Intraday Timing of Management Earnings Forecasts: Are Disclosures after Trading Hours Effective?

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1 Intraday Timing of Management Earnings Forecasts: Are Disclosures after Trading Hours Effective? Soo Young Kwon* Korea University Mun Ho Hwang Korea University Hyun Jung Ju Korea University * Corresponding author sykwon@korea.ac.kr Please do not quote without permission September 2009 Keywords: Management forecasts, intraday timing, during trading, after the close of trading, overnight return JEL Classifications: G15; L11; M41; M49 Data Availability: The data are available from the KIS Value database. We would like to thank seminar participants at Korea University. 1

2 Intraday Timing of Management Earnings Forecasts: Are Disclosures after Trading Hours Effective? Abstract This study examines the behavior of firms with respect to the systematic intraday timing of the release of management earnings forecasts. In particular, this study tests the hypothesis that good news is more likely to be released when the security markets are open, while bad news appears more frequently after the close of trading. We classify good and bad news as management forecasts that are greater or lesser than analysts forecasts, respectively. The results support the good news during and the bad news after hypothesis, similar to the case of earnings announcements. In addition, we examine whether managers engage in intraweek timing of earnings forecasts, but we find no evidence that bad news is released more frequently on Fridays. This study also performs an information content analysis using daily stock price data to examine how differences in the timing of disclosures affects inferences about the magnitude of the response of stock price and the speed of price adjustment. The results suggest that the information disclosed after trading hours is reflected in the opening price during the next trading day. The magnitude of the response of stock prices to the disclosure of forecast information after trading hours is not significantly different from the magnitude of responses during trading hours. This indicates that management actions of disclosing bad news after trading hours in order to mitigate the effects on stock prices are not effective. 2

3 Intraday Timing of Management Earnings Forecasts: Are Disclosures after Trading Hours Effective? 1. INTRODUCTION In this paper, we examine the market wisdom that managers release bad news after the market closes using management forecasts. Several studies have found empirical results that are consistent with this disclosure behavior (Patell and Wolfson 1982; Damodaran 1989; Bagnoli et al. 2005). These studies generally find that managers exercise their discretion in timing the announcements of bad news to reduce media coverage and investor attention at the end of business days and/or on the weekend. However, all these studies focus on earnings and/or dividend announcements. The notable exceptions are the examination of corporate disclosure during trading halts by King et al. (1992) and the documentation of greater shocks in the non-trading period by Baginski et al. (1995). This study employs management earnings forecasts to examine the strategic timing of disclosures associated with the nature of news. We believe that management forecasts are of particular interests, since managers have more discretion in voluntary disclosures. This study differs from King et al. (1992) in that we focus on managers strategic timing in releasing management forecasts while their study investigated corporate disclosures during trading halts. This study is also distinct from Baginski et al. (1995) in that we investigate the existence and effectiveness of strategic timing of voluntary disclosures while their study viewed strategic timing as a way to provide the less-informed with a period for information evaluation. 3

4 Our results do find that the proportion of bad news forecasts that are disclosed after trading hours is significantly higher than the proportion disclosed during trading hours. This provides evidence that managers do exercise intraday timing of earnings forecasts. Furthermore, management forecasting errors that are disclosed after trading hours are smaller than those disclosed during trading hours, but the significant difference disappears after including control variables. This is not consistent with the expectation that discretionary disclosures are likely to be more accurate than non-discretionary disclosures. Biases that occur in management forecasts that are disclosed after trading hours are smaller than those disclosed during trading hours. In addition, management forecasts disclosed during trading hours convey information to the market on the dates of the announcements and the next day. On the other hand, management forecasts disclosed after trading hours have no response on the dates of announcements, and the strongest response occurred on the following trading day. Furthermore, the market responds less to management forecasts when forecasts are less reliable. The magnitude of market response to the announcements released after trading hours is not significantly different from that released during trading hours; this is consistent with the explanation that managers efforts to reduce media coverage and investor attention at the end of business days are not effective. We conduct additional analyses by examining the intraweek timing. Our results find that the proportion of bad news on Fridays is not significantly different from that of Mondays through Thursdays, providing no evidence that managers engage in intraweek timing of earning forecasts. In addition, the frequency of bad news disclosed after trading hours on Fridays is not much different from that after trading hours on Mondays-Thursdays. 4

5 There are several ways in which this paper contributes to the existing body of knowledge. First, this is one of few studies which examine the intraday disclosure pattern and stock price behavior of management forecasts. It is surprising to see most prior studies focused on the announcements of earnings and dividends. Announcement dates of earnings are regular and quite predictable. But, we expect that managers have more discretion in announcing forecasts relative to making announcements of earnings and dividends. Thus, the salient features of market responses to announcements, if any, will be more pronounced with the intraday timing of management forecasts. Second, this study examines whether the opening stock prices of management forecasts disclosed after the close of trading reflect the news contained in the management forecasts. Prior studies investigated how long it took for stock prices to reflect earnings that were disclosed in non-trading periods (Francis et al., 1992). However, no prior studies examined the stock market response to voluntary disclosures such as management forecasts released during non-trading periods. The in-depth analyses of intraday timing of voluntary disclosures allow us to better understand managers incentives and market reactions. Third, this study examines whether managers discretions regarding the timing of releasing management forecasts are effective. The finding that the market efficiently reacts to the information disclosed the day before but after trading hours sheds some light on regulators and market participants. It appears that a manager s discretionary acts of releasing voluntary disclosures after trading hours would not be a concern as long as the price is efficient to the information and is independent of timing of releases. The remainder of the paper is organized as follows. Section 2 reviews the literature on the intraday timing of corporate disclosures and develops our hypotheses. Section 3 5

6 presents our models and explains the measurement of proxies. Section 4 describes the sample and reports our primary test results. Section 5 provides a summary and conclusion. 2. LITERATURE REVIEW AND HYPOTHESES 2.1 Studies on Timing of Corporate Disclosures Several studies document managers preferences to report good news in a timelier manner than bad news. Pastena and Ronen (1979) examined the timing of the firms related press releases and found that management acts as if it tends to delay negative information. Chambers and Penman (1984) found that when reports are published earlier than expected, they tend to have larger price effects than when they are published on time or later than expected. Further, they reported that unexpectedly early reports are characterized by good news, whereas unexpectedly late reports tend to bear bad news, and when firms miss their expected reporting dates, the market interprets this as bad news. Kross and Schroeder (1984) documented a positive association between stock returns and the timeliness of earnings announcements. Specifically, they found that late earnings announcements, relative to the announcement date in the same quarter of the previous year, contain bad news. Bowen et al. (1992) documented that US firms with bad news, announced earnings later than expected while firms with good news announced earnings earlier than expected. They argued that managers have an incentive to minimize the adverse 6

7 reaction of stakeholders to bad news, thus delaying the announcement of bad news. 1 Consistent with these findings, Penman (1987) documented that earnings announcements during the first two weeks of calendar quarters 2, 3 and 4 are more likely to report good news, while earnings announcements later in the quarter are more likely to report bad news. Prior research also documents managers preferences for reporting that good news is more likely to be released while the markets are open while bad news is more likely to appear after the market closes. Patell and Wolfson (1982) and Damodaran (1989) suggested that managers might be trying to reduce media attention and panic selling by announcing bad news when investors have the weekend or overnight to assimilate the news before trading on it. Bagnoli et al. (2005) performed a more recent study (from 2000 to 2003) and continue to find that announcements made on Fridays are more negative, especially for those announced after the market opens. They also found that news announced after the market is closed tends to more negative than announcements made before the market is open. On the other hand, Doyle and Magilke (2009) found no evidence that managers strategically choose to report bad news after the market closes or on Fridays, using firm-level tests. Baginski et al. (1995) provided evidence that managers strategically time larger shock management forecast releases in non-trading periods. They contended that this result is consistent with explanations for voluntary disclosure that rely on a precommitted policy of information asymmetry reduction. They suggested that managers strategically time management forecasts by releasing large unexpected earnings in non- 1 The unexpected time lag was measured as the time lag of the same quarter of last year minus the time lag of this year s quarter. As far as the content is concerned, the news is considered bad when the unexpected return is negative and good when the unexpected return is positive. 7

8 trading periods in order to provide the less-informed trader with a period to evaluate information. However, this study does not address the timing issue documented in prior studies. There is a tendency for bad news to be disclosed in non-trading periods, which seems to indicate that managers are hiding bad news information to minimize price reaction. However, no prior studies tested whether this interpretation is plausible and if so, whether managers efforts are effective. This paper extends prior studies by linking the voluntary disclosure to the intraday timing of disclosures. We compare the market reactions to management forecasts that are released during non-trading hours ( overnight disclosures) with the reactions to management forecasts released during trading hours ( daytime disclosures). Prior studies found that stock prices react within minutes of daytime announcements (Patell and Wolfson, 1984; Jennings and Starks, 1986). There are several reasons to believe, however, that the market responses to overnight and daytime disclosures differ. First, systematic differences in the type of information disclosed during versus after trading hours may affect the speed of security price adjustments. Second, investors may learn about and respond to overnight and daytime disclosures differently. Third, the processes used to set opening and intraday prices at the stock exchanges may differ in terms of the ability to attain equilibrium in prices. We examine the reactions of prices to disclosures that occur overnight and during the day. The reaction of prices to overnight disclosures is measured using the closing price prior to the announcement and the opening price subsequent to the disclosure. We focus on price reactions that occur at the opening of the Korea Stock Exchange immediately following overnight disclosures (the opening reaction), because these represent investors first opportunity to trade on the information that is disclosed 8

9 overnight. We find evidence that investors opening trades reflect information that is disclosed overnight. This result is different from that of Francis et al. (1992) where they document the absence of an opening reaction to overnight earnings announcements. 2 We believe that the discrepancy between our study and theirs is due to the fact that we focus on voluntary disclosures (i.e., management forecast releases) rather than on mandatory disclosures (i.e., earnings announcements). 2.2 Hypotheses Development Firms whose securities are listed on stock exchanges are required to quickly release to the public any news or information that might reasonably be expected to materially affect the market for those securities. The typical way to release corporate data is by means of a press release. Annual earnings and dividend announcements, acquisitions, mergers, tender offers, stock splits, corporate restructuring, and any substantive items of a non-current nature should be handled on an immediate release basis. While ordinary disclosures are made during market trading hours, disclosure of material information can often be made after the market closes. The timing of disclosures can be affected by several factors other than managers discretion. For example, the board of directors meeting is over right before the market closes so that managers can release the boards decisions after trading hours, or news items happened to occur right around the time the market closes. In these random cases, it is not likely that those news items tend to be bad news. However, when managers exercise their discretion as an attempt to minimize exposure, we expect that managers 2 They say that the absence of an opening reaction is due to traders submitting only partial orders at the open. They attribute this behavior to investors reluctance to submit full orders because of their effect on opening prices. 9

10 release bad news after trading hours. Our first hypothesis is stated in the following alternative form: H1 n : Holding others constant, management forecasts released after the market closes are likely to be bad news. Management forecasts are different from earnings announcements since the former is a voluntary disclosure while the latter is a mandatory disclosure. Then, the question is why managers don t withhold earnings forecasts in the first place rather than delaying forecasts when the nature of forecasts is bad. Skinner (1994) suggests that managers have incentives to preempt the announcement of large negative earnings surprises. He contends that this strategy reduces expected legal costs, 3 and that managers may also have reputational incentives to preempt negative earnings news. Given that managers have incentives to disclose bad news in the setting, they do disclose bad news after trading hours when they are more certain about the nature of news. This implies that the news after trading hours is likely to be more accurate than the news during trading hours. This leads to the second hypothesis: H2 n : Holding others constant, management forecasts released after the market closes are more accurate than those released during trading hours. One motivation for releasing bad news after trading hours is to reduce media coverage and investor attention so that the market does not fully reflect the implication of news. Given the tendency for bad news to be disclosed after trading hours, it is necessary to test managers efforts to hide bad news information to minimize price 3 Skinner (1994) provides two reasons managers may bear costs as a result of large negative earnings surprises. First, if the information is disclosed voluntarily, it is more difficult for the plaintiff to argue that the manager withheld information. Second, disclosing early limits the period of nondisclosure, thereby reducing the damages that plaintiffs can claim. 10

11 reaction are effective. We test this by examining whether the market opening price in the following trading day captures the nature of news disclosed after the market closes. H3 n : Holding others constant, management forecasts released after the market closes are reflected on the opening price of the following day. 3. RESEARCH DESIGN 3.1 Timing of Management Forecast Disclosures This study requires us to identify the time when a firm releases its management forecasts. The announcements were obtained from the Data Analysis, Retrieval and Transfer System (DART), which is an electronic disclosure system that operates 24 hours and allows companies to submit disclosures online, where it becomes immediately available to investors and other users. Managers can release disclosures from 7:00 am to 7:00 pm. Therefore, announcements of management forecasts can appear up to two hours before the opening of trading (9:00 am) or as many as four hours after the close of trading (3:00 pm). Most management forecasts appear in news releases of one to four paragraphs in length, and each release or release segment ends with a notation of the time (hour and minute) of the disclosure. Based on this notation of the time, we classify a management forecast release as whether it is disclosed during the weekdays or on the weekend, and whether it is disclosed during trading hours or after the market closes. 3.2 Nature of Management Forecasts The nature of management forecasts is measured as the unexpected component of the management forecast, that is, the extent to which the forecast differs from expected 11

12 earnings. For each observation meeting the selection criteria, a forecast deviation is calculated as the difference between the management forecast and the analyst forecast prevailing prior to the management forecast disclosure (or time-series earnings with a random walk model). Since the latter represents a proxy for expected earnings, the forecast deviation provides an estimate of the unexpected portion of the management forecast. FN it = [MF(X it ) E(X it )] / P it where MF(X it ) = management forecast of earning at t, X it ; E(X it ) = market expectation of earning at t, measured by analysts forecasts or time-series earnings with a random walk model; = Pre-release market value of equity. P it The nature of management forecasts is called good news if the unexpected portion of the management forecast news, FN it, is positive and bad news if it is negative. 3.3 Accuracy of Management Forecasts The accuracy of management forecasts is measured as the difference between forecasted earnings by managers and actual earnings deflated by the stock price in the beginning year. FE it = (MF it AE it )/ P it, where AE stands for actual earnings. The larger FE is, the less accurate management forecasts are. This measure is not known at the time management forecasts and will be found out at the time of earnings announcements. However, the accuracy measure ex ante captures the bias of management forecasts. The positive or negative value FE 12

13 implies that a management forecast was optimistic or pessimistic, and the value close to 0 indicates no forecast bias. The larger FE is, the less reliable the management forecast. Seeing through the bias contained in the management forecast, the market responds less to the biased forecast. Thus, we expect a negative correlation between abnormal returns (AR) at the time management releases forecast and management forecast errors (FE). 3.4 Market Response to Management Forecast Releases The test of the information content of management forecasts is based on an association between abnormal returns and the unexpected component of the management forecast. Information content is measured by the difference between realized and expected stock returns, heretofore, referred to as abnormal returns. Security abnormal returns are estimated using the market model or: AR it = R it ˆ α + ˆ β R ( i i mt ), where R it = return of security i for day t; R mt = equally weighed return of market portfolio for day t; α i, β i = estimated intercept and slope parameters, respectively. Daily abnormal returns for various portfolios are estimated between days -5 and +5 where day 0 refers to the release date of management forecast in the DART system. To ensure that the effect of management forecasts on security prices is not driven by a particular estimation model, we calculate the following daytime return (RD) and overnight returns (RO) respectively, RD RO it o = ( P c it P o it ) / P it, c ( P o it 1 P c it ) / P it, it = + where P c it = close price of security i at t. 13

14 P o it = open price of security i at t; 3.5 Model Specification Model of the accuracy of management forecasts with respect to forecast news and forecast timing To test whether management forecasts disclosed after trading hours are more accurate than those disclosed during trading hours, we employ the following model: Abs_FE it (or FE it ) = δ 0 + δ 1 AFTER it + δ 2 BAD it + δ 3 AFTER it BAD it + δ SIZE it + δ 5 LEV it + δ 6 MBR it + δ 7 HORIZON it + η it (1) where Abs_FE it = absolute value of (MF it AE it ) / P it ; FE it = (MF it AE it ) / P it ; AFTER it = 1 if management forecast of security i is disclosed after trading hours at t and 0, otherwise; BAD it = 1 if FE it < 0 and 0 otherwise; SIZE it = natural logarithm of total assets of security i at the beginning of t. LEV it = total liabilities / total assets of security i at the beginning of t; MBR it = Market value of equity / book value of equity of security i at the beginning of t; HORIZON it = Natural logarithm of number of days between release date of management forecasts and the fiscal year-end date of security i at t Model of the price reaction to the management forecast disclosures with respect to forecast news and forecast timing We estimate the following regression models to test whether the market reaction to the management forecasts disclosed during non-trading hours is smaller than that of the management forecasts disclosed during trading hours. AR FN AFTER BAD it = γ 0 + γ 1FNit + γ 2FNit AFTERit + γ 3FNit BADit + γ 4 it it it 14

15 + γ FN Abs FE + γ SIZE + ε, (2) 5 it _ it 6 it it where AR it = Abnormal returns of security i at t around the announcements of management forecasts; FN it = [MF it E(X it )] / P t ; Abs_FE it = absolute value of (MF it AE it ) / P it ; To avoid the possibility that results are driven by a choice of abnormal return estimation models, we run the regression model (2) by replacing AR it with RD it for management forecasts disclosed during trading periods and RO it for those disclosed during non-trading periods. 4. SAMPLE AND DESCRIPTIVE STATISTICS 4.1 Sample Selection Procedures Panel A of Table 1 reports our sample selection criteria. We obtained management forecast data under the Fair Disclosure from the DART system. Of 1,002 observations, we deleted from the sample if i) forecasts are not for a current period, ii) forecasts include neither sales nor operating income forecasts, iii) forecasts are released during the non-trading period. Our final sample includes 711 observations from 262 firms for years ranging from 2002 to The sample for forecast accuracy analyses consists of 677 after deleting 34 observations due to their being released before trading hours, and the sample for the market study is further reduced to 654 due to lack of return data. [Insert Table 1 here] Panel B exhibits the distribution of sample by year. Overall, each year shows even observations except The small size of observations in 2002 is due to the fact that Regulation Fair Disclosure was implemented in November 2002, so the level of 15

16 voluntary disclosures was relatively not frequent until the Fair Disclosure Rule. Panel C shows the distribution of forecast items- sales forecasts and operating income forecasts. Of the final sample, 501 observations (71.9%) have both sales and operating income forecasts. Of the 697 sales forecasts, 196 forecasts (28.1%) are released without operating income. This is a significantly high proportion compared to operating income forecasts with 14 forecasts (2.7%) that contained no sales forecasts. It appears that sales forecasts without operating income forecasts occur almost three out of ten, while operating forecasts without sales forecasts rarely occur. 4.2 Test of Timing Disclosures of Management Forecasts after the Market Closes In order to test the hypothesis that good news appears during trading hours and bad news is released after trading, we must operationally label each announcement as good or bad news. We employ an analyst s forecast that is most closely available before a management forecast is released as a proxy for market expectation. We use the previous year s earnings if analyst forecasts are not available. News is good or bad if the management forecast is greater or lesser than the market expectation. The tests for systematic disclosure timing policies are direct, two-by-two contingency table comparisons. It is clear from Table 2 that many more announcements appeared during trading than after trading. The question we wish to address is whether the proportion of bad news is significantly higher in the after-trading announcements. Of the 442 management forecasts appearing during trading, only 121 (27.4%) were bad news, while of the 221 releases appearing after the close of trading, 70 (31.7%) were bad news. But, the result is not significant at conventional levels. On the other hand, the results become significant when the nature of news is split into three categories: 16

17 extremely good news, good news and bad news. Furthermore, for operating income forecasts, 105 (31.4%) out of 334 management forecasts released during trading were bad news, while 67 (42.4%) out of 158 forecasts after the close of trading were bad news. The Chi-square statistic for this comparison is 5.675, implying a probability of less than that the underlying likelihood of good and bad news releases is equal for during-trading and after-trading announcements. Similar results are observed for operating income forecasts with tri-categorization. Overall, it appears that managers tend to disclose good news during trading hours and bad news after the close of trading. This disclosure behavior is more pronounced when we focus on extremely good news. Table 3 presents how the news of management forecasts, measured by the difference between management forecasts and analysts forecasts (or last year s actual earnings) and deflated by the pre-release market price, is related to the timing of management forecasts. The mean of sales forecast news released during trading hours is 0.400, significantly greater than 0.232, the mean after the close of trading (t-statistic=2.84). The median of sales forecasts news during trading hours is 0.097, which is also significantly larger than 0.061, the median after the close of trading (t-statistic=2.55). Similar results are observed for operating income forecasts. Taken as a whole, these tests generally support the hypothesis that good news about earnings is more likely to be released during trading, while bad news appears with a higher relative frequency after the market is closed. 5. EMPIRICAL RESULTS 5.1 Intraday Forecast Timing and Ex Post Forecast Accuracy 17

18 This section explains the test that were conducted to see whether intraday timing differences of management forecasts affect the accuracy and bias of management forecasts ex post. The error of management forecasts is the absolute value of the difference between management forecasts and actual earnings standardized by the prerelease market value of equity. The bias is the mere difference between management forecasts and actual earnings that are deflated by the pre-release market value of equity. Panel A of Table 4 shows that the mean and median values of sales forecast errors for the forecasts disclosed during trading are and 0.129, respectively, which are significantly higher than and 0.111, the forecast errors after the close of trading. It appears that management forecasts released after the close of trading are more accurate than those during trading. In addition, Panel A also documents that the mean and median values of sales forecast biases during trading are and 0.044, respectively, which are significantly higher than and 0.009, the forecast biases for the forecasts disclosed after the close of trading. This indicates that management forecasts released during trading are more biased (or optimistic) than those released after the close of trading. The results of accuracy and bias of management sales forecasts varying with the timing of disclosures remain valid for operating income forecasts. The significant differences in univariate analyses could be due to the omitted variable problem if the timing of disclosures is correlated with the variables associated with forecast accuracy and bias. To allow the effect of potential factors that affect forecast accuracy and bias, we create multivariate regression models. The descriptive statistics of variables are presented in Table 5. The Abs_FE variable, measured by the absolute value of forecast errors, reflects the accuracy of management forecasts. The larger the Abs_FE, the less accurate the management forecast. The FE variable, measured by the 18

19 standardized forecast errors, reflects the bias of management forecasts. The larger the FE, the more biased the management forecasts. The mean (median) of Abs_FE is (0.124), indicating 39% of forecast errors, while the mean (median) of FE is (0.032), exhibiting 11.1% of forecast bias. The AFTER variable shows 33.2% of management forecasts were released during nontrading periods. The mean value of BAD, indicates that 28.8% of management forecast is classified as bad news, implying that management forecasts are optimistically biased. Columns 2 and 3 in Panel A of Table 6 present regression results for accuracy of the sales forecast associated with the timing of forecast disclosures. With no inclusion of control variables, the coefficients of both AFTER and BAD variables are significantly negative. But, with the inclusion of control variables, the coefficient of AFTER becomes insignificant while that of BAD remains significant. So the difference in the accuracy of forecasts varies with the nature of news, and not with the timing of disclosures. As shown in Panel A, this observation still holds for operating income forecasts. Columns 2 and 3 in Panel B of Table 6 present regression results for the sales forecast bias associated with the timing of forecast disclosures. With no inclusion of control variables, the coefficients of both AFTER and BAD variables are significantly negative. But, with inclusion of control variables, both coefficients of AFTER and BAD variables become insignificant. As is shown in Panel B, this observation still holds for operating income forecasts. So the difference in the bias of forecasts varies with neither the nature of news nor the timing of disclosures; this is different from the results contained in the univariate analyses. 19

20 5.2 Market Responses to Disclosures of Management Forecasts after the Market Closes In order to demonstrate how intraday announcement timing differences might affect research designs that use daily stock price observations, we conducted an information content analysis of our management forecasts sample. We estimate each firm s intercept and slope coefficients for the market model using ordinary least-squares regression of the 250 daily returns for the preceding day 5. If fewer than 150 observations were available, the announcement was discarded. These estimates were then used to compute prediction errors (i.e., estimated residuals in the test period) for days 5 to +5. In order to avoid contaminating the results by using the imperfect expectation model through the canceling out of improperly classified price changes, we squared the prediction error and standardized it by using the estimated residual variance during the estimation period as follows: u it = R it (α i + β i R mt ), t= 5,,+5 Squared standardized prediction error ψ it is computed as u 2 it / s 2 i. Estimated residual variance s 2 i is Σ ε 2 it / (T 2), where ε it is the estimated residual in the pre-announcement period and T is the number observations in the pre-announcement period. This statistic is used to analyze the response of prices to management earning forecasts. Under the null hypothesis which contains no information content, the expected value of ψ it is slightly greater than 1, due to both the characteristics of the F distribution and to the increase in residual variance when predicting outside the estimation period. For the value of T used in these computations, the expected value is approximately

21 Several interesting features are apparent in the numerical results presented in Table 7. As the second column indicates, the average response for sales forecasts during trading hours is This represents the second largest response for sales forecasts on the release date from the DART system. Column five indicates that the largest average response (1.811) for operating income forecasts during trading occurred on the release date. However, columns three and six present only announcements that appeared after trading and demonstrate that the strongest response occurred on the following trading day. It appears that management conveys new (or inside) information about earnings through its forecasts to the market at the time of forecast disclosures, usually during trading hours. But, management forecasts released after the close of trading are reflected in the next trading day. Panels A and B of Figure 1 depicts ψ i for days from 5 to +5. They clearly show peaks on the following trading day for after-trading forecast releases. 5.3 Magnitude of returns to management forecasts: During trading vs. after the close of trading Table 8 presents descriptive statistics of market return variables. The AR 0 variable represents abnormal returns at t=0 estimated from the market model. The Adj_AR 0 variable stands for the adjusted abnormal returns by replacing abnormal returns at t=0 by those at t+1 for management forecast disclosures that occur after the close of trading. We also calculate daytime returns (RD 0 ) and overnight returns (RO 0 ) at t=0 to eliminate the possibility that empirical results are driven by a particular estimation model. The Adj_R 0 variable reflects the adjusted overnight returns by replacing overnight returns at t=0 by those at t+1 for the management forecasts released after the close of trading. It appears that the distribution of Adj_AR 0 is quite similar to that of AR 0. It is also striking 21

22 to see that RO 0 and Adj_R 0 exhibit similar distributions. Sales forecasts and operating income forecasts are strikingly similar in the distributions of variables. We also include α and β, parameter estimates from the market model. Table 9 reports the estimation of market responses to the disclosures of management forecasts. Market responses are defined as AR 0, AR 1 and Adj_AR 0. For the dependent variable AR 0, the coefficient of forecast news variable FN is 0.011, significantly positive at the 1% level. Similar results are observed for the dependent variables AR 1 and Adj_AR 0. These indicate that good news forecasts are associated with significant positive abnormal returns on the days that management forecast disclosures, and a significant positive association between the magnitude of forecast deviation and the magnitude of abnormal returns. One interesting feature is that the coefficient of FN AFTER is significantly positive at the 1% level for the dependent variables of AR 1. This illustrates that the market captures the forecast news disclosed after the close of trading- on the following day. The significance in the coefficient of FN AFTER for the dependent variables of Adj_AR 0 disappears since the abnormal returns are adjusted by replacing abnormal returns at t=0 by those at t+1 for the management forecast disclosures after the close of trading. The coefficient of FN BAD is significantly negative at the 1% level for the dependent variables of AR 0 and Adj_AR 0. This indicates that the market responds to bad news weaker than good news; this is not consistent with the result in Skinner (1994). The coefficient of FN AFTER BAD is positive but insignificant, consistent with the interpretation that the magnitude of market response to bad news after the close of trading is not different from the magnitude to bad news during trading. It appears that 22

23 management s intention (or effort) to reduce the impact of bad news on the stock price by disclosing bad news after the close of trading is not effective. The coefficient FN Abs_FE is with a t-statistic of -2.65, which is significantly negative at the 1% level. This implies that the smaller a forecast error is, the larger the market response to the nature of the management forecast. This is consistent with the study by Pownall and Waymire (1989) and Hutton et al. (2003) which documented that the market response to management forecasts is stronger when the forecasts are more credible. The forecasts of operating income exhibit similar results except for the FN AFTER variable. Its coefficient is , significantly negative at the 5% level. Given that the coefficient of FN is 0.073, the net effect of management forecasts after the close of trading is close to zero, suggesting that there was no market response to the management forecast that was released after trading hours. The SIZE coefficient is significantly negative, implying smaller responses for larger firms. This is consistent with the explanation that information asymmetry is negatively associated with the market reaction to disclosed information. 6. ADDITIONAL ANALYSES 6.1 Magnitude of price changes to management forecasts: During trading vs. after the close of trading We calculate price changes to the disclosure of management forecasts to avoid the possibility that the results of market tests are driven by a particular estimation model. Furthermore, we examine whether the opening stock price on the next day reflects the implications of management forecasts released after trading hours. As is shown in Table 23

24 10, for the dependent variables of RD 0 and Adj_RD 0, the coefficients of forecast news variable are all significantly positive, indicating that good news forecasts are associated with significant positive abnormal returns on the days which management provides forecasts disclosures. The coefficient of FN AFTER is not significant for the dependent variable of RD 0, but significantly positive for the dependent variables of RO 0. The coefficient FN also shows no statistical significance. These results show that opening stock prices reflect the forecasted news that was disclosed after the close of trading on the following day. The coefficient FN BAD is significantly negative at the 1% level for the dependent variables of RO 0 and Adj_R 0. This indicates that the market response to bad news is weaker than it is to good news. The coefficient FN AFTER BAD is positive but insignificant. This is consistent with the interpretation that the magnitude of market response to bad news after the close of trading is not different from that during trading. This suggests that managers disclosure of bad news during non-trading periods is not effective in minimizing price reaction to bad news. The coefficient FN Abs_FE is significantly negative at the 1% level, implying that the smaller a forecast error is, the larger the market response to the nature of management forecasts. This confirms that less noisy information gains a higher market response to released information. Overall, operating income forecasts exhibit similar results. 6.2 Test of Timing Disclosures of Management Forecasts on the Weekend 24

25 Table 11 presents the intraweek timing of management forecast releases. Panel A of Table 11 presents the distribution of management sales forecasts across days of the week along with the nature of news. The proportion of good news ranges from 67.9% to 77.8%, illustrating that management forecasts tend to be optimistically biased. As shown in Panel B, the proportion of good news becomes lower for operating income forecasts. Neither sales forecasts nor operating income forecasts provide any evidence that the frequency of management forecast releases that occur after trading hours is higher on Friday than on Monday through Thursday. To test whether there is more bad news after the market closes on Friday, Panel C presents the proportion of bad news that arises from management forecast releases with respect to both intraday and intraweek timings. The results also show no clear indication that managers exercise their discretion in management forecasts by releasing bad news more frequently after trading hours on Friday than during the week. [Insert Table 11 here] Panel A of Table 12 illustrates the distribution of management sales forecasts across days of the week as well as the times of release (before, during or after trading). As shown, Friday was a relatively unpopular day for management forecasts, containing 17.2%, and Monday comes next with 18.8%. But, the difference appears to be insignificant. On the other hand, 33.3 % (40 out of 120) of the Friday sales forecasts appeared after trading hours, compared to an average of 31.4% (181 out of 577) for Monday through Thursday. Panel B presents the distribution of management forecasts of operating income across the days of the week as well as the times of the release. The distribution shows a pattern similar to sales forecasts. Panel C conducts frequency tests, 25

26 indicating no significant results. It appears that management forecasts were more uniformly distributed across the days of the week and times of release, compared to earnings announcements in previous studies (e.g., Patell and Wolfson 1982). [Insert Table 12 here] 6.3 Test of Timing Disclosures to Reduce Information Asymmetry Baginski et al. (1995) suggest that managers strategically time management forecasts by releasing greater shocks in non-trading periods in order to provide the lessinformed trader with a period for evaluating information. To see whether management forecast disclosures that occur after trading hours are motivated by a pre-committed policy of information asymmetry reduction, we test the association between the magnitude of news and the timing of disclosures. Panel A of Table 13 shows that the magnitude of forecast news during trading hours is larger than that the magnitude which occurs after trading hours. This is not consistent with the explanation that managers hold information to allow investors to have time to analyze large shocks. Panel B provides the results of regressing the absolute value of news with respect to the disclosure timing dummy OPEN and firm size. The coefficient of OPEN is insignificant, indicating that the disclosures of management forecast after trading hours are not motivated by a firm s intention to reduce information asymmetry among investors. 7. CONCLUSION This study examines managers behavior with respect to the systematic intraday timing of earnings forecasts. In particular, we test the hypothesis that bad news appears 26

27 more frequently after the close of trading. Empirical results do not support intraweek timing disclosure patterns, but provide evidence that the likelihood of bad news disclosures by management forecast increases after the close of trading for the day. The relative proportion of disclosures of good news management forecasts was significantly higher during trading than after trading. Price changes were more likely to be positive for releases that occurred duringtrading, while there was a marked shift toward negative price changes for disclosures released after-trading. This stock price response may contribute to an interpretation of the systematic behavior as an attempt to reduce the public exposure of unfavorable events. The magnitude of stock price response to the disclosure after trading hours is not significantly different from that during trading hours. This indicates that managers exercises to disclose bad news after trading hours in order to mitigate the effects on stock prices are not effective. This study contributes to the existing literature by testing whether managers strategically time voluntary disclosures and, if so, whether investors correctly react to those disclosures in a timely manner. The effectiveness of strategic timing was not examined in the U.S. and many other countries. It would be interesting to replicate the structure of the research design in this study in other countries to see how strategic timing of voluntary disclosure and market response differ across countries. 27

28 REFERENCES Baginski, S., J. Hassell., and D. Pagach Further Evidence on Nontrading-Period Information Release. Contemporary Accounting Research 12(1): Bagnoli, M., M. Clement, and S. Watts Around-the-Clock Media Coverage and the Timing of Earnings Announcements. Working Paper, University of Texas at Austin (December). Bowen, R.M., M.F. Johnson, T. Shevlin, and D. Shores Determinants of the Timing of Quarterly Earnings Announcements. Journal of Accounting, Auditing, and Finance (Fall): Chambers A. E. and S. H. Penman Timeliness of Reporting and Stock Price Reaction to Earnings Announcements. Journal of Accounting Research 22(1): Damodaran, A The Weekend Effect in Information Releases: A Study of Earnings and Dividend Announcements. The Review of Financial Studies 2(4): Doyle, J.T. and M. Magilke The Timing of Earnings Announcements :An Examination of the Strategic Disclosure Hypothesis. The Accounting Review 84(1): Francis, J., D. Pagach, and J. Stephan The Stock Market Response to Earnings Announcements Released During Trading versus Nontrading Periods. Journal of Accounting Research 30: Hutton, A., G. S. Miller, and D.J. Skinner The Role of Supplementary Statements with Management Earnings Forecasts. Journal of Accounting Research 41(5) : Jennings, R., and L.Starks Earnings announcements, stock price adjustments and the existence of option markets, Journal of Finance41(1): King, R., Pownall,G.,and Waymire, G Corporate disclosure and price discovery associated with NYSE temporary trading halts, Contemporary Accounting Research 8(2):

29 Kross, W., and D. A. Schroeder An Empirical Investigation of the Effect of Quarterly Earnings Announcement Timing on Stock Returns. Journal of Accounting Research.22(1): Pastena, V., and R. Ronen Some Hypotheses on the Pattern of Management s Information Disclosure. Journal of Accounting Research, 17, Patell, J.M., and M. Wolfson Good News, Bad news, and the Intraday timing of Corporate Disclosures. The Accounting Review 57(3): Penman, S The Distribution of Earnings News Over Time and Seasonalities in Aggregate Stock Returns. Journal of Financial Economics 18 (2): Pownall, G., and Waymire,G Voluntary Disclosure Credibility and Securities Prices: Evidence from Management Earnings Forecasts, Journal of Accounting Research 27(2): Skinner, D Why firms voluntarily disclose bad news, Journal of Accounting Research, 32:

30 Table 1 Sample Selection Criteria and Characteristics of Forecast Sample Panel A Sample Selection Criteria Observations with management forecasts of earnings under Reg. FD for the period of November 2002 and December ,002 Exclude: Observations with management forecasts beyond one year ahead 90 Observations with neither sales forecasts nor operating income forecasts 194 Observations with management forecasts disclosed during non-trading days 7 Sample for the intraday timing analysis 711 Exclude: Observations with management forecasts disclosed before trading hours 34 Sample for the forecast accuracy analysis 677 Exclude: Observations with missing return data 23 Sample for the market reaction analysis 654 Panel B Sample for the Intraday Timing Analysis by Forecast Year Year Number % of total Total Panel C Sample for the Intraday Timing Analysis by Disclosed Items Disclosed Items Sales Operating income Total Both items disclosed 501 (71.9%) 501 (97.3%) 501 (70.5%) Only sales forecast disclosed 196 (28.1%) (27.6%) Only operating income forecast disclosed - 14 (2.7%) 14 (1.9%) Total

31 Table 2 Frequency of Management Forecasts by Intraday Timing and Nature of Forecast News Panel A Frequency of Sales Forecasts by Intraday Timing and Nature of Forecast News Forecast news 1) N During After Freq. (%) Freq. (%) χ 2 (Prob.) Good news 2) (72.6) 151 (68.3) Bad news 3) (27.4) 70 (31.7) (0.249) Total (100.0) 221 (100.0) Extreme good news 4) (54.6) 55 (39.9) Bad news 4) (45.4) 83 (60.1) (0.004) Total (100.0) 138 (100.0) Panel B Frequency of Operating Income Forecasts by Intraday Timing and Nature of Forecast News Forecast news 1) N During After Freq. (%) Freq. (%) χ 2 (Prob.) Good news 2) (68.6) 91 (57.6) Bad news 3) (31.4) 67 (42.4) (0.017) Total (100.0) 158 (100.0) Extreme good news 4) (54.5) 44 (40.7) Bad news 4) (45.5) 64 (59.3) (0.019) Total (100.0) 108 (100.0) 1) Forecast news (FN) = (Management forecast market expectation of earnings) prerelease market value of equity. We measure the market expectation of earnings by analysts forecasts or random-walk expectation if analysts forecasts are not available. 2) Good news if FN > 0. 3) Bad news if FN < 0. 4) Forecast news is classified into three groups- extreme good news, good news, and bad news. 31