a H2 examines how the differential summer reaction may... my prediction that for positive (negative) earnings surprises, the ERC is larger (smaller) for non -summer announcements than for summer earnings announcements 4.5.3 Testing of H7 The presence and magnitude of longer-term postannouncement price drift may vary based upon which group(s) of investors are paying attention at the time of the earnings announcement Institutional- investor inattention may cause... 1985), and trading volume (Bamber et al 1995) following the announcement While much of the literature focuses on identifying firm characteristics that help determine the nature of the market reaction, my study focuses primarily on the characteristics of the audience of investors for the announcement The differential beliefs and behavior of these investors can help dictate the nature of the market response