Conditional event-study analysis of issue anticipation and issue announce- ment returns

sample are small and may experience light trading on the event date; consequently, some of the price adjustment may not be observed until the following day. For firm i on day t, the market-adjusted abnormal returns are computed by subtracting the AmarketB returns R from the returns of the individual stock R , m t i t i.e., AR s R y R . 12 Ž . i t i t m t For NYSE rAMEX stocks, we use the return to the CRSP NYSErAMEX equal-weighted portfolio as our proxy for the market return. For NASDAQ stocks, our proxy for the market return is the return to the CRSP NASDAQ equal-weighted portfolio. We deleted the December 8, 1992 issue announcement by TSS. This observation had an abnormal return of 259, and had substantial influence on some of the test statistics. We use market-adjusted returns rather than market model prediction errors in most of our tests. This is because price run-ups in the periods before splits and equity issues can bias estimates of the market model parameters. According to Ž . Brown and Warner 1985 , event studies with market-adjusted returns have comparable power to procedures that use the market model. To insure that our conclusions about asymmetric information are not driven by our measure of abnormal returns, we confirm our major findings using market model prediction errors.

5. Conditional event-study analysis of issue anticipation and issue announce- ment returns

In this section, we first develop an innovative probit regression to analyze factors that predict equity issues. Then we test whether the announcements of stock splits, dividends, and earnings affect the uncertainty about managers’ private information. 5.1. Predicting equity issues We assume that the choice to issue equity is made in two stages. First, the firm decides whether or not to raise external funds. Second, firms that have decided to raise external funds determine whether or not to raise equity. The probability that a firm will issue equity equals the probability of external financing multiplied by the conditional probability that equity will be issued if the firm uses external financ- ing. This can be expressed Prob Issue Equity s Prob External Financing Ž . Ž . = Prob Issue Equity N External Financing . 13 Ž . Ž . We assume that Prob External Financing s F x X b Ž . Ž . 1 i 1 and Prob Issue Equity N External Financing s F x X b . 14 Ž . Ž . Ž . 2 i 2 Where x is a vector of variables that predict a firm’s decision to use external 1 i financing, x is a vector of variables predicting that equity financing will be 2 i chosen by firms that intend to raise external funds, and b and b are vectors of 1 2 parameters. The probability that a firm will issue equity, then, is simply Prob Issue Equity s F x X b = F x X b . 15 Ž . Ž . Ž . Ž . 1 i 1 2 i 2 This model, which we call a Acompound probitB, can be estimated using maxi- mum likelihood. Next, we look at the variables that predict external financing. 5.1.1. Variables associated with external financing A firm’s external financing requirements are determined by its planned invest- ment, cash on hand, and expected internal cash flow. To establish this relation, we begin with the following balance sheet identity DCurrent Assets q DFixed Assets s DLiabilitiesq DEquity, 16 Ž . where the symbol D means change. Because the change in fixed assets equals net Ž . capital expenditures minus depreciation, and net working capital Net WC equals Ž . current assets minus current liabilities, Eq. 16 can be expressed Net Capital Expenditures y Depreciation q DNet WC s DDebt q DEquity, Ž . 17 Ž . where DDebt represents the change in long-term debt. Defining Investment as net capital expenditures plus additions to Net WC gives us Investment y Depreciations DDebt q DEquity. 18 Ž . The change in long-term debt and equity equals the change in external financing plus additions to retained earnings, so that Investment y Depreciations Additional external financing q Net income y Dividends. 19 Ž . Ž . Rearranging Eq. 19 gives Additional external financing s Investmenty Internal Cash Flow ICF , Ž . 20 Ž . where ICF s Net income q Depreciationy Dividends. This definition of internal cash flow is equivalent to operating cash flow minus interest and dividends. It is the portion of operating cash flow that is actually available for investment. Because a firm can use all of its excess cash in its investments, the external financing required for a given level of investment is External funds needed EFN s Investmenty Internal Cash Flow ICF Ž . Ž . y Excess Cash. 21 Ž . At time t, the expected external fund requirement for the next s periods equals the expected investment over this horizon, minus the expected internal cash flow over this period, less the excess cash available at time t E EFN s E Investment y E ICF y Excess Cash . 22 Ž . Ž . Ž . Ž . t t , t qs t t , t qs t t , t qs t Ž . If E EFN is small relative to a firm’s assets in place, the firm could avoid t t, t qs issuing new securities by making minor changes to the magnitude and timing of the investment, by using the firm’s credit line, or by finding ways to boost its internal cash flow. If external financing needs are high relative to the firm’s size, the firm will not be able to avoid external financing without forgoing profitable investments. This suggests that the benefit from issuing securities is a positive Ž . Ž . function of the ratio of E EFN to the book value of assets B . t t, t qs t Ž . To estimate E EFN , we need to get a proxy for expected investment and t t, t qs expected internal cash flow. We assume that expected investment is proportional to the present value of the firm’s growth opportunities, which approximately Ž . Ž . equals the difference between the market value M and the book value B of a t t firm. We further assume that the expected internal cash flow over the next s periods is proportional to the cash flow from the most recent year, i.e., E Investment s k M y B , 23 Ž . Ž . Ž . t t , t qs t t and E ICF s mICF , 24 Ž . Ž . t t , t qs t y1 , t where k and m are constants. Because we are interested in predicting future Ž . internal cash flow, we omit extraordinary income from ICF in Eq. 24 . t y 1, t Assuming that a firm’s working cash requirement equals a fraction n of total assets, the excess cash equals cash and cash equivalents minus n times total assets Excess cash s Cash y nB . 25 Ž . t t t This implies that the expected external financing needed is approximately, E EFN s k M y B y mICF y Cash q nB . 26 Ž . Ž . Ž . t t , t qs t t t y1 , t t t Ž . As discussed above, a firm’s proclivity to issue is a function of E EFN rB . t t, t qs t Ž . Dividing both sides of Eq. 26 by B gives t E EFN rB s a q a M rB q a ICF rB q a Cash rB , Ž . Ž . Ž . Ž . t t , t qs t 1 t t 2 t y1 , t t 3 t t 27 Ž . where a s n y k, a s k, a s ym, and a s y1. This simple model suggests 1 2 3 that variables M rB , ICF rB and Cash rB are associated with the probability t t t -1, t t t t that a firm raises external capital. Next, we consider variables related to a firm’s choice of equity versus debt. 5.1.2. Variables associated with the choice of equity financing A firm needing external funds could rely on bank borrowing, bond issues, or Ž . other alternatives to equity. Marsh 1982 and others use the relation between current and historical debt ratios to predict a firm’s choice of debt or equity. We assume that a firm needing external funds is more likely to issue equity when its Ž . debt ratio is above its target level. We measure a firm’s debt ratio DEBT as the book value of total liabilities divided by the sum of the book value of liabilities Ž . and the market value of equity. We measure the target debt ratio TDEBT as the Ž debt ratio a year earlier. We get comparable results when we use the industry average debt level as the target, or when leverage is measured as total liabilities . divided by total book value of assets . Ž . In addition, the model of Lucas and McDonald 1990 suggests that the stocks of issuing firms have large positive abnormal returns before equity issues. We use a firm’s continuously compounded stock return over the preceding 250 trading Ž . days RET250 to measure the increase in share price before an issue. RET250 essentially shows how the firm’s equity is priced relative to its pricing a year Ž . earlier. Moreover, Choe et al. 1993 argue that firms are less likely to issue equity when the stock market is more volatile. We use the standard deviation of the equal-weighted CRSP index during the preceding 250 trading days as a measure of Ž . the volatility of equity markets STDMKT . Our final set of variables measures the asymmetric information between a firm’s managers and its outside investors. Recall that the ultimate goal of our procedure is to distinguish between anticipation and asymmetric information. In order to do this, all variables that we test in the second stage regression must be included in the first stage probit as well. The first stage controls for anticipation; the second tests for asymmetric information. We also have another reason for including asymmetric information variables at this point. Asymmetric information affects the public’s reaction to the equity offering, depressing the price the firm realizes from the sale of its shares. Firms should consider this as they choose between issuing debt and equity. Because asymmetric information reflects the difference between private and public information, asymmetric information tends to be lower for firms with more public information. Because larger firms have more news coverage and are followed more closely by analysts, we assume that asymmetric information decreases at a decreasing rate with a firm’s market value of equity. Consequently, Ž . we use the log of market value of equity Lmveq , to control for differences in asymmetric information that are due to differences in the amount of public information. Aside from differences arising from size, there is greater valuation uncertainty about firms with more growth opportunities. This suggests that asym- metric information is higher for firms with higher market-to-book ratios. The market-to-book ratio is already included as a variable in the compound probit from the model that predicts external financing, so we do not include it a second time with other asymmetric information variables. Finally, we include the standard Ž . deviation of market adjusted returns Stdret as a more direct measure of a firm’s valuation uncertainty. This standard deviation is estimated from the time-series of abnormal returns from days y127 to y2 relative to the issue announcement. Next, we look at information releases that may reduce asymmetric information. Ž . Korajczyk et al. 1992 suggest that managers can reduce the information asymmetry prior to an equity issue by releasing information before announcing the issue. To test whether split declarations reduce asymmetric information, we include two split-related variables, D and Sdays, in our analysis. D is a split split dummy variable that equals one if a split declaration occurs within 250 trading days prior to the issue announcement, and zero otherwise. Sdays is the number of trading days between the issue announcement and the preceding split declaration. Ž . Korajczyk et al. 1991 find evidence that the issue announcement return is negatively related to the time since the preceding earnings release. This suggests that earnings releases reduce asymmetric information. To control for this effect, we include the number of days between the issue announcement and the preceding earnings release, Edays, in our regression. Ž . Fama et al. 1969 present evidence that stock splits are associated with dividend increases. To control for dividend changes around equity issues and to test for a relation between dividends and asymmetric information, we include three Ž . indicator variables D , D , and D , similar to the ones in Loderer divplus divneg divzero Ž . and Mauer 1992 . D equals one if the firm increased its dividend at the last divplus dividend declaration before the issue announcement, and zero otherwise. D divneg equals one if the firm decreased dividends before the issue announcement, and zero otherwise. D equals one if the firm left its dividend unchanged before divzero the announcement, and zero otherwise. To test for the effect of the timing of the dividend declarations around the issue announcement, we add another variable, Ddays, which equals the number of days between the issue announcement and the preceding dividend declaration. If the firm did not declare a dividend in the 250 days before the issue, then all four of the dividend variables are set to zero. In summary, the asymmetric information variables are Lmveq, Stdret, M rB, D , Sdays, Edays, D , D , D , and Ddays. Other publicly observ- split divplus divneg divzero Ž . able variables that help investors to anticipate equity issues x include internal i cash flow to assets, and cash to assets ratios, the change in the debt ratio, the cumulative stock return over the preceding 250 days, and the standard deviation of the market return. All financial ratios are computed from Compustat data for the most recent fiscal year that ends before the date of the observation. 5.2. Estimating the probability of equity issues The first stage in our event study will estimate p , the probability that a firm i Ž . intends to issue equity, using the compound probit model given in Eq. 15 . This Ž . Ž . requires AissuingB Õ G 0 and Anon-issuingB Õ - 0 observations. To get our i i issuing sample, we begin with the sample of 1509 issue announcements described in Section 4.1. Of these, 589 observations had insufficient CRSP or Compustat data, leaving 892 issuing observations. To get our non-issuing observations, we used a random number generator to randomly select 15,000 security-days from the CRSP NASDAQ and NYSE rAMEX files from 1980 to 1994. We deleted from this random sample all observations for which there was an SDC equity issue in the subsequent 250 trading days, and observations with insufficient CRSP and Compustat data. We were left with 5696 observations in our non-issuing sample. Table 1 tabulates the sample statistics for the variables included in the probit regression. The means and standard deviations are reported for the 892 issuing and 5696 non-issuing observations separately. We report t-statistics for differences across these groups. Because t-statistics would not reflect how Sdays interacts with D , or how Ddays interacts with the dividend dummies, we do not report split t-statistics for Sdays or Ddays. From the t-statistics of Lmveq, Stdret, M rB, and ICFrB, we see that issuing firms in the sample tend to be larger, safer firms with greater growth opportunities, and less internal cash flow. The t-statistics of RET250 and STDMKT show us that firms tend to issue after their stocks have had large price run-ups and during periods when the stock market is less risky. The t-statistics for D and Edays are split preliminary evidence that firms are more likely to issue stock after declaring splits and shortly after earnings releases. Finally, the statistics for DEBT–TDEBT shows that firms issuing stock are more likely to have had their debt-to-market value ratios fall slightly over the previous year. These univariate statistics, however, do not control for the correlation between DEBT–TDEBT and other relevant vari- ables. In particular, the 43.5 mean run-up in share price before equity issues increases the denominator of the debt-to-market value ratio, decreasing the debt ratio of the issuing firms. Table 2 presents the results of our compound probit regression. All of the coefficients have the signs predicted by the model developed in Section 5.1, although some are not statistically significant. From the coefficients of Lmveq, Stdret, Cash rB and ICFrB, we see that equity issues are more likely to occur for larger, safer firms with less cash and less internal cash flow. The coefficient of RET250 provides further evidence that firms tend to have large run-ups in share prices before issuing equity. After controlling for these other variables, the L. Guo, T.S. Mech r Journal of Empirical Finance 7 2000 113 – 141 130 Table 1 Sample statistics for the 892 issuing and 5696 non-issuing observations included in the probit regression: 1980–1994 Equity-issuing sample Non-issuing sample t-Statistic for the difference in mean Mean Median Standard Mean Median Standard deviation deviation Variables predicting external financing a M rB s market-to-book of assets 1.95 1.26 2.27 1.65 1.23 1.51 5.07 a ICF rB s internal cash flow divided by assets 0.019 0.053 0.255 0.044 0.069 0.166 y3.90 Cash rB s Cash divided by assets 0.116 0.038 0.186 0.126 0.060 0.162 y1.56 Variables predicting preference for equity oÕer debt a DEBT y TDEBT s market debt ratio minus debt ratio in previous year y0.011 y0.005 0.116 0.009 0.003 0.112 y4.97 a RET250 s Stock return over previous 250 trading days 43.5 37.4 47.5 3.7 6.8 48.7 22.76 a STDMKT s standard deviation of CRSP daily equal-weighted index 0.561 0.530 0.15 0.586 0.505 0.25 y2.88 Split declaration Õariables a Dsplit s 1 if split declared in preceding 250 days, and 0 otherwise 0.157 0.364 0.090 0.286 6.21 Sdays s number of days since split declaration if Dsplit s 1, 13 40 12 43 and zero otherwise Earnings release Õariable a Edays s number of days since earnings release 32 25 31 39 31 37 y5.31 DiÕidend announcement Õariables D s 1 if dividend increased in preceding 250 days, and 0 otherwise 0.114 0.318 0.099 0.299 1.38 divplus D s 1 if dividend decreased in preceding 250 days, and 0 otherwise 0.014 0.120 0.023 0.150 y1.63 divneg D s 1 if dividend did not change in preceding 250 days, 0.391 0.488 0.373 0.484 1.02 divzero and 0 otherwise Ddays s number of days since dividend announcement if 17 1 30 22 36 D q D q D s 1, and zero otherwise divplus divneg divzero Firm-specific Õaluation uncertainty a Ž . Lmveq s log market value of equity 5.08 4.96 1.57 4.59 4.40 2.11 6.61 a Stdret s standard deviation of firm daily abnormal returns 2.71 2.45 1.50 3.26 2.55 2.65 y5.98 a Significant at the 1 level using a two-tailed test. Table 2 Coefficients from probit regressions of D on the specified variables for 6588 observations from issue 1980 to 1994. D equals one for the 892 observations with issue announcements, and zero issue otherwise. Z-statistics are reported in parentheses. The probit model is specified as follows: Prob Issue Equity s Prob External Financing =Prob Issue Equity N External Financing Ž . Ž . Ž . sF x X b F x X b q ´ , Ž . Ž . 1 1 2 2 where x is a vector of variables that predict external financing, x is a vector of variables that predict 1 2 preference for equity over debt, b and b are vectors of parameters, and ´ is a random error 1 2 Variables predicting external financing Ž . M r B s market-to-book of assets 0.0901 1.55 a Ž . ICF r B s internal cash flow divided by assets y4.772 y7.05 a Ž . Cash r B sCash divided by assets y1.549 y4.64 a Ž . Intercept 0.704 3.90 Variables predicting preference for equity oÕer debt b Ž . DEBT yTDEBT s market debt ratio minus debt ratio in previous year 0.522 2.17 a Ž . RET250 sStock return over previous 250 trading days 1.239 16.72 Ž . STDMKT sstandard deviation of CRSP daily equal weighted index y12.886 y1.04 Split declaration Õariables a Ž . D s one if split declared in preceding 250 days, and zero otherwise 0.656 3.85 split a Ž . Sdays s number of days since split declaration if D s1, y0.0049 y4.16 split and zero otherwise Earnings release Õariable a Ž . Edays s number of days since earnings release y0.0023 y2.88 DiÕidend announcement Õariables Ž . D s one if dividend increased in preceding 250 days, y0.123 y1.18 divplus and zero otherwise Ž . D s one if dividend decreased in preceding 250 days, y0.235 y1.17 divneg and zero otherwise Ž . D s one if dividend did not change in preceding 250 days, y0.049 y0.63 divzero and zero otherwise b Ž . Ddays s number of days since dividend announcement if y0.0025 y2.60 D q D q D s1, and zero otherwise divplus divneg divzero Firm-specific Õaluation uncertainty a Ž . Ž . Lmveq s log market value of equity 0.078 4.33 Ž . Stdret sstandard deviation of firm daily abnormal returns y2.493 y1.40 a Ž . Intercept y1.112 y7.42 2 Pseudo R 0.1344 No. of observations 6588 a Significant at the 1 level using a two-tailed test. b Significant at the 5 level using a two-tailed test. coefficient of DEBT–TDEBT is positive, as predicted by our model. This suggests that firms are more likely to issue equity when their debt ratios are high relative to their targets. Consistent with the hypothesis that managers issue equity when asymmetric information is relatively low, we find that equity issues are more likely shortly after split, earnings, and dividend announcements. In the second stage regression, we will test whether this strategy is effective. The coefficients of the dividend dummies are insignificant, which suggests that the likelihood of a split does not depend significantly on whether the latest dividend change was zero, negative, or positive, after controlling for RET250 and other variables that reflect the firm’s performance. Table 3 gives statistics regarding the fit of the compound probit, after our Ž . corrections for unequal sampling. We describe these corrections in Section 5.3. The cutoff point is the minimum predicted probability for which we would predict an equity issue. Thus, if we use a cutoff of 0.04, we would predict an equity issue if the compounded probit had a predicted value of 0.04 or greater. Using our sample, we report the frequencies of Type I and Type II errors from our probit for different cutoffs. A Type I error occurs when the model does not predict an equity issue that subsequently occurs; a Type II error occurs when the model erroneously Table 3 Frequencies of Type I and Type II errors, the sums of Type I and Type II errors, and the percentages of correct classifications for the probit regression in Table 2. Type I error occurs when the model does not predict an equity issue that subsequently occurs. Type II error occurs when the model erroneously predicts an equity issue. The estimated probability of issuing equity is computed under the assumption that managers’ decision horizon H equals 60 days Cutoff Frequency Frequency The sum Correctly point of Type I of Type II of Type I classified Ž . Ž . error error and Type II Ž . errors 0.040 88.68 1.84 90.52 86.40 0.038 86.66 2.09 88.75 86.46 0.036 85.09 2.28 87.37 86.51 0.034 84.19 2.72 86.91 86.25 0.032 82.29 3.32 85.61 85.99 0.030 80.04 3.83 83.87 85.85 0.028 78.36 4.44 82.80 85.55 0.026 75.56 5.35 80.92 85.14 0.024 72.20 6.41 78.61 84.68 0.022 68.05 7.94 75.98 83.93 0.020 62.11 9.60 71.71 83.29 0.018 56.50 12.34 68.84 81.68 0.016 49.33 16.10 65.43 79.40 0.014 39.69 20.77 60.46 76.67 0.012 30.94 27.21 58.15 72.28 0.010 23.43 35.22 58.65 66.38 0.008 14.80 45.08 59.88 59.02 0.006 9.19 56.41 65.60 49.98 0.004 4.82 70.45 75.27 38.43 0.002 1.68 84.88 86.57 26.38 predicts an equity issue. As a benchmark, recall that the probability of a Type I error, given that the firm issues, plus the probability of a Type II error, given that the firm does not issue, will equal 100 for a naive model that uses no information. We can compare this to the sums of the frequencies of the Type I and Type II errors in Table 3. As the cutoff point gets higher, the percentage of observations that are correctly classified increases. This is because only a very small number of our observations have equity issues. Naively predicting that none of the observations have stock issues would result in a large number of correct classifications, but would do nothing to explain how stock issues can be predicted. In Table 3, the sum of the frequencies of Type I and Type II errors is lowest at a cutoff of 0.012. The sum of the frequencies at this point, 58.15, is much lower than the 100 we would get from a naive model. This shows that our model has considerable predictive power, even though substantial uncertainty remains. We generated similar statistics using a holdout sample. To be specific, we estimated the compound probit model using data from 1980–1992, then used the model to classify observations from 1993– 1994. At a cutoff of 0.024, the sum of the frequencies of Type I and Type II errors was 60.46. This confirms that our model has significant predictive content. In Section 5.3, we correct the predicted values of the compound probit for sampling biases. This gives us estimated probabilities of equity issues, for use in our second stage regression. 5.3. Correcting for unequal sampling We use the procedure in Section 2.3 to correct the predicted values from the compound probit for unequal sampling. From information provided by the NAS- DAQ reference library 7 , there were approximately 30,216,282 security-days on NASDAQ, AMEX, and NYSE from 1980 through 1994, and 5071 seasoned equity issues on the three markets over this period. By definition, H equals the total number of security-days on which managers intend to issue, divided by 5071. This implies that the total number of security-days with Õ G 0 is 5071 H, and the i total number with Õ less than zero is 30,216,282 minus 5071 H. Of these, our i probit sample has 892 observations with Õ G 0, and 5696 observations with i Õ - 0. Expressing these numbers in terms of the parameters defined in Section i Ž . 2.3, we have S s 892, S s 5696, n s 5071, and N s 30,216,282. Eq. 10 E NE E then becomes p ˆ i c p H s , 28 Ž . Ž . ˆ i 892 30,216,282 y 5071 H Ž . Ž . p q 1 y p Ž . ˆ ˆ i i 5696 5071 H Ž . Ž . 7 We would like to thank Brenda Ebersole from the NASDAQ Economic Research Department, for her help in getting this information. where p is the predicted value from the probit regression before adjusting for ˆ i unequal sampling, and H is the horizon. For reasons described in Section 2.3, we estimate our second-stage regression for H s T, 2T, and 4T, where T is the average number of days between the issue announcements and the issues them- selves. For our equity issue sample, T equals 30 days. This provides us with all that we need to estimate the second-stage regression Ž . given by Eq. 11 . 5.4. The effects of pre-issue information releases on information asymmetry Ž . Table 4 presents the results of the nonlinear regression in Eq. 11 . Coefficient estimates and t-statistics are very similar for different values of H. This suggests that our findings are not very sensitive to our assumptions about the average horizon of managers. In all of our regressions, p s y1 gave a better fit than p s q1. This is consistent with the negative issue announcement returns observed Ž . Ž . by Asquith and Mullins 1986 , Kolodny and Suhler 1985 , Masulis and Korwar Ž . Ž . Ž . 1986 , Mikkelson and Partch 1986 , Schipper and Smith 1986 , and Smith Ž . Ž . 1986 . Recall that in our nonlinear regression 11 asymmetric information equals c X y , where c and y are the vectors of coefficients and variables, respectively. i i This means that the coefficients in Table 4 give the effects of the variables on asymmetric information. Because asymmetric information is negatively related to Ž . issue announcement returns, a positive negative coefficient signifies that a Ž . variable has a negative positive effect on issue announcement returns. Ž . The coefficient of Stdret c is highly significant in all of the regressions. 10 Stdret is the standard deviation of pre-issue market-adjusted returns, which measures valuation uncertainty. The positive coefficient of Stdret is consistent with the hypothesis that firms with more uncertainty have more asymmetric information and more negative issue announcement returns. Although the coeffi- Ž . cient of Ddays c is significant at the 0.10 level for some values of H, the 7 negative sign of c would imply that there is less uncertainty as the time since the 7 latest dividend announcement increases. Since this result is neither robust nor plausible theoretically, we take the marginal significance of c to be a Type I 7 error. 8 The statistics for Sdays, Edays, and Ddays are inconsistent with the hypothesis that asymmetric information is reduced by the timing of split declara- tions, earnings releases, and dividend declarations. Together, Tables 2 and 4 show the effects of pre-issue announcements on anticipation and asymmetric information. Table 2 provides evidence that the timing of split, earnings, and dividend announcements can help investors antici- pate equity offerings. Table 4 presents evidence that asymmetric information is not materially affected by the timing of these announcements. 8 Table 4 estimates 33 coefficients. At a significance level of 0.10, the expected number of Type I errors equals 3.3. L. Guo, T.S. Mech r Journal of Empirical Finance 7 2000 113 – 141 135 Table 4 Ž . Nonlinear weighted least squares estimates of Eq. 11 y1 c f z f F p H Ž . Ž . w x Ž . ˆ i i X AR spv q ´ sp c y q ´ , i i i i i c F z p H Ž . Ž . ˆ i i where c X y s c q c D q c Sdaysq c Edaysq c D q c D q c D q c Ddaysq c Mr B q c Lmvegq c Stdret. AR is the 3-day market 1 split 2 3 4 divplus 5 divneg 6 divzero 7 8 9 10 Ž . Ž . y1 Ž c . Ž c . adjusted issue announcement return, f P and F P are the standard normal density and distribution functions, respectively, z sF p , and p is the ˆ ˆ i i estimated ex-ante probability that firm i would issue. If a split is declared in the 250 trading days before an issue announcement, then D equals 1 and split Sdays equals the number of days between the split declaration and the issue announcement; otherwise both D and Sdays equal 0. Edays and Ddays are the split number of trading days between the issue announcement and the preceding earnings release and dividend announcement, respectively. D , D , and divplus divzero D equal one if dividends are increased, unchanged, or decreased at the last dividend declaration before the issue announcement. If a firm does not pay divneg dividends, then D , D , and D equal 0. M r B is market-to-book of assets. Lmveq is the natural logarithm of market value of equity. Stdret is divplus divzero divneg the standard deviation of market adjusted returns in the pre-event period. Each observation is weighted by the standard deviation of market adjusted returns in the pre-event period. Coefficients are estimated under three different assumptions about H, the horizon of managers. t-statistics are in parentheses 3 3 6 6 3 3 3 6 3 3 2 c =10 c =10 c =10 c =10 c =10 c =10 c =10 c =10 c =10 c =10 c Adj. R No. of observation 1 2 3 4 5 6 7 8 9 10 H s 30 days a b y0.0478 y0.0094 0.0345 y0.065 0.0072 y0.0073 0.0145 y0.2560 0.0126 0.0009 0.0043 0.205 891 Ž . Ž . Ž . Ž . Ž . Ž . Ž . Ž . Ž . Ž . Ž . y1.48 y0.87 0.20 y0.48 0.36 y0.40 0.79 y1.74 1.25 0.50 5.19 H s 60 days b y0.0568 y0.0105 0.0319 y0.0688 0.0075 y0.0102 0.0158 y0.2920 0.0145 0.0012 0.0051 0.204 891 Ž . Ž . Ž . Ž . Ž . Ž . Ž . Ž . Ž . Ž . Ž . y1.59 y0.81 0.17 y0.42 0.29 y0.44 0.62 y1.59 1.49 0.48 5.39 H s 120 days a b y0.0720 y0.0128 0.0275 y0.0976 0.0099 y0.0116 0.0211 y0.3830 0.0192 0.0014 0.0064 0.203 891 Ž . Ž . Ž . Ž . Ž . Ž . Ž . Ž . Ž . Ž . Ž . y1.51 y0.74 0.12 y0.46 0.32 y0.43 0.72 y1.82 1.40 0.50 5.27 a Significant at the 10 level using a two-tailed test. b Significant at the 1 level using a two-tailed test. 5.5. Comparison with earlier studies Some of our findings contrast with those of previous studies. Korajczyk et al. Ž . Ž . 1991 present evidence at the 10 significance level that issue announcement returns are negatively related to the days since the preceding earnings releases. Ž . Loderer and Mauer 1992 find that issue announcement returns are lower for dividend-paying stocks. After controlling for anticipation and cross-sectional differences in asymmetric information, we do not find either of these effects. Given our more extensive data and more sophisticated methods, it is not surprising that our results differ. Notwithstanding, it is instructive to look more carefully at the reason for these differences. We want to know how important it is to control both for anticipation, and for the cross-sectional variation in uncertainty, as measured by M rB, Lmveq, and Stdret. We turn to the anticipation question first. Table 5 reports mean and median predicted probabilities from the compound probit model under various assump- tions about the managers’ horizon, H. Two things are immediately evident. First, the predicted probabilities are over twice as large for firms that subsequently issue equity than for firms that do not issue. This is further evidence that equity issues can be anticipated, to some extent. The second thing that emerges from this table is that the predicted probabilities are very small in magnitude. Even when we use the longest horizon of 120 days, the average pre-issue probability of an equity issue is only 4.21 for the sample that subsequently issued. To look at this another way, there was almost a 96 chance that these firms would not issue. These numbers suggest that anticipation played only a minor role. To look at the importance of controlling for cross-sectional variation in uncertainty, we use the methods of earlier studies, with our data and control variables. Table 6 summarizes the results. The regression in the first column uses market adjusted issue announcement returns as the dependent variable, and Table 5 The predicted probability of issuing equity after correcting for unequal sampling Ž . Ž . N Mean Median H s 30 days Issuing sample 892 1.10 0.81 Non-issuing sample 5696 0.49 0.36 H s 60 days Issuing sample 892 2.17 1.62 Non-issuing sample 5696 0.97 0.72 H s 120 days Issuing sample 892 4.21 3.22 Non-issuing sample 5696 1.92 1.43 Table 6 Using the traditional event-study procedure to examine issue announcement abnormal returns for the period 1980–1994. Estimated coefficients from regressions of abnormal issue announcement returns on the specified variables. The dependent variables for Model 1 and Model 2 are AR and AR , mk t mm respectively. AR is the three-day market adjusted issue announcement return. AR is the mk t mm three-day abnormal return estimated from a market model regression. If a split is declared in the 250 trading days before an issue announcement, then D equals one and Sdays equals the number of days split between the split declaration and the issue announcement; otherwise both D and Sdays equal zero. split Edays and Ddays are the number of trading days between the issue announcement and the preceding earnings release and dividend announcement, respectively. Div equals one if dividends are declared before the issue announcement, and zero otherwise. D , D , and D equal one if divplus divzero divneg dividends are increased, unchanged, or decreased at the last dividend declaration before the issue announcement. If a firm does not pay dividends, then D , D , and D equal zero. M r B divplus divzero divneg is market-to-book of assets. Lmveq is the natural logarithm of market value of equity. Stdret is the standard deviation of market adjusted returns in the pre-event period. Model 1 is estimated with weighted least squares, using the standard deviation of market adjusted returns in the pre-event period as the weights. Model 2 is estimated using ordinary least squares. t-statistics are in parentheses Dependent variable Model 1 AR Model 2 AR mk t mm a Ž . Ž . Intercept y0.0039 y0.51 y0.0504 y4.85 Ž . Ž . D 0.0027 0.50 y0.0049 y0.63 split y4 Ž . Ž . Sdays=10 y0.0077 y0.01 0.5860 0.79 y4 Ž . Ž . Edays=10 0.1768 0.40 0.8789 1.55 Ž . Ž . D 0.0001 0.01 0.0093 1.29 divplus Ž . Ž . D 0.0027 0.27 0.0109 0.71 divneg Ž . Ž . D y0.0035 y0.83 0.0048 0.87 divzero y4 Ž . Ž . Ddays=10 0.6095 1.20 y0.5117 y0.70 b Ž . Ž . M r B y0.0015 y1.34 y0.0014 y1.66 a Ž . Ž . Lmveq 0.0003 0.41 0.0039 2.85 a Ž . Ž . Stdret y0.7784 y4.32 0.0116 0.07 Sample size 935 934 2 R 0.047 0.027 2 Adj. R 0.036 0.017 a Significant at the 1 level using a two-tailed test. b Significant at the 10 level using a two-tailed test. weights each observation by the inverse of the standard deviation of the abnormal Ž . returns, as in Korajczyk et al. 1991 . The second column uses issue announce- ment abnormal returns estimated from a market model regression, with data from the 150-day period starting 169 days before the announcement day, and does not weight the observations. This is similar to the procedure used by Loderer and Ž . w x Mauer 1992 . Unlike these earlier studies, we use three-day y1 to q1 issue w x announcement returns instead of two-day y1 to 0 returns. Because Lexis–Nexis announcement dates are usually a day earlier than the WSJ dates used by other studies, our inclusion of event day q1 helps to insure that we pick up effects captured by earlier studies. For the period of 1980–1994, the average 3-day market-adjusted return for the issue announcements in our sample is y2.31, and the average 3-day market-model prediction error is y2.79. These estimated issue announcement returns are in line with those reported in earlier studies Ž . Bayless and Chaplinsky, 1996 and others . Unlike the nonlinear regression in Table 4, the simple regressions in Table 6 do not distinguish between the effects of anticipation and asymmetric information. Because so little of an equity issue announcement is anticipated, however, anticipation probably has very little effect on the coefficient estimates. When comparing Tables 4 and 6, recall that the coefficients in Table 4 estimate the association of the regressors with v or asymmetric information. Under the i assumption that asymmetric information is inversely related to issue announce- ment returns, the coefficients of Table 6 should have the opposite sign. Both of the methods used in Table 6 support our earlier finding that issue announcement returns are significantly affected by firm variables associated with asymmetric information, but not by the timing of pre-issue earnings, dividend, or split announcements. A quick comparison of Tables 4 and 6 suggests that the Ž . relatively simple method of Korajczyk et al. 1991 gives similar results to our conditional event study, at least in this case when the probabilities of the event are small. For other investigations where anticipation is stronger, the benefits of the conditional event study will be greater. We also see that it is very important to control for cross-sectional differences in asymmetric information, before testing for the effect of pre-issue releases of information.

6. Conclusions