Is the performance of firms following seasoned equity issues anomalous?

Journal of Banking & Finance 27 (2003) 1273–1296 www.elsevier.com/locate/econbase Is the performance of ﬁrms following seasoned equity issues anomalo...

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Journal of Banking & Finance 27 (2003) 1273–1296 www.elsevier.com/locate/econbase

Is the performance of ﬁrms following seasoned equity issues anomalous? Mark Bayless a b

a,1

, Nancy R. Jay

b,*

School of Business, Wayne State University, Detroit, MI 48202, USA Stetson School of Business and Economics, Mercer University-Atlanta, 3001 Mercer University Drive, Atlanta, GA 30341-4155, USA Received 12 September 2001; accepted 5 November 2001

Abstract The hypothesis that negative abnormal returns following an equity issue are anomalous assumes the use of the correct benchmark. The alternative hypothesis assumes that an incorrect benchmark has been used or that benchmarks change following an issue. We evaluate these assumptions by examining the performance of SEO ﬁrms during periods when there was no issue activity. Results indicate that SEO ﬁrms experience positive abnormal returns away from the issue window and that positive performance is most pronounced for small SEO ﬁrms. These and other results are inconsistent with the alternative hypothesis. Ó 2003 Elsevier Science B.V. All rights reserved. JEL classiﬁcation: G14; G30 Keywords: Seasoned equity oﬀerings; Buy-and-hold abnormal returns

1. Introduction The evaluation of ﬁrmsÕ performance following an equity issue relies on assumptions about the eﬃcacy of the benchmarks used (Fama, 1998). Loughran and Ritter (1995) and Spiess and Aﬄeck-Graves (1995) use benchmarks they believe accurately capture investorÕs expectations and ﬁnd that ﬁrms underperform following equity issues. Both papers interpret the observed underperformance as anomalous. Consistent *

Corresponding author. Tel.: +1-678-547-6297; fax: +1-678-547-6337. E-mail addresses: [email protected] (M. Bayless), [email protected] (N.R. Jay). 1 Tel.: +1-248-855-6286; fax: +1-313-577-0058. 0378-4266/03/$ - see front matter Ó 2003 Elsevier Science B.V. All rights reserved. doi:10.1016/S0378-4266(02)00257-1

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with this interpretation, Loughran and Ritter (1997) and Lee (1997) provide evidence suggesting that managers may ineﬀectively use issue proceeds. Brav and Gompers (1997) and Brav et al. (2000) contend that the returns observed following equity issues are consistent with that of other small growth ﬁrms and are unrelated to the decision to issue. They conclude that once appropriate benchmarks are used there is no anomaly to be explained. This argument is consistent with reports by Fama and French (1996, Table 1) and Mitchell and Staﬀord (2000, Table 6) that small growth ﬁrms have negative intercepts in three-factor model regressions. Eckbo et al. (2000) also ﬁnd no anomaly in the poor performance of SEO ﬁrms. In their view SEO ﬁrms should experience a decline in performance because an equity issue reduces leverage and therefore the systematic risk of issuing ﬁrms. They argue that once benchmarks are allowed to change following an issue the performance of issuing ﬁrms is in line with expectations. It is diﬃcult to evaluate competing hypothesis about the performance of SEO ﬁrms because existing papers confound issue and benchmark eﬀects when they focus exclusively on performance following an issue. In this paper we attempt to overcome this limitation by examining the performance of SEO ﬁrms outside the traditional six-year window around the issue (one year prior to announcement and ﬁve years after issue). In expanding the scope of analysis we compliment the work of Brav and Gompers (1997), Brav et al. (2000), and Eckbo et al. (2000) by evaluating changes in benchmarks both before and after the traditional six-year issue window. For consistency with previous studies, we measure abnormal returns using calendar time portfolios and factor models based on the Fama–French three-factor model, a four-factor model inspired by Jegadeesh and Titman (1993) and Carhart (1997), and three- and four-factor models that have been purged of new issues. 2 We also estimate mean monthly abnormal returns using the returns to portfolios matched by size, market-to-book ratio and momentum. Our empirical results reveal that SEO ﬁrms consistently enjoy superior performance except during the ﬁve years following issues. Factor loadings indicate that SEO ﬁrms mimic smaller, higher risk ﬁrms before the issue but then mimic larger, lower risk ﬁrms subsequently. This suggests that the negative returns in the ﬁve years following issue may occur during a transition period for high quality ﬁrms that issue equity as part of a growth and risk reduction strategy. We also document greater misvaluation for small ﬁrms in both the issue and non-issue periods. This is not surprising given the observation by Loughran and Ritter (2000) that ‘‘. . . just about every known stock market pattern is stronger for small ﬁrms than for big ﬁrms.’’ However, in contrast to the prediction of Brav et al. (2000) that SEO ﬁrms should chronically underperform because of their small size, we ﬁnd that the smallest two ﬁrm size terciles exhibit the greatest positive abnormal return performance during non-issue periods. Additionally, factor loading comparisons with non-issuers indicate the SEO ﬁrms most closely mimic small ﬁrms in the one year prior to issue when returns are the most positive. This evidence is inconsistent with explanations that

2

Loughran and Ritter (2000) present a thorough discussion of the rationale for purged factors.

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rely on the use of incorrect benchmarks or changes in benchmarks to explain underperformance following issues. The plan of the paper is as follows. Section 2 describes the sample we examine and the approaches we use to estimate abnormal returns. We present the empirical results in Section 3. A summary and conclusions are presented in Section 4. 2. Methodology In this section we describe four time periods relative to equity issues that we analyze, and explain the methods we use to compute abnormal returns. These are calendar time factor models and mean monthly calendar time methods. 2.1. Sample construction Our sample consists of data on oﬀers for the period 1971–1995 purchased from Securities Data Corporation (now Thompson Securities Data Company), acquired from a Nexis search of the ﬁnancial press and from data reported by Lee (1997). 3 To be included in the ﬁnal sample we require that stock returns be available on the Center for Research in Securities Prices (CRSP) NYSE/AMEX or NASDAQ daily tapes. Balance sheet information must be recorded on either the annual Compustat Industrial Tape (COMPUSTAT) or Compact Disclosure and the ﬁrm must have positive book value. For consistency with other studies of seasoned equity oﬀers we exclude utilities, ﬁnancial ﬁrms, closed-end funds, REITs, and ADRs. Our ﬁnal sample is composed of 1239 seasoned equity oﬀers by ﬁrms that made a single issue during the period 1971–1995. As illustrated in Fig. 1 we deﬁne four periods relative to each oﬀer. These are (1) pre-issue period which begins in January 1976 and ends 12 months prior to the announcement of an equity issue; (2) prior period which begins 12 months prior to announcement and continues until the month preceding the announcement; (3) issue period which begins the month following the oﬀer and continues for ﬁve years (60 months) or until December 1994, whichever is earlier; and (4) post-issue period which begins 61 months following an issue and continues until December 1994. Depending on when the issue announcement was made, the pre-issue and post-issue periods may vary in length from zero to 13 years. The prior and issue periods are ﬁxed at one year and ﬁve years as in previous studies. 4 3

We are grateful to Inmoo Lee for providing data on oﬀers that take place from 1971 to 1973. See for example, Asquith and Mullins (1986), Masulis and Korwar (1986), Mikkelson and Partch (1986), Loughran and Ritter (1995), Spiess and Aﬄeck-Graves (1995), Brav and Gompers (1997), Brav et al. (2000), and Eckbo et al. (2000). 4

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Fig. 1. Time lines for period construction. The example assumes an initial oﬀer made in January 1977 and a subsequent oﬀer made in January 1990. Periods are deﬁned as follows. (1) Pre-issue period begins in January 1976 and ends 13 months prior to the announcement of an equity issue; (2) Prior period begins 12 months prior to announcement and continues until the month preceding the announcement; (3) Issue period begins the month following the oﬀer and continues for ﬁve years (60 months) or until December 1994, whichever is earlier; (4) Post-issue periods begins 61 months following an issue and continues until December 1994.

In constructing our four periods we are sensitive to the problems of overlapping returns. In the example in Fig. 1, the post-issue period of an initial oﬀer made in January 1977 has a six-year overlap with the prior and issue periods of a subsequent oﬀer made in January 1990. Among other periods, the pre-issue period of the subsequent oﬀer in this example has a six-year overlap with the prior and issue periods of the initial oﬀer. The overlap would be more problematic for issues made closer together. The situation depicted in Fig. 1 is an example of the most severe form of cross-sectional dependence and leads to biased test statistics in random samples (Lyon et al., 1999; Brav, 1997; Cowan and Sergeant, 1996). Lyon et al. (1999) argue that the only ready solution to this problem is to purge the sample of overlapping returns. This is the approach taken by Loughran and Ritter (1995, 2000) and Spiess and Aﬄeck-Graves (1995, 1999). To comply with the recommendations of Lyon et al. (1999) we examine only the oﬀers of ﬁrms that made a single issue during the sample period. This eliminates 1148 of the 2387 ﬁrms that made equity oﬀering between 1971 and 1995 and yields our ﬁnal sample of 1239 oﬀers by single issuing ﬁrms. To mitigate the problem of overlap with the returns of unobserved SEOs that are beyond our sample period of 1971– 1995, we establish a ﬁve-year ÔbuﬀerÕ at the beginning of our sample (1971–1975) and a one-year ÔbuﬀerÕ at the end of our sample (1995). This reduces the period in which we estimate returns to SEO ﬁrms to the period from January 1976 to December 1994. 5 5

It is not possible to completely eradicate the problem of overlapping periods. For example, for ﬁrms that have made oﬀers in 1970 or earlier, our entire sample period is also technically a post-issue period for the early oﬀer.

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Fig. 2. Year of oﬀer for 1239 industrial ﬁrmsÕ seasoned equity oﬀers from January 1971 to December 1995. To be included in the sample ﬁrms must have made a single issue of seasoned equity during the period 1971–1995, be listed on CRSP (NYSE/AMEX or NASDAQ tapes) and the Compustat Industrial Tape (COMPUSTAT) and have registration and oﬀer dates available from Securities Data Corporation (SDC) or from a search of the ﬁnancial press. The sample excludes utilities, ﬁnancial ﬁrms, closed end funds, REITs, and ADRs. Firms with missing or negative book value on COMPUSTAT are excluded.

Fig. 2 shows the distribution of the sample by the year the security oﬀering is completed. The largest issuing year for the sample period is 1983 with 154 issues. Only three issues were completed in 1971 while four were completed in 1974. Table 1 provides descriptive statistics for the sample ﬁrms and indicates considerable variation across the measures we examine. For example, ﬁrms in our sample range in size from $0.91 million to $29.2 billion and have book-to-market values that range from 0.01 to 4.44. The average market value is $434.5 million and the average book-to-market ratio is 0.40. Buy-and-hold returns in the year preceding announcement range from 0.86 to 9.50 with a mean of 0.94. Oﬀering amount relative to the market value of equity ranges from 0.01 to 2.85 with a mean of 0.22. The time from registration to oﬀer averages 36.7 trading days. 2.2. Calendar time factor models In previous studies of ﬁrm performance following SEOs, calendar time factor models have been implemented by forming a single portfolio each month composed of all ﬁrms that have made an equity oﬀer within some time horizon, usually three to

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Table 1 Descriptive statistics for sample ﬁrms Market value of equity (millions) Book-to-market Prior return [Oﬀering amount/MVE] (102 ) Days between announcement and oﬀer

Mean

Median

Minimum

Maximum

434.5 0.40 0.94 0.22 36.7

93.2 0.30 0.62 0.19 29.0

0.91 0.01 0.86 0.01 0

29,193.0 4.44 9.50 2.85 397.0

To be included in the sample ﬁrms must have made a single equity issue during the sample period 1971– 1995, be listed on CRSP (NYSE/AMEX or NASDAQ tapes) and the Compustat Industrial Tape (COMPUSTAT) and have registration and oﬀer dates available from Securities Data Corporation (SDC) or from a search of the ﬁnancial press. The sample excludes utilities, ﬁnancial ﬁrms, closed end funds, REITs, and ADRs. Firms with missing or negative book value on COMPUSTAT are excluded. Market value of equity (MVE) is share price times the number of shares calculated in the month prior to the issue announcement. We measure book equity as CompustatÕs book value of stockholdersÕ equity, plus balance sheet deferred taxes and investment tax credit (if available), minus the book value of preferred stock. In order of preference, depending on availability, we use the redemption, liquidation, or par value of preferred stock. To insure that the book value we use is available to investors, we require that at least six months elapse between the end of the ﬁrmÕs ﬁscal year and the reported book value. Prior return is the buy-and-hold return in the year preceding issue announcement. Oﬀering amount is the number of shares oﬀered times the oﬀer price. Time between announcement and oﬀer are trading days beginning at announcement day plus one and concluding at oﬀer day minus one.

ﬁve years. 6 In this paper we extend this methodology to form four portfolios each calendar month composed of ﬁrms that are currently in their pre-issue, prior, issue, or post-issue period. That is, beginning in January 1976 and continuing until December 1994 we form four portfolios each month. The ﬁrst, the pre-issue portfolio, is composed of all ﬁrms that will announce an oﬀer in 13 months or more. We form the second portfolio each month from ﬁrms that will announce an oﬀer in 12 months or less and from ﬁrms that have announced but not yet made an oﬀer. These ﬁrms are in the prior period. The third portfolio is similar to those formed by Loughran and Ritter (1995, 2000), Lyon et al. (1999) and Brav et al. (2000) and is composed of all ﬁrms that have completed an oﬀer in the previous ﬁve years. These ﬁrms are in the issue period. 7 Finally, each month we form a portfolio of ﬁrms that have oﬀered 61 or more months ago. These ﬁrms are in the post-issue period. 8 To measure abnormal returns using the Fama–French three-factor model we estimate the following regression for each of the four portfolios: Rpt Rft ¼ a þ b½Rmt Rft þ sSMBt þ hHMLt þ ept ;

ð1Þ

where Rpt is the simple average monthly return on the calendar time portfolio in month t, Rft is one month Tbill yield in month t, Rmt is the return on the valueweighted index of NYSE, AMEX, and NASDAQ stocks in month t, SMBt is the 6

See, for example, Loughran and Ritter (1995, 2000), or Brav et al. (2000). For example, in January 1976, this portfolio is composed of all ﬁrms that issued from February 1971 to December 1975. 8 In January 1976 this portfolio is composed of ﬁrms that issued in January 1971. 7

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return on small ﬁrms minus the return on large ﬁrms in month t, and HMLt is the return on high book-to-market ﬁrms minus the return on low book-to-market ﬁrms in month t. The factor deﬁnitions are described in Fama and French (1993). The regression produces parameter estimates of a, b, s, and h. The error term in the regression is denoted by ept . The estimate of the intercept term ðaÞ provides a test of the null hypothesis that the mean monthly excess return on the calendar time portfolio is zero. Jegadeesh and Titman (1993) report that ﬁrms having a high return in one year tend to have a high return in the following year. This result inspired Carhart (1997) to extend the Fama–French model by including a fourth factor that is nearly orthogonal to the Fama–French factors. The fourth factor is based on ranking ﬁrms by their return over the previous year, or price momentum, and increases the explanatory power of the Fama–French model. Brav et al. (2000) report that the four-factor model is useful in pricing portfolios of issuing ﬁrms. Therefore, we also estimate calendar time abnormal returns by estimating Rpt Rft ¼ a þ b½Rmt Rft þ sSMBt þ hHMLt þ pPR12t þ ept ;

ð2Þ

where PR12t is the return on ﬁrms with the top 50% of returns in the previous 12 months minus the return on ﬁrms with the bottom 50% of returns in month t and all other variables are identical to those described in Eq. (1). This regression produces parameter estimates of a, b, s, h, and p. The error term in the regression is denoted by ept . As in Eq. (1) the estimate of the intercept term ðaÞ provides a test of the null hypothesis that the mean monthly excess return on the calendar time portfolio is zero. Loughran and Ritter (2000) conduct simulations that document that the power of a test can be improved by eliminating sample stocks from benchmarks. They suggest that the solution to this problem of Ôbenchmark contaminationÕ is to construct size (SMB), book-to-market (HML), and prior return (PR12) factors from portfolios after eliminating SEO ﬁrms. Loughran and Ritter (2000) argue, however, that the market factor ðRmt Rft Þ is an equilibrium priced risk factor and for mean–variance eﬃciency considerations do not purge it of SEO ﬁrms. Therefore, we re-estimate Eqs. (1) and (2) using purged factors. 2.3. Mean monthly calendar time abnormal returns In order to evaluate the robustness of the results of factor model calendar time approaches, we also compute monthly abnormal returns (ARit ) for each ﬁrm by matching it with a portfolio of ﬁrms based on size, book-to-market, and prior return momentum using the procedure described in Daniel and Titman (1997). The details of the portfolio construction are as follows. We form 125 portfolios based on size, book-to-market, and prior return momentum using all ﬁrms listed on the NYSE, AMEX, and NASDAQ exchanges during the period 1976–1994. To form reference portfolios we sort all NYSE ﬁrms into quintiles based on their market value of equity calculated on the last day of June from 1976 to 1994. AMEX and NASDAQ ﬁrms are placed into these quintiles according to their size. Within each size quintile we sort ﬁrms into ﬁve portfolios based on their book-to-market ratios. Next, ﬁrms in

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each size and book-to-market portfolio are further sorted into quintiles based on their preceding 12-month stock return. This results in 125 portfolios. Finally, we match each of our SEO ﬁrms to one of the 125 portfolios based on size, bookto-market, and prior return. We are able to match each of our SEO ﬁrms to an appropriate benchmark portfolio. Each month we calculate abnormal returns to each ﬁrm as the diﬀerence between the ﬁrmÕs observed return and the return to the reference portfolio matched by size, book-to-market, and momentum: ARit ¼ Rit P Rpt . We calculate a mean abnormal return for all ﬁrms in the portfolio as MARt ¼ xit ARit . For the analysis of equally weighted abnormal returns, xit ¼ 1=nt . A grandPmean monthly abnormal return is calculated for all T months as MMAR ¼ ð1=T Þ MARt . To test the null hypothesis of zero mean monthly abnormal returns, a t-statistic is calculated using the timeseries standard deviation of the mean monthly abnormal returns: tðMMARÞ ¼ MMAR=½rðMARt Þ=T 0:5 . We estimate mean monthly calendar time abnormal returns for each of the four sub-periods (pre-issue, prior, issue, and post-issue). To increase the power of our statistical tests, we weight each month by the number of securities in the portfolio for that month (see Loughran and Ritter, 2000).

3. Empirical results This section describes our key empirical results: SEO ﬁrms experience positive abnormal returns in all non-issue periods; and changes in systematic risk appear incapable of explaining their relatively poor performance following issues. We conclude the section with tests of robustness which support the eﬃcacy of our methodology. 3.1. Calendar time factor regressions In Table 2 we report the results of time-series regressions of monthly portfolio returns for issuers and non-issuers as described in Eqs. (1) and (2). 9 To increase the power of our tests and to reduce potential heteroskedasticity of the error term we use weighted least squares where the weighting factor is the number of securities in the portfolio in each calendar month. We re-estimated these equations using value-weighted returns with similar results to those we report in Table 2. Results for value-weighted returns are presented in the table in Appendix A. An inspection of the intercept term ðaÞ in row (3) reveals that the issue period underperformance of the SEO ﬁrms in our sample is similar to that reported by Loughran and Ritter (1995, 2000) and Brav et al. (2000). The intercept for the Fama–French three-factor model for the issue period in row (3) of column (1) indicates underperformance by 72 basis points per month. As predicted by Loughran and Ritter (2000) purging Fama–French factors of issuing ﬁrms increases the 9

Non-issuing ﬁrms made no equity issues during the period 1971–1995.

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Table 2 Abnormal returns Fama–French factors (1)

Fama–French purged factors (2)

(3)

Four purged factors (4)

Intercepts for issuers by period and for non-issuers (1) Pre-issue 0.30 0.36 (4.33) (4.60) (2) Prior 3.25 3.22 (18.54) (17.67) (3) Issue 0.72 0.75 (7.08) (6.98) (4) Post-issue 0.24 0.23 (2.77) (2.53) (5) Non-issuers 0.03 0.06 (0.06) (1.03)

0.39 (5.58) 3.28 (17.83) 0.54 (5.34) 0.43 (5.04) 0.03 (0.44)

0.40 (5.03) 3.19 (16.94) 0.63 (5.88) 0.33 (3.64) 0.05 (0.89)

Return diﬀerences for issuers between periods (6) Pre-issue ) issue 1.01 (8.39) (7) Prior ) issue 3.97 (18.25) (8) Post-issue ) issue 0.96 (6.91) (9) Post-issue ) pre-issue 0.05 (0.46)

0.93 (7.64) 3.82 (17.31) 0.97 (7.05) 0.04 (0.40)

1.03 (7.79) 3.83 (16.51) 0.96 (6.57) 0.07 (0.58)

1.11 (8.43) 3.97 (17.36) 0.98 (6.71) 0.13 (1.05)

Four factors

To be included in the sample SEO ﬁrms must have made a single issue of seasoned equity during the sample period, be listed on CRSP (NYSE/AMEX or NASDAQ tapes) and the Compustat Industrial Tape (COMPUSTAT) and have registration and oﬀer dates available from Securities Data Corporation (SDC) or from a search of the ﬁnancial press. The sample of non-issuing ﬁrms made no public issue of equity during the sample period 1971–1995. The sample excludes utilities, ﬁnancial ﬁrms, closed end funds, REITs, and ADRs. Firms with missing or negative book value on COMPUSTAT are excluded. Fama–French factors are: Rpt , the simple monthly buy-and-hold return on an equally weighted calendar time portfolio, Rmt , the return on the value-weighted index of NYSE, AMEX, and NASDAQ stocks in month t, Rft , the beginning-of-month three-month T-bill yield in month t, SMBt , the return on small ﬁrms minus the return on large ﬁrms in month t, and HMLt the return on high book-to-market stocks minus the return on low book-to-market stocks in month t. CarhartÕs factor, PR12t , is the return on ﬁrms with the top 50% of returns in the previous 12 months minus the return on ﬁrms with the bottom 50% of returns. Purged factors are constructed from ﬁrms that do not issue equity. The pre-issue period begins in 1976 and continues until one year prior to the announcement of an equity issue. Prior period begins one year prior to announcement and continues until the announcement date. Issue period begins at oﬀer and continues for ﬁve years. Post-issue period begins ﬁve years following an issue and continues until December 1994. For non-issuing ﬁrms returns are calculated from January 1975 to December 1994. Parameter estimates are produced using weighted least squares, where the weighting factor is the number of securities in the portfolio each month. T-statistics are in parentheses. Regressions for the pre-issue period use 226 monthly observations, prior period use 228, issue Period use 228 and post-issue period use 216. Rpt Rft ¼ a þ b½Rmt Rft þ sSMBt þ hHMLt þ ept ; Rpt Rft ¼ a þ b½Rmt Rft þ sSMBt þ hHMLt þ pPR12t þ ept :

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observed underperformance to 75 basis points per month in column (2). In column (3) the intercept for the four-factor model indicates underperformance by 54 basis points per month. This increases to 63 basis points in column (4) when purged factors are used in the estimation. The intercepts during the period prior to issue are presented in row (2) and indicate positive abnormal performance of close to 3% per month during the year prior to issue announcement. The dramatic positive performance during the year preceding issue announcement has been reported by others (see, for example, Asquith and Mullins, 1986; Lee, 1997). Of particular importance in this study is the size of the intercept during the pre-issue and post-issue periods. If the performance of ﬁrms following SEOs is not anomalous then, all else equal, these coeﬃcients should be similar in magnitude to the coeﬃcient we observe for the issue period. Instead, the results in row (1) indicate positive abnormal performance during the pre-issue period ranging from 30 basis points per month for the Fama–French three-factor model in column (1) to 40 basis points per month for the purged four-factor model in column (4). All the intercepts in row (1) are signiﬁcant at better than the 1% level. Post-issue abnormal performance in row (4) is also positive and ranges from 23 basis points per month for the Fama–French purged factors in column (2) to 43 basis points per month for the four-factor model in column (3). T-values for the coeﬃcients in row (4) range from 2.53 in column (2) to 5.04 in column (3). For purposes of comparison, results for non-issuers are presented in row (5) and reveal intercepts that are not reliably diﬀerent from zero. The evidence presented in rows (6), (7), and (8) indicates that diﬀerences in intercepts between the pre-issue, prior, and post-issue period and the issue period are signiﬁcant at better than the one per cent level. In row (9) intercept diﬀerences between the pre- and post-issue periods are not reliably diﬀerent from zero. Fig. 3 depicts the intercepts for the four periods we analyze. The negative abnormal return performance in the issue period combined with positive abnormal return performance in all other periods is consistent with the existence of an issue eﬀect that cannot be explained solely by the characteristics of SEO ﬁrms. Table 3 presents evidence on the behavior of the coeﬃcients for the explanatory variables. We restrict our presentation to the four-factor model described in Eq. (2) because the magnitude of the coeﬃcients is similar to those derived when we use three-factor models. The results for the four-factor purged model are illustrated in Fig. 4 and reveal that the systematic risk of oﬀering ﬁrms exceeds that of nonissuers in the pre-issue and prior periods. Diﬀerence are signiﬁcant at better than the 5% level as can be seen in rows (1) and (2) of Table 3. The results for b in row (3) show no signiﬁcant diﬀerences between the systematic risk of SEO ﬁrms in the issue period and non-issuers. In row (4) SEO ﬁrms in the post-issue period have b values that are 0.07 below non-issuers. The evidence on b in Table 3 is consistent with the observation of Eckbo et al. (2000) that the systematic risk of SEO ﬁrms falls following issue. With the beneﬁt of a longer view our evidence reveals that the decline in b following issue is relative to the prior period only. In row (5) the diﬀerence in b between the pre-issue and issue period is not reliably diﬀerent from zero. This ﬁnding along with our observation of

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Fig. 3. Monthly abnormal returns. Coeﬃcients are from a four-factor purged model.

positive abnormal returns in the pre-issue period belie the contention of Eckbo et al. (2000) that the weak performance of SEO ﬁrms following issue can be attributed to a reduction in systematic risk. Our evidence indicates that systematic market risk does not fall, relative to long-term norms, until the post-issue period when SEO ﬁrms again enjoy positive abnormal returns. In rows (7) and (8) diﬀerences in b between the post-issue period and the pre-issue and issue periods are signiﬁcant at better than the 1% level. Fama and French (1993, 1996) report that values for the coeﬃcient for SMB decline with ﬁrm size. As can be seen in Fig. 4, SMB slopes for issuers are well above those of non-issuers in the pre-issue period then spike in the prior period before declining in the issue and post-issue periods. The results in Table 3 show that these diﬀerences are signiﬁcant at better than the 1% level for all but the post-issue period. In row (4) the diﬀerence in the coeﬃcient for SMB between SEO ﬁrms in the post-issue period and non-issuers is not reliably diﬀerent from zero using a four-factor model and signiﬁcant at the 10% level using purged four-factor model. The evidence in row (5) indicates that the slope diﬀerences in SMB between the pre-issue and issue periods are signiﬁcant at the ten per cent level. In rows (6) and (7) slope diﬀerences in SMB between the issue period and the prior and post-issue periods are signiﬁcant at better than the 5% level. In row (8) the coeﬃcient for SMB is lower in the post-issue periods relative to the pre-issue period by amounts that are signiﬁcant at better than the 1% level.

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Table 3 Diﬀerences in coeﬃcients for four-factor models for issuers/non-issuers zero investment portfolios by period Rm Rf Four factors

SMB Four factors

HML

(7)

0.04 (0.77) 0.43 (4.82) 0.24 (4.27) 0.07 (1.30)

0.10 0.11 (4.16) (2.14) 0.03 0.03 (0.61) (0.36) 0.18 0.28 (6.02) (4.13) 0.20 0.25 (6.29) (3.86)

Coeﬃcient diﬀerences for issuers between periods (5) Pre-issue ) issue 0.03 0.01 0.09 0.09 0.25 0.21 (1.00) (0.50) (1.84) (1.66) (5.02) (3.25) (6) Prior ) issue 0.09 0.11 0.50 0.46 0.09 0.18 (1.75) (2.09) (6.03) (5.29) (0.96) (1.67) (7) Post-issue ) issue 0.10 0.12 0.21 0.14 0.15 0.17 (2.82) (3.15) (3.68) (2.39) (2.59) (2.36) (8) Post-issue ) pre-issue 0.13 0.13 0.29 0.23 0.09 0.03 (4.53) (4.25) (6.66) (4.66) (1.94) (0.57)

0.08 0.17 (2.01) (2.20) 0.15 0.32 (2.23) (2.62) 0.02 0.03 (0.41) (0.33) 0.10 0.14 (2.60) (1.88)

(3)

Four factors purged (4)

Four factors

PR12 Four factors purged (6)

(1)

Four factors purged (2)

(5)

Coeﬃcients for zero investment portfolios: single issuers ) non-issuing ﬁrms (1) Pre-issue period 0.06 0.06 0.33 0.31 0.00 (2.74) (2.55) (9.61) (8.30) (0.02) (2) Prior period 0.12 0.16 0.74 0.68 0.33 (2.82) (3.62) (10.91) (9.89) (4.44) (3) Issue period 0.03 0.04 0.24 0.22 0.25 (1.12) (1.55) (5.40) (4.80) (5.40) (4) Post-issue period 0.07 0.07 0.03 0.08 0.09 (2.53) (2.62) (0.77) (1.86) (2.05)

Four factors

Four factors purged (8)

The sample and models are the same as in Table 2. Zero investment portfolios assume a long position in issuing ﬁrms and an equal-valued short position in non-issuing ﬁrms.

The results for SMB suggest that issuers perform like small ﬁrms most clearly in the pre-issue and prior periods when they also experience the most positive abnormal returns. This ﬁnding, along with the decline in the magnitude of the coeﬃcient for SMB in both the issue period and the post-issue period, when abnormal returns are positive, makes it seem unlikely that poor issue period performance can be attributed solely to a size eﬀect as argued by Brav et al. (2000). Fama and French (1993, 1996) interpret the loading on HML as a systematic risk factor related to relative distress. They argue that weak ﬁrms with persistently low earnings will tend to have positive slopes on HML while strong ﬁrms with persistently high earnings will have negative slopes on HML. The pattern for HML in Fig. 4 indicates that diﬀerences between SEO ﬁrms and non-issuers are close to zero in the pre- and post-issue periods but well below zero in the prior and issue periods. Table 3 results conﬁrm this impression. In rows (2) and (3) of columns (5) and (6) the coeﬃcients for HML for SEO ﬁrms in the prior and issue periods are more negative than for non-issuers by amounts that are signiﬁcant at the 1% level. In rows (1) and (4) diﬀerences between SEO ﬁrms in the pre- and post-issue periods and non-issuers are not reliably diﬀerent from zero. Under the interpretation suggested by Fama and

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Fig. 4. Coeﬃcients for Rm Rf , SMB, and HML for zero investment portfolios by period. Values are from Table 3 and are calculated using the four-factor purged model.

French (1993, 1996) the fall in the coeﬃcient for HML in the prior period indicates that the non-diversiﬁable risk of distress falls before the issue announcement. This suggests that an equity issue may not be the only tool or even the primary tool SEO ﬁrms utilize in order to reduce their risk. Daniel and Titman (1997) dispute the Fama–French interpretation and argue that the loading on HML may capture the eﬀect on returns of similar ﬁrm properties such as industry or region. In this view, the behavior of returns to SEO ﬁrms changes beginning in the prior period so that their returns co-vary negatively with their former cohorts. However the coeﬃcient is interpreted, the loadings for HML suggest that important changes in the return generating characteristics of SEO ﬁrms occur before the issue is announced. For purposes of exposition, coeﬃcients for PR12 are not included in Fig. 4. However, an inspection of the results in columns (7) and (8) of Table 3 reveals that, with one exception, the coeﬃcients for PR12 are more negative for issuers than non-issuers in the pre-issue, issue and post-issue periods at signiﬁcance that exceeds the 1% level. The one exception is in column (8) for the pre-issue period, when the diﬀerence in the slope of PR12 is signiﬁcant at the 5% level. In row (2), the diﬀerence between prior period issuers and non-issuers is not reliably diﬀerent from zero. These results suggest that the returns to SEO ﬁrms covary positively with low momentum ﬁrms except in the year prior to issue. Brav et al. (2000) argue that the negative loading for PR12 in the issue period could imply that SEO stocks experience a reduction in risk following the issue or that PR12 is picking up SEO mispricing (see also Lakonishok et al., 1994). Consistent with this the evidence in rows (5) and (6) indicates that the loading

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on PR12 is signiﬁcantly more negative in the issue period than in either the pre-issue or prior periods. 10 However, in row (7) the diﬀerence in the loading on PR12 between the issue and post-issue periods is small and insigniﬁcant. This suggests that the reduction in risk may be permanent, or alternatively that mispricing continues past the ﬁveyear issue period window. The latter interpretation is inconsistent with the positive coeﬃcients for the intercept term in the post-issue period in Table 2. 11 While it is diﬃcult to place a precise interpretation on the observed diﬀerences in factor loading across periods and between SEO ﬁrms and non-issuers, the evidence in Table 3 is consistent with variations in the return generating process between issue and non-issue periods. In the pre-issue period SEO ﬁrms have high systematic risk and mimic the behavior of small ﬁrms. These are characteristics that Fama and French (1993, 1996) and others associate with the high returns we observe then. Following the issue period loadings on ½Rmt Rft and SML decline which indicates that systematic risk has decreased and that SEO ﬁrms in the post-issue period no longer mimic small ﬁrms. The coeﬃcient for HML increases in the post-issue period as would be expected following a period of weak performance (Fama and French, 1993, 1996). This evidence suggests the possibility that SEO ﬁrms may be pursuing a growth and risk reduction strategy that relies not just on lowering leverage but on investing in less risky projects as well. This could explain why the systematic risk of distress, as measured by the slope for HML, decreases in the prior period before the new seasoned equity has been issued. Also consistent with this scenario are reports by Loughran and Ritter (1997) that operating performance deteriorates in the issue period. This would not be surprising if ﬁrms invested in lower risk projects. 3.2. Mean monthly calendar time abnormal returns In Table 4 we present the results of calendar time abnormal returns calculated using reference portfolios based on size, book-to-market and prior return. Lyon et al. (1999) report that test statistics based on calendar time abnormal returns using reference portfolios lead to rates of rejection of the null hypothesis of zero abnormal returns that are lower than those produced by the Fama–French three-factor model. This suggests that calendar time portfolio methods based on reference-portfolio returns may provide a useful alternative to the calendar time factor model approach. 12 The results for the full sample presented in column (1) are similar to those produced by factor models. Average abnormal returns are a positive 50 basis points 10 Signiﬁcance is at better than the 5% level for both the diﬀerence between the issue and pre-issue period and between the issue and prior period. 11 2 R values for our factor model regressions (omitted to save space) range from 0.87 to 0.97 and are in line with other published papers in the three- and four-factor model framework. 12 Lyon et al. (1999) suggest two reasons why calendar time portfolio methods based on referenceportfolio abnormal returns might dominate those based on the Fama–French three-factor model. First, Fama–French three-factor regressions implicitly assume that the constructed market, size, and bookto-market factors are linear, although returns to portfolios based on size and book-to-market appear to be non-linear (see, for example, their Table I). Second, the Fama–French three-factor model does not allow for interaction between the three factors.

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Table 4 Mean monthly calendar time abnormal returns Period

(1) Pre-issue (2) Prior (3) Issue (4) Post-issue (5) Return diﬀerence: pre-issue ) issue (6) Return diﬀerence: post-issue ) issue (7) Return diﬀerence: post-issue ) pre-issue

Full sample (1)

Small ﬁrms Mediumsize ﬁrms (2) (3)

(4)

0.50 (4.48) 3.72 (14.74) 0.41 (3.39) 0.36 (3.16) 0.91 (10.65) 0.77 (7.38) 0.13 (1.37)

0.77 (3.06) 4.07 (10.44) 0.74 (3.49) 0.59 (2.21) 1.52 (7.58) 1.33 (5.58) 0.18 (0.77)

0.19 (2.05) 2.06 (8.60) 0.17 (1.30) 0.13 (0.89) 0.36 (3.41) 0.30 (2.43) 0.06 (0.51)

0.81 (4.01) 5.03 (15.33) 0.31 (1.91) 0.46 (2.62) 1.12 (7.11) 0.77 (4.11) 0.34 (1.89)

Large ﬁrms

Monthly abnormal returns (ARit ) for each ﬁrm are calculated using the returns on 125 size/book-tomarket/momentum reference portfolios (Rpt ): ARit ¼ Rit P Rpt . In each calendar month, a mean abnormal return of ﬁrms in the portfolio is calculated as MARt ¼ xit ARit . For the analysis of equally weighted abnormal returns, xit ¼ 1=nt . A grand mean monthly abnormal return is calculated as MMAR ¼ P ð1=T Þ MARt . To test the null hypothesis of zero mean monthly abnormal returns, a t-statistic is calculated using the time-series standard deviation of the mean monthly abnormal returns: tðMMARÞ ¼ MMAR=½rðMARt Þ=T :5 . Mean monthly calendar time abnormal returns are estimated for four subperiods. The pre-issue period begins in 1976 and continues until one year prior to the announcement of an equity issue. Prior period begins one year prior to announcement and continues until the month preceding the announcement. Issue period begins at oﬀer and continues for ﬁve years. Post-issue period begins ﬁve years following an issue and continues until December 1994. Size is measured as the number of shares times share price in the month prior to issue announcement. T-statistics are in parentheses. Monthly observations by period were pre-issue 208, prior 220, issue 240 and post-issue 212. For a complete description of the sample see Table 2.

per month in the pre-issue period and 36 basis points per month in the post-issue period. Both coeﬃcients are signiﬁcant at the 1% level. The coeﬃcient for the prior period in column (1) indicates that, on average, issuing ﬁrms enjoyed positive abnormal returns of 3.72% per month in the year prior to announcement. In contrast, the issue period coeﬃcient indicates a negative mean abnormal return of 41 basis points per month in the ﬁve years following an issue. In rows (5) and (6) diﬀerences between the pre- and post-issue periods and the issue period are 91 and 77 basis points per month and signiﬁcant at the 1% level. In row (7) the diﬀerence in abnormal returns between the pre-issue and post-issue periods is not reliably diﬀerent from zero. In columns (2), (3), and (4) we report results after segmenting the sample into terciles based on ﬁrm size. Similar to the results produced by factor models (see Table 5) and consistent with the observation by Loughran and Ritter (2000) misvaluation in all periods is more pronounced for smaller ﬁrms. In columns (2) and (3) in the preissue period the smallest two terciles of ﬁrms experiences positive abnormal returns

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of 77 and 81 basis points per month which is more than four times the 19 basis points per month of the largest tercile. Coeﬃcients in the pre-issue period for the smallest two terciles are signiﬁcant at the 1% level while the coeﬃcient for large ﬁrms is signiﬁcant at the 5% level. Diﬀerences across ﬁrm size terciles are similar in row (4) for the post-issue period. Abnormal returns for the smallest two terciles of ﬁrms are 59 and 46 basis points per month which are more than three and one half times the 13 basis points per month for large ﬁrms. Coeﬃcients for the two smallest terciles are signiﬁcant at better than the 5% level while the coeﬃcient for the largest tercile is insigniﬁcant. In row (3) abnormal returns for the issue period are 74 basis points for small ﬁrms, 31 basis points for medium ﬁrms, and 17 basis points per month for large ﬁrms. The coeﬃcient for small ﬁrms is signiﬁcant at the 1% level, the coeﬃcient for medium-sized ﬁrms is signiﬁcant at the 10% level, and the coeﬃcient for large ﬁrms is not signiﬁcant at conventional levels. In row (5) diﬀerences in returns between the pre-issue and issue periods range from 152 basis points a month for small ﬁrms to 36 basis point a month for large ﬁrms. Diﬀerences between the post-issue and issue periods in row (6) range from 133 basis points for small ﬁrms to 30 basis points for large ﬁrms. In row (7) diﬀerences between the pre- and post-issue returns are close to zero and insigniﬁcant for small and large ﬁrms. For medium-sized ﬁrms the diﬀerence is 34 basis points a month which is signiﬁcant at the 10% level. The results in Table 4 are consistent with those produced using factor models. Both approaches indicate that, even after considering ﬁrm size eﬀects, knowledge of an issue provides information that is useful in understanding ﬁrm returns.

3.3. Tests of robustness The generally larger loading on SML in Table 3 for SEO ﬁrms relative to nonissuers is consistent with a size eﬀect in the return generating process for these ﬁrms. In Table 5 we provide further evidence to examine the claim by Brav and Gompers (1997) and Brav et al. (2000) that negative abnormal performance in the issue period is due to a size eﬀect and is unrelated to the decision to issue. For consistency with Loughran and Ritter (2000) and Brav et al. (2000) we examine terciles based on ﬁrm size and report only the intercepts in order to save space. 13 The results for small ﬁrms in Panel A reveals strong positive performance in the pre-issue and post-issue periods and strong negative performance in the issue period. 14 Diﬀerences in returns

13 Brav et al. (2000) and Loughran and Ritter (2000) also examine portfolios based on book-to-market ratio. In unreported regression we repeat the analysis in Table 5 after segmenting the sample into terciles based on book-to-market ratio. The results of these regressions are similar to those reported for size terciles in Table 5. Firms in high book-to-market terciles experience greater negative abnormal performance during the issue period but more positive abnormal performance during non-issue periods. 14 There are 413 ﬁrms in each of the ﬁrm size terciles.

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Table 5 Abnormal returns for small, medium, and large ﬁrms Intercept

Fama–French factors (1)

Panel A: Small ﬁrms (1) Pre-issue

0.65 (3.19) (2) Prior 4.04 (11.10) (3) Issue 1.21 (6.72) (4) Post-issue 0.42 (1.90) (5) Return diﬀerence: pre-issue ) issue 1.86 (6.79) (6) Return diﬀerence: post-issue ) issue 1.62 (5.56) (7) Return diﬀerence: post-issue ) pre-issue 0.24 (0.79) Panel B: Medium-sized ﬁrms (1) Pre-issue

0.27 (2.31) (2) Prior 3.71 (16.03) (3) Issue 0.80 (5.71) (4) Post-issue 0.27 (1.49) (5) Return diﬀerence: pre-issue ) issue 1.07 (5.81) (6) Return diﬀerence: post-issue ) issue 1.07 (5.12) (7) Return diﬀerence: post-issue ) pre-issue 0.00 (0.01) Panel C: Large ﬁrms (1) Pre-issue

0.11 (1.38) (2) Prior 1.83 (8.99) (3) Issue 0.17 (1.36) (4) Post-issue 0.19 (2.14) (5) Return diﬀerence: pre-issue ) issue 0.28 (1.93) (6) Return diﬀerence: post-issue ) issue 0.36 (2.35) (7) Return diﬀerence: post-issue ) pre-issue 0.08 (0.67)

Fama–French Four purged factors factors (2) (3)

Four purged factors (4)

0.73 (3.47) 3.96 (10.98) 1.27 (6.85) 0.42 (1.88) 2.00 (7.11) 1.69 (5.63) 0.30 (0.98)

0.75 (3.48) 4.00 (10.54) 0.90 (5.00) 0.78 (3.56) 1.66 (5.89) 1.68 (5.75) 0.02 (0.08)

0.70 (3.21) 3.82 (10.34) 1.13 (5.98) 0.53 (2.32) 1.83 (6.33) 1.67 (5.41) 0.16 (0.50)

0.37 (2.86) 3.70 (14.93) 0.85 (5.74) 0.21 (1.46) 1.23 (6.18) 1.07 (4.87) 0.16 (0.80)

0.34 (2.74) 3.81 (15.91) 0.61 (4.24) 0.49 (3.34) 0.95 (4.97) 1.10 (5.13) 0.15 (0.76)

0.39 (2.89) 3.71 (14.51) 0.69 (4.63) 0.37 (2.56) 1.08 (5.36) 1.06 (4.86) 0.02 (0.08)

0.15 (1.84) 1.77 (8.68) 0.16 (1.27) 0.18 (1.98) 0.31 (2.11) 0.33 (2.18) 0.07 (1.09)

0.17 (2.07) 1.90 (8.95) 0.13 (1.06) 0.22 (2.31) 0.31 (2.05) 0.36 (2.21) 0.05 (0.38)

0.22 (2.65) 1.85 (8.78) 0.12 (0.91) 0.20 (2.20) 0.33 (2.24) 0.32 (2.02) 0.01 (0.12)

The sample and models are the same as in Table 2. Size is measured as the number of shares times share price in the month prior to issue announcement. Small, medium, and large refer to size terciles.

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between the pre-issue and issue period in row (5) range from 1.66% per month to 2.00% per month and are signiﬁcant at better than the 1% level. In row (6) diﬀerences between the post-issue and issue periods range from 1.62% to 1.69% per month and are signiﬁcant at better than the 1% level. In row (7) diﬀerences in abnormal returns in the pre- and post-issue period are small and insigniﬁcantly diﬀerent from zero. In Panel B abnormal returns for medium-sized ﬁrms are similar to those for small ﬁrms but less pronounced. Pre- and post-issue performance for medium-sized ﬁrms is less positive than for small ﬁrms while issue period performance is less negative. In row (5) of Panel B diﬀerences between the pre-issue and issue periods range from 95 to 123 basis points per month and in row (6) diﬀerences between the post-issue and issue period range from 106 to 110 basis points per month. These diﬀerences are signiﬁcant at better than the 1% level but are smaller in magnitude than those observed for small ﬁrms. In row (7) return diﬀerences between the pre- and post-issue periods are not distinguishable from zero. The abnormal returns for large ﬁrms presented in Panel C show the same pattern observed for the other two ﬁrm terciles, but are the smallest in absolute value and have the lowest signiﬁcance levels. Pre-issue period abnormal returns for large ﬁrms range from 11 to 22 basis points per month in row (1) and, for four-factor models, are signiﬁcant at better than the 5% level. Post-issue period abnormal returns range from 18 to 22 basis points per month in row (4) and are signiﬁcant at better than the 5% level. Although issue period returns in row (3) are not signiﬁcantly diﬀerent from zero, diﬀerences between the pre- and post-issue periods and the issue period abnormal returns in rows (5) and (6) are signiﬁcant at better than the 5% level. Return diﬀerences between the pre- and post-issue period in row (7) are small and insignificantly diﬀerent from zero. Brav and Gompers (1997) and Brav et al. (2000) contend that underperformance following issues is a ﬁrm size phenomenon and is unrelated to the decision to issue. Fig. 5 summarizes the results for size terciles for a four-factor purged model and clearly supports the linkage between ﬁrm performance and ﬁrm size. The largest tercile of ﬁrms does not underperform in the issue period and underperformance increases as ﬁrm size decreases. However, if underperformance following issues were due strictly to the size of issuing ﬁrms, then the smallest two terciles of ﬁrms should also exhibit more negative performance in non-issue periods. Instead, non-issue period performance is more positive for small ﬁrms. This result is not surprising if issue period performance is anomalous since stock market patterns are stronger for small ﬁrms (Loughran and Ritter, 2000). The pattern that emerges from an inspection of Fig. 5 is one of positive non-issue period performance and negative abnormal returns only following an issue. This pattern suggests that an issue conveys information about ﬁrm performance that cannot be explained by ﬁrm size alone. Our study extends the methodology of factor models and calendar time regressions by considering time periods beyond the traditional ﬁve-year issue period. To improve the precision of our results we limit our sample to ﬁrms that have made a single issue

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Fig. 5. Monthly abnormal returns for small, medium, and large sized ﬁrms. Coeﬃcients are from a fourfactor purged model.

during the sample period. We now consider whether our variations on the traditional methodology might introduce a look-ahead bias and discuss investorÕs ability to exploit our results. The potential for look-ahead or survivor bias is unavoidable in the use of factor models because each monthly return portfolio is based solely on the ﬁrms that are listed for that month (see, for example, Loughran and Ritter, 1995, 2000; Lyon et al., 1999; Brav et al., 2000; Eckbo et al., 2000). This means that portfolio returns could appear to be higher in later months if poor performing ﬁrms de-list. In our paper this would lead to an upward bias in observed post-issue period returns. To address this problem we use weighted least squares in Tables 3–5 where the weighting factor is the number of ﬁrms used to compute each monthly portfolio return. This methodology mitigates the eﬀects of survivor bias by giving lower weight to months that have fewer observations due to the de-listing of ﬁrms. The results in these tables show no clear indications of a look-ahead or survivor bias. If non-issue period results were driven by a survivor bias, we would expect post-issue period returns to be more positive than pre-issue period returns. In addition, we would expect this diﬀerence to be greater for sub-samples of smaller ﬁrms that are more likely to contain a higher proportion of low quality ﬁrms at the time of issue. Instead, our results indicate that post-issue period abnormal returns are smaller than pre-issue period abnormal returns although diﬀerences generally are not reliably diﬀerent from zero. Therefore, our results do not appear to be driven by an upward bias in postissue returns.

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As an additional precaution we re-estimated factor models after value weighting monthly portfolio returns. Value weighting is eﬀective in mitigating survivor bias because small ﬁrms are more likely to be of low quality and de-listed before the end of the sample period. As before we used weighted least squares to estimate parameter coeﬃcients from the time series of monthly portfolio returns. The results of these regressions for three- and four-factor purged models are presented in the table in Appendix A. Unpurged models yield the same impression and are omitted to save space. These results conﬁrm the impression using the equal weighted monthly portfolio returns presented above. Post-issue period abnormal returns are less positive than pre-issue abnormal returns for small ﬁrms, and the diﬀerences in pre- and post-issue period returns are not signiﬁcantly diﬀerent from zero for the full sample or for any of the ﬁrm size terciles. Other results in the appendix table indicate that equity-issuing ﬁrms experience positive and signiﬁcant abnormal returns except in the ﬁve-year period following issue when returns are signiﬁcantly negative. Abnormal returns in all periods are more pronounced for small ﬁrms. Another feature of our empirical methodology is that we restrict our sample to ﬁrms that make a single issue during the sample period. We believe that this improves the ability to interpret our results because the problem of overlapping returns noted by Lyon et al. (1999) is particularly severe when periods beyond the traditional issue period are considered. However, the conclusions we draw from our empirical analysis do not depend on the decision to focus on single issuers. In Table 6 we present results for multiple issuers and for zero investment portfolios composed of long positions in single issuer portfolios and equivalent short positions in multiple issuer portfolios. 15 These results indicate that multiple issuers exhibit positive abnormal performance in non-issue periods and negative performance in the issue period. In column (1) coeﬃcients for the intercept term for multiple issuers are positive and signiﬁcant in the pre-issue, prior, and post-issue period and negative and signiﬁcant in the issue period. The t-values are 5.25, 19.01, and 2.54 for the pre-issue, prior, and post-issue periods and 4.65 for the issue period. Coeﬃcients for zero investment portfolios in column (1) are not reliably diﬀerent from zero in any period. This indicates that the return performance of multiple and single issuers is similar in all the periods we examined. A close inspection of the remainder of the table reveals that the only reliable difference between single and multiple issuers is in column (3) for the slope of SMB. Coeﬃcients for SMB for zero investment portfolios are positive and signiﬁcant at the 1% level for all but the pre-issue period. This suggests that single issuers behave more like small ﬁrms than do multiple issuers and could help explain why they had relatively little issue activity. Bayless and Chaplinsky (1990) report that small ﬁrms have greater diﬃculty in accessing capital markets. Otherwise we ﬁnd little to distinguish single and multiple issuers.

15 We present only the results of four-factor purged models in order to save space. Results for threefactor and unpurged models are virtually identical to those we present.

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Table 6 Weighted least squares regressions for multiple issuers and zero investment portfolios Period and portfolio Pre-issue period (1) Multiple oﬀers (2) Zero investment portfolio: single ) multiple oﬀers Prior period (3) Multiple oﬀers (4) Zero investment portfolio: single ) multiple oﬀers Issue period (5) Multiple oﬀers (6) Zero investment portfolio: Single ) multiple oﬀers Post-issue period (5) Multiple oﬀers (6) Zero investment portfolio: single ) multiple oﬀers

Intercept (1)

Rm RF (2)

0.49 (5.25) 0.09 (0.68)

1.08 (48.85) 0.03 (0.93)

0.80 (22.65) 0.03 (0.55)

0.05 (1.06) 0.10 (1.59)

0.10 (1.94) 0.01 (0.01)

2.96 (19.01) 0.23 (0.95)

1.22 (33.43) 0.07 (1.21)

0.87 (14.66) 0.33 (3.61)

0.28 (3.78) 0.03 (0.28)

0.05 (0.58) 0.01 (0.07)

0.48 (4.65) 0.16 (1.02)

1.00 (39.91) 0.08 (1.08)

0.58 (13.93) 0.17 (2.71)

0.28 (5.61) 0.22 (2.98)

0.15 (2.50) 0.11 (1.20)

0.29 (2.54) 0.04 (0.22)

0.97 (33.10) 0.05 (1.15)

0.48 (10.15) 0.13 (1.89)

0.08 (1.37) 0.03 (0.38)

0.22 (3.17) 0.01 (0.06)

SMB (3)

HML (4)

PR12 (5)

Adj. R2 (6) 0.95 0.95

0.89 0.88

0.92 0.92

0.88 0.89

Results are produced using weighted least squares regressions of monthly percentage returns on Fama and FrenchÕs market, size, and book-to-market return realizations and a momentum factor for pre-issue, prior, issue and post-issue periods. Monthly portfolio returns are computed as the equal weighted returns of ﬁrms in the portfolio each month. The sample description is presented in Table 2. Rpt Rft ¼ a þ b½Rmt Rft þ sSMBt þ hHMLt þ pPR12t þ ept :

We believe that investorÕs ability to exploit our results for the issue and post-issue period is similar to their ability to exploit the results of other papers that examine ﬁrm performance following an equity issue (for example, Loughran and Ritter, 1995, 2000; Lee, 1997; Lyon et al., 1999; Brav et al., 2000; Eckbo et al., 2000). Our key contribution in this regard is that equity-issuing ﬁrms appear to be good investments following the ﬁve year issue period. Investors need not eschew these ﬁrms forever. Investors will have diﬃculty exploiting our results for the pre-issue and prior period just as they will have diﬃculty exploiting the results of other papers that examine return performance before an issue (see, for example, Asquith and Mullins, 1986; Mikkelson and Partch, 1986; Jung et al., 1996; Bayless and Chaplinsky, 1996; or Lee, 1997). The purpose of examining pre-issue returns is to promote our understanding of how ﬁrms gain access to capital markets and to evaluate whether performance following issue is anomalous. It seems unlikely that investors attempting to develop a

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trading rule could rely on the results of any paper, including ours, that examines ﬁrm performance preceding an issue.

4. Conclusions The evidence in this paper suggests that negative abnormal performance following equity issues represents an anomaly in that ﬁrms experience positive performance at other times. Our evidence indicates that the return generating process changes from issue to non-issue periods in ways we believe reﬂects changing systematic risk. But variations in systematic risk do not appear capable of explaining the discrepancy in the performance of SEO ﬁrms between issue and non-issue periods. Our results are robust to variations in our sampling technique and to our econometric techniques. We conclude that the hypothesis that apparent SEO underperformance is the result of incorrect benchmarks or benchmarks that change following issue should be rejected.

Acknowledgements The authors gratefully acknowledge the research assistance of Sharad Singhal, Rajeesh Talwar, Seema Nandwani, Archana Sharma, Dilpreet Sherwal, and Amit Utrejaa.

Appendix A In this appendix we discuss the results of value weighting monthly return portfolios. We do this to examine whether our results are dependent on the speciﬁc methodology we use and to report results that can be compared with other published papers. Value weighting is potentially important because theory does not provide clear guidance regarding the weighting method to use (see Loughran and Ritter, 1995, 2000; or Lyon et al., 1999). Therefore most papers present results for value as well as equal weighted monthly returns (for example, Loughran and Ritter, 1995, 2000; Lyon et al., 1999; Brav et al., 2000; Eckbo et al., 2000). In the following table we present the results when monthly returns are weighted by market value of equity, which is deﬁned as price times number of shares calculated in the month prior to the issue announcement. To save space we report only three- and four-factor purged models for the full sample and for ﬁrm size terciles. These results are very similar to those presented in Tables 3 and 5 for equal weighted monthly portfolios.

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Time-series regressions using value-weighted portfolios: Period

(1) Pre-issue (2) Prior (3) Issue (4) Post-issue (5) Return diﬀerence: pre-issue ) issue (6) Return diﬀerence: post-issue ) issue (7) Return diﬀerence: post-issue ) preissue

Full sample

Small ﬁrms

Medium-sized ﬁrms

Large ﬁrms

Three factors purged (1)

Four factors purged (2)

Three factors purged (3)

Four factors purged (4)

Three factors purged (5)

Four factors purged (6)

Three factors purged (7)

Four factors purged (8)

0.25 (2.72) 1.93 (9.98) 0.22 (1.77) 0.26 (3.17) 0.47 (1.51)

0.33 (3.58) 2.0 (10.18) 0.22 (1.70) 0.29 (3.40) 0.55 (3.51)

0.72 (3.54) 4.78 (12.59) 1.39 (8.41) 0.55 (2.61) 2.11 (8.09)

0.71 (3.37) 4.58 (11.78) 1.21 (7.26) 0.60 (2.78) 1.91 (7.20)

0.34 (2.52) 4.04 (2.67) 0.88 (6.01) 0.32 (2.18) 1.22 (6.12)

0.36 (2.60) 4.10 (14.86) 0.72 (4.92) 0.51 (3.51) 1.08 (5.36)

0.17 (1.87) 1.58 (7.48) 0.21 (1.57) 0.28 (3.12) 0.38 (2.38)

0.24 (2.66) 1.70 (7.75) 0.21 (1.52) 0.32 (3.41) 0.45 (2.78)

0.48 (3.05)

0.51 (3.12)

1.94 (7.08)

1.81 (6.49)

1.20 (5.53)

1.23 (5.68)

0.49 (3.05)

0.53 (3.17)

0.01 (0.11)

0.04 (0.28)

0.17 (0.57)

0.10 (0.33)

0.02 (0.09)

0.15 (0.71)

0.11 (0.86)

0.08 (0.59)

Results are produced using weighted least squares regressions of monthly percentage returns on Fama and FrenchÕs market, size, and book-to-market return realizations and a momentum factor for pre-issue, prior, issue and post-issue periods. Monthly portfolio returns are computed as the value-weighted returns of ﬁrms in the portfolio each month, where market value of equity is used as the weighting factor. The sample description is presented in Table 2. Rpt Rft ¼ a þ b½Rmt Rft þ sSMBt þ hHMLt þ ept ; Rpt Rft ¼ a þ b½Rmt Rft þ sSMBt þ hHMLt þ pPR12t þ ept :

For the full sample, issuing ﬁrms experience positive and signiﬁcant abnormal returns in non-issue periods that range from 25 to 33 basis points per month and negative and signiﬁcant abnormal returns in the issue period of 22 basis points per month. Diﬀerences between non-issue and issue period abnormal returns range from 47 to 55 basis point per month. Diﬀerences between pre- and post-issue returns are close to zero. Results for ﬁrm size terciles in columns (3)–(8) of the above table indicate that non-issue period returns are more positive and issue period returns more negative for smaller ﬁrms. There is no evidence in row (7) of any reliable diﬀerence between pre- and post-issue returns.

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Is the performance of firms following seasoned equity issues anomalous?

Is the performance of firms following seasoned equity issues anomalous?

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