Credit Ratings and the Pricing of Seasoned Equity Offerings * Yang Liu

Transcription

1 Credit Ratings and the Pricing of Seasoned Equity Offerings * Yang Liu California Polytechnic State University Orfalea College of Business San Luis Obispo, CA Paul H. Malatesta University of Washington School of Business Box Seattle, WA First Draft: January 2005 This Draft: October 31, 2005 Abstract This paper examines how firm credit ratings affect SEO pricing and announcement-period abnormal stock returns. We find that firms with credit ratings are underpriced less and have larger announcement-period abnormal stock returns than those experienced by firms without credit ratings. Firms with high credit ratings also have announcement-period abnormal stock returns that are larger than those experienced by firms with low credit ratings. These results suggest that credit ratings reduce information asymmetry in equity offerings. They are also consistent with the notion that SEO-related wealth transfers from shareholders to bond holders are negatively related to rating levels. * We thank Xi Han for assistance with data collection.

2 2 Credit Ratings and Pricing of Seasoned Equity Offerings 1. Introduction The pricing of seasoned equity offerings (SEOs) has drawn much attention among financial economists recently. Corwin (2003) reports that SEO underpricing averaged 2.92% for offers made from 1990 through 1998 and that the average reached a high of 3.72% in Several studies, including Corwin (2003), Altinkiliç and Hansen (2003), and Mola and Loughran (2004), examine the cross-sectional determinants of seasoned offer pricing. In this paper, we provide a comprehensive analysis of how firm credit ratings affect underpricing and announcement-period abnormal stock returns for a sample of SEOs made from 1990 through We develop and test hypotheses related to asymmetric information and wealth transfer from shareholders to bondholders. To our knowledge, this paper is the first study that connects credit ratings to SEO underpricing and announcement-period abnormal stock returns. Most studies of credit ratings examine their impact on firm financing decisions (e.g. Denis and Mihov (2003) and Kisgen (2005)) or their information content (e.g. Ederington, Yawitz, and Roberts (1987), and Ederington and Goh (1998)). Studies in the SEO literature have focused on the effects of information asymmetry, price pressure, pre-offer price moves, and transaction cost savings. These studies, however, have not considered the effects of credit ratings. Many theoretical analyses seek to explain underpricing from the perspective of uncertainty and differences in information between parties involved in the offer. Rock (1986) argues that because of the winner s curse problem offering firms must price the shares at a discount to guarantee that uninformed investors will purchase the issue.

3 3 Beatty and Ritter (1986) further demonstrate that there is a monotone relation between the underpricing of an offering and uncertainty regarding its value. Corwin (2003), Altinkiliç and Hansen (2003), and Mola and Loughran (2004) provide supportive empirical evidence. Credit ratings may contain information that uninformed investors do not have. Hand, Holthausen, and Leftwich (1992) find significant negative average excess bond and stock returns at the announcements of downgrades of straight debt. Ederington, Yawitz, and Roberts (1987) and West (1973) find that credit ratings are significant predictors of bond yields to maturity beyond information contained in publicly available financial variables and other derivable factors that would predict yield spreads. Ederington and Goh (1998) show that credit rating downgrades result in negative equity returns, on average, and that equity analysts tend to revise earnings forecasts sharply downward following the downgrades. If the existence of credit ratings reduces information asymmetry between parties involved in SEOs, then the offers of firms with credit ratings would tend to be underpriced less than the offers of firms without credit ratings. Credit ratings may also affect SEO announcement-period abnormal stock returns. Altinkiliç and Hansen (2003) find that stockholders incorporate predictable underpricing in stock prices when equity offers are first announced. If rational investors comprehend that the existence of credit ratings reduces SEO underpricing, at the announcements of their SEOs firms with credit ratings would experience abnormal stock returns that are algebraically larger than would firms without credit ratings. That is, when SEOs are

4 4 announced, the stock prices of firms with credit ratings would tend to fall less than the stock prices of those without ratings. There is considerable controversy surrounding market efficiency in SEO pricing. Many studies report a five-year period of underperformance for firms stocks following their SEOs. 1 Delayed market reactions to SEOs are inconsistent with the efficient market hypothesis. Fama (1998), however, argues that long-term abnormal return measures are particularly vulnerable to incorrectly estimating expected returns due to the mismeasurement of risks. He concludes that previously documented abnormal returns following SEOs are a manifestation of risk mismeasurement, not market inefficiency. Recent studies by Eckbo, Masulis, and Norli (2000), and Brav, Geczy, and Gompers (2000) support Fama s conclusion by finding generally insignificant long-term abnormal stock returns following SEOs with their respective models of expected stock returns. Related to market efficiency, there is also controversy about whether SEOs cause a wealth transfer from shareholders to bondholders. Kalay and Shimrat (1987) find negative short-term abnormal bond returns around and following SEO announcements and conclude that SEOs do not cause wealth transfers. In a more recent study, Eberhart and Siddique (2002) find a positive bond price response over the five-year period following SEOs. Their study suggests that the market has a delayed reaction to the wealth transfer caused by SEOs. Findings in both studies are inconsistent with the efficient market hypothesis. Credit rating levels also may affect SEO announcement-period abnormal stock returns. Credit ratings reflect the probability of default on debt. For firms with high 1 See Loughran and Ritter (1995), Spiess and Affleck-Graves (1995), and Jung, Kim, and Stulz (1996).

5 5 credit ratings this probability is small and the potential wealth transfer that could arise due to an SEO-related leverage reduction is also small. Conversely, firms with lower credit ratings have greater default risk and the potential for wealth transfer is correspondingly higher. Hence, we would expect the wealth transfer to be negatively related to credit rating levels. If the market is efficient, any wealth transfer effect should occur quickly and completely after the SEO announcement. Thus, under market efficiency, the wealth transfer hypothesis implies that abnormal stock returns around the announcement date should be positively related to rating levels. It is also possible that credit rating levels are related to information asymmetry and affect SEO underpricing. Firms with high credit ratings may be informationally more transparent than those with low ratings and transparency reduces the extent to which informational asymmetries can develop. Under this assumption credit rating levels would be negatively related to underpricing. If the market is efficient, investors would anticipate the underpricing and announcement-period abnormal stock returns would be positively related to rating levels. Hence, the information asymmetry and wealth transfer effects would reinforce each other. On the other hand, firms with low credit ratings may attract a relatively high degree of scrutiny by financial intermediaries possessing comparative advantages in valuation and information production. The due diligence process associated with credit analysis for low rated firms may disseminate information about these firms and reduce informational asymmetries related to them. Thus, credit rating levels would be positively related to informational asymmetries and, therefore, to SEO underpricing. In this case, the information asymmetry and wealth transfer effects of

6 6 credit rating levels on announcement-period abnormal stock returns would tend to offset each other. We examine 3,243 SEOs that occurred from 1990 through 2002 and find that credit ratings play a significant role. Firms with credit ratings have lower SEO underpricing than firms without credit ratings. Firms with credit ratings also experience announcement-period abnormal stock returns that are significantly larger than those that are experienced by firms without credit ratings. These results suggest that the existence of credit ratings reduces information asymmetry in equity offerings. In our sample, SEO underpricing is negatively related to credit rating levels, though the estimated relation is not statistically significant at conventional confidence levels. At the announcements of SEOs, however, firms with high credit ratings experience abnormal stock returns that are significantly larger than those experienced by firms with low credit ratings. Hence, credit rating levels are positively related to announcement-period abnormal stock returns. Taken together, these results suggest that rating levels are negatively related to informational asymmetries, wealth transfers, or both, and that stock prices impound information about wealth transfers and predictable underpricing, at least partially, on the SEO announcement dates. We also examine changes in credit rating subsequent to SEOs. Issuing firms, on average, improve their credit ratings. Credit ratings in a majority of the issuing firms become higher or remain the same during the two years after SEOs. One year after SEOs, firms on average are upgraded. This finding is consistent with the wealth transfer hypothesis.

7 7 The evidence has important implications for firms that seek capital through SEOs. The important role of credit ratings in debt markets is well recognized. Their effects on the equity offering process, however, have not been widely appreciated. Our results indicate that having an issuer rated and improving credit rating levels before SEOs can significantly reduce the cost of equity capital. The remainder of the paper proceeds as follows. Section 2 discusses the sample. In section 3 we examine the relation between the existence of credit ratings and SEO underpricing and announcement-period abnormal stock returns. Our results on the relation between credit rating levels and SEO underpricing and announcement-period abnormal stock returns are reported in section 4. Section 5 presents evidence on firm credit rating level changes subsequent to SEOs and section 6 concludes the paper. 2. Sample The sample of seasoned equity offers is obtained from the population of seasoned offers reported in the Securities Data Company Global New Issues database. The Securities Data Company (SDC) database provides offer data (offer date, gross proceeds, lead bank identity, auditor identity, number of shares issued, and the exchange where the issuer s shares are listed). Stock market prices, shares outstanding, daily trading volume, and daily returns are obtained from the Center for Research in Security Prices (CRSP) tapes. Credit rating data are obtained from Compustat. To begin, we collect the full sample of U.S. common stock offerings from January 1, 1990 through December 31, 2002, excluding initial public offerings, unit offerings,

8 8 rights offerings, mutual conversions, and issues by non-u.s. firms, closed-end funds, or financial industry firms. 2 This results in a sample of 4,311 offers. We then apply several additional sample restrictions. To be included in the final sample, an offer must (1) include at least some primary shares, (2) be issued by a firm listed on NYSE, AMEX or NASDAQ, (3) have data available on Compustat and CRSP, and (4) have an offer price of at least $3.00. These restrictions eliminate 647, 132, 174, and 25 offers, respectively. We also eliminate 9 offers with underpricing outside the range of [-0.50, 0.50] and 81 offers with missing offer data. The sample restrictions result in a final sample of 3,243 seasoned equity offerings, including 1,056 offerings by NYSE and AMEX-listed firms and 2,187 offerings by NASDAQ-listed firms. Further, the sample includes offers by 2,285 firms. Of these firms, 1,609 made one seasoned offer, 474 made two offers, 140 made three offers, 51 made four offers, 8 made five offers, 1 made six offers, and 2 made eight offers. For the majority of cases underpricing is measured relative to the closing stock price on the day before the offer. Lease, Masulis, and Page (1991), Eckbo and Masulis (1992), and Safieddine and Wilhelm (1996) note, however, that the closing price on the day before the offer is inappropriate for analyzing the underpricing of offers that take place after the market closes. For these cases one should measure the underpricing relative to the closing price on the offer date. This is equivalent to treating the day following these offers as the effective offer date and measuring underpricing relative to the closing price on the day before the effective offer date. Following prior research (e.g. Eckbo and Masulis (1992), Corwin (2003)), we use a volume-based adjustment method 2 Financial industry firms are those with two-digit SIC codes from 60 through 64.

9 9 to identify these cases. If trading volume on the day after the offer date is more than twice that on the offer date and more than twice the average daily trading volume over the 250 days prior to the offer date, the day following the actual offer date is treated as the effective offer date. This date adjustment applies to 34.3% of the sample. Altinkiliç and Hansen (2003) and Corwin (2003) find that this approach correctly identifies offers that take place after the market closes with at least 98% accuracy. Table 1 provides summary statistics for the sample of SEOs. For each variable, the table lists mean values for the complete sample and for the subsamples of the NYSE and AMEX offers and of the NASDAQ offers. The last column reports the differences in the means across markets. [Insert Table 1 here] The average seasoned equity offer raised $103.4 million in proceeds. Offer characteristics differ significantly across markets, with NYSE and AMEX firms making larger offers. The average proceeds are $140.9 million for NYSE and AMEX firms compared to $85.3 million for NASDAQ firms. Relative offer size, defined as offered shares divided by total shares outstanding prior to the offer, averages 19.2% for the NYSE-AMEX group relative to 24% for the NASDAQ firms. The differences in the average proceeds and relative offer size across markets are statistically significant at the 1% level. NYSE-AMEX issuers tend to be larger and have less volatile stock returns than NASDAQ issuers. The average pre-offer market capitalization is $1.9 billion for NYSE- AMEX issuers and $690.9 million for NASDAQ issuers. The average number of shares outstanding prior to the offer for NYSE-AMEX issuers is 41.4 million more than that for

10 10 NASDAQ issuers. Volatility, the standard deviation of daily stock returns over the 30 trading days ending 11 trading days prior to the offer, averages 1.2% for NYSE/AMEX offers compared to 4.3% for Nasdaq offers Credit ratings and SEO underpricing Summary statistics for SEO underpricing are provided in Table 2. As in prior research, underpricing is defined as negative one times the return from the closing trading price on the day prior to the (effective) offer date to the offer price. Consistent with previous studies, we find that SEOs tend to be priced at a discount. In the full sample, the mean underpricing is 3.4%, which is significantly different from zero at the 1% level. [Insert Table 2 here] Table 2 also reports SEO underpricing by credit ratings. Underpricing averages 3.8% for firms without credit ratings compared to 1.9% for firms with credit ratings. The difference in underpricing is significantly different from zero at the 1% level. We further divide firms with credit ratings into subsamples that consist of firms with different credit rating levels. Underpricing is positive in all subsamples and statistically significant in most subsamples. A negative relation between rating levels and underpricing is clearly apparent in the table and is almost monotonic. The magnitude of SEO underpricing has increased substantially over time. This time-series pattern is illustrated in Figure 1. From 1990 through 1995, the average underpricing was 2.9% in the full sample, 3.4% in the subsample of firms without credit ratings, and 1.1% in the subsample of firms with credit ratings. In contrast, from 1996

11 11 through 2002, the average underpricing was 3.8% in the full sample, 4.2% in firms without credit ratings, and 2.4% in firms with credit ratings. The difference in average underpricing between firms without credit ratings and firms with credit ratings, however, decreased from 2.3% to 1.8%. The increase in SEO underpricing during the 1990s is consistent with results reported by Mola and Loughran (2004), Corwin (2003), and Altinkiliç and Hansen (2003). [Insert Figure 1 here] 2.2. Announcement-period abnormal stock returns Seasoned equity offering announcements are identified by a search of the Lexis- Nexis database. We found announcements in Lexis-Nexis for 2,830 offers. In 424 cases there were concurrent announcements about earnings, bank credit agreements, credit rating changes, or dividends. We were unable to find announcements in Lexis-Nexis for 413 offers. For these cases we assume that the information about the offers first became publicly available on the SEC offer filing date, which we treat as an implicit announcement date. The offer announcement-period is defined as the three-day interval that centers on the announcement date and we analyze abnormal stock returns cumulated over this interval. We use prediction errors from a market model to measure abnormal stock returns. To estimate the intercept and the slope of the model for each firm we regress its stock returns on the returns to the CRSP value-weighted index. For this regression we use daily returns over the period from 136 trading days through 11 trading days before

12 12 the announcement date. 3 We then use the estimated intercept and slope to predict firm stock returns, conditional on the market index return. The estimated abnormal return equals the actual stock return less the predicted return. The summary statistics for SEO announcement-period abnormal stock returns are provided in Table 3. For all offers, the announcement-period abnormal stock return has an average of 3.2%, with a median of 2.9%. For offers with no other announcementday news, the mean abnormal stock return is also 3.3%, with a median of 3.0%. For offers with other announcement-day news, the mean abnormal stock return is 2.7%, with a median of 2.4%. All of the means and medians are highly statistically significant. [Insert Table 3 here] Table 4 reports the summary statistics for SEO announcement-period abnormal stock returns broken out by credit ratings. Announcement-period abnormal stock returns averaged -3.4% for firms without credit ratings and 2.7% for firms with credit ratings. Investors respond more negatively to the announcement of SEOs by firms without credit ratings than by firms with credit ratings, and the difference is statistically significant at the 1% level. However, within the rated group of firms, the relation between rating levels and announcement-period abnormal stock returns is not clear. [Insert Table 4 here] 3 Thus, the estimation window spans the trading days over a period of approximately six calendar months.

13 Variable definitions Credit ratings used in this paper are the Standard & Poor (S&P) long-term domestic issuer credit ratings obtained from Compustat. We use two variables to describe firm credit ratings. A dummy variable, DCR, equals 1 if a firm has an S&P credit rating and 0 otherwise. Another variable, CR, is a number assigned to each rated firm based on the credit rating data from Compustat. As shown in the appendix, CR ranges from 1 to 22, with higher numbers corresponding to higher credit ratings. For example, for a firm with AAA rating CR equals 22, and for a firm with BBB rating CR equals 14. Among our sample of SEO firms, 469 have S&P long-term credit ratings. These firms made 680 SEOs during the sample period. Following prior studies, we define two proxies for uncertainty and information asymmetry. The first proxy is firm size, defined as total market capitalization on the day prior to the offer. Small firms are likely to be associated with greater uncertainty and information asymmetry than large firms. The second proxy is volatility, which is defined as the sample standard deviation of daily stock returns over the 30 trading days ending 11 trading days prior to the issue. This variable reflects the ex-ante uncertainty associated with the value of the issuer. Corwin (2003) finds that SEO underpricing decreases with firm size and increases with volatility. As in Corwin (2003), we use the offer size relative to outstanding shares to measure price pressure. Scholes (1972) notes that an increase in supply of a firm s shares will drive down the price if the aggregate demand curve is downward sloping. Any permanent decrease in price due to an increased supply of shares should occur on the announcement date of SEOs, not on the offer date. Underpricing occurs if there is any

14 14 temporary price pressure on the offer date. Corwin (2003) finds that underpricing is higher for firms with relatively large offer sizes. Gerard and Nanda (1993) argue that investors may attempt to depress the offer price by selling in the pre-offer secondary market. As in prior research, we use the estimated cumulative abnormal stock return, CAR, over the 5 days prior to the offer as a proxy for manipulative trading. CAR has an average of 1.7% for NYSE-AMEX offers and 2.3% for NASDAQ offers. Another important source of underpricing is offer price rounding. Mola and Loughran (2004) and Corwin (2003) find that when setting an offer price underwriters tend to round the preceding day s closing price down to the nearest integer value or to a value with an increment of $0.25. Thus, underpricing as a fraction of stock price should be larger for firms with closing prices that are not a $0.25 increment, and for firms with low stock prices. Stock price is measured as the closing price on the trading day before the effective offering date. To capture the effect of offer price rounding on underpricing we define a dummy variable, Ticker<0.25. Ticker<0.25 is set equal to one if the mantissa of the closing price on the day prior to the effective is not an increment of 0.25 and equal to 0 otherwise. In our multivariate analysis we control for lead investment bank reputation and auditor reputation. Lead bank reputation is measured using the Carter and Manaster (1990) ranking of banks that underwrite unseasoned equity offerings and as updated in Carter, Dark, and Singh (1998). This measure ranges from 0 to 9, with higher values associated with more prestigious banks. Sample banks identified from the Securities Data Company database that are not ranked by Carter and Manaster (1990) or by Carter,

15 15 et al. (1998) are assigned a reputation of zero. Following Slovin, Sushka, and Hudson (1990), we define a dummy variable, auditor reputation, which equals 1 when an issuer employs a Big Eight auditor and 0 otherwise. 4 We also control for IPO underpricing and the exchange where the issuer s shares are listed. Corwin (2003) suggests that there might be additional common factors affecting both IPO and SEO underpricing. Thus, we include the average IPO underpricing during the same month as the SEO. 5 Because of the difference in underpricing across markets, we have a dummy variable, NASDAQ, which equals 1 if the issuer s shares are listed on NASDAQ and 0 otherwise. Prior research also finds an increasing trend in SEO underpricing, as in Figure 1. Accordingly, we include year dummy variables to control for any trends in SEO underpricing. We also include a utility dummy variable, which equals 1 for utility firms and 0 otherwise, to control for the difference in underpricing between regulated industries and unregulated industries. 3. The existence of credit ratings and SEOs To examine the effects of credit ratings on SEO underpricing and announcementperiod abnormal stock returns, we must first consider a sample selection issue. Credit ratings arise endogenously as a consequence of choices made by issuing firms and rating agencies. This endogenous selection process has the potential to bias estimates of the causal impacts of credit ratings on underpricing and announcement-period abnormal 4 The original Big Eight accounting firms were Arthur Andersen, Arthur Young & Company, Coopers & Lybrand, Ernst & Whinney, Deloitte, Haskins and Sells, KPMG, Price Waterhouse, and Touche Ross. Due to mergers and the collapse of Arthur Andersen only four large accounting firms remain today: Deloitte Touche, PricewaterhouseCoopers, Ernst & Young, and KPMG. 5 The monthly underpricing estimates for IPOs are obtained from Jay Ritter s web page at

16 16 stock returns. To see this, suppose that credit ratings do not affect information asymmetries but that only relatively transparent firms that are easy and, therefore, cheap to evaluate seek to be rated. In this case, an ordinary regression of underpricing or announcement-period abnormal stock returns on the dummy variable indicating the existence of a rating, DCR, may yield misleading results. If information asymmetry causes underpricing, then we would expect the coefficient of DCR to be negative in the underpricing regression, but credit ratings are only coincidentally related to information asymmetry in this example. They have no causal impact on underpricing and the ordinary regression estimate of the coefficient of DCR is biased and inconsistent. To address the selection bias problem discussed above we estimate a twoequation treatment model. 6 The model consists of a treatment equation and a regression equation. We suppose that there is an unobservable underlying variable, DCR * i, that determines whether the i th firm is rated. If DCR * i exceeds zero, the firm is rated, otherwise, it is not. Formally, the treatment rule is given by DCR * i = γ W i + u i DCR i =1 if DCR * i >0, DCR i = 0 otherwise, and [1] Prob(DCR i = 1) = Φ( γ W i ) In [1] W i denotes a column vector containing values for the variables hypothesized to affect the probability that firm i is rated. γ is a row vector of coefficients multiplying the elements of W i, and u i is a disturbance term assumed to be normally distributed with 6 Models of this type are discussed in detail by Heckman (1979), Maddala (1983, p ), and Greene (2000, p ).

17 17 mean zero and variance one. Φ(γ W i ) denotes the cumulative standard normal distribution function evaluated at the point γ W i. The regression equation relates the variable of primary interest, SEO underpricing or announcement-period abnormal stock returns, to the credit rating dummy variable, DCR i, and to a vector of control variables, X i. Let Y i denote either underpricing or abnormal stock returns. Then the regression equation is Y i = β X i + λdcr i + e i [2] In [2] β is a row vector containing the coefficients of the control variables X i and λ measures the effect of being rated on Y i, underpricing or abnormal stock returns. The control variables may differ between the analysis of underpricing and that of abnormal returns. The disturbance term e i is assumed to be normally distributed with mean zero and variance σ 2 e, and the correlation between u i and e i is denoted by ρ. The parameters of the two equation model are estimated simultaneously by the method of maximum likelihood. 7 We rely on previous work in formulating the specification of the treatment equation. Cantillo and Wright (2000) report that more than 97% of firms that issue public debt are rated. Therefore, firm characteristics that determine whether or not firms have public debt also determine whether or not they have credit ratings. We use four characteristics in our specification. All are measured as of the ends of the fiscal years immediately preceding SEOs. The natural logarithm of sales is a proxy for flotation costs. There are fixed costs to issuing debt publicly and these tend to deter small issues. 7 The form of the likelihood function follows from the properties of the truncated bivariate normal distribution. (See Greene (2000, p. 927) Theorem 20.5.) The function is quite complex and the maximization problem must be solved numerically. Our estimates were derived using STATA release 8.

18 18 Thus, because they raise capital on a large scale, large firms are more likely than small firms to issue public debt. The ratio of earnings before interest, taxes, depreciation, and amortization (EBITDA) to total assets (TA) is a proxy for default risk. Firms with low profitability and high default risk are less likely to issue public debt than other firms because it is more costly in financial distress to negotiate with diverse public debt holders than with a small group of private lenders. Our specification also includes a proxy for growth options in firm investment opportunity sets. Following Barclay and Smith (1995), we use the market-to-book ratio for capital (MTB). To calculate MTB we add the book value of total debt to the market value of outstanding equity. We then divide this by the sum of the book values of debt and equity. Growth options are more difficult to value than assets-in-place and private lenders would tend to have a comparative advantage in evaluating these options. Thus, firms with extensive growth options are less likely than other firms to issue public debt. We also attempt to control for differences in asset tangibility by including the ratio of fixed assets (FIX) to total assets in the model. If it is easier to assess the value of tangible assets than of intangible ones we would expect this ratio to be positively related to public debt issuance. The precise specification of γ W i in [1] is γ W i = γ 0 + γ 1 *ln(sales i ) + γ 2 *EBITDA i /TA i + γ 3 *FIX i /TA i + γ 4 *MTB i [3] Johnson (1997), Krishnaswami, Spindt, and Subramaniam (1999), and Denis and Mihov (2003) study firms choices between issuing public debt and borrowing privately and use models similar to ours. [Insert Table 5 here]

19 19 Table 5 reports the estimates for the treatment equation. Models 1, 2, and 3 correspond to different specifications of the regression equation [2] where the dependent variable is SEO underpricing. Estimates for the regression equation are reported in table 6 and discussed below in section 3.1. Because the selection and regression equations are estimated jointly, a change in the specification of one equation will generally affect the likelihood maximizing parameter values for both equations. Estimates for a single equation stand alone probit regression are also provided in table 5 for comparison. We find that size is significantly positively related to the probability that a firm will be rated, reflecting transaction cost economies of scale in public debt issues. Contrary to our expectations, the default risk proxy, EBITDA/TA, is significantly negatively related to rating probability. This is inconsistent with the argument that firms with low profitability would tend to avoid issuing public debt. Firms with high marketto-book ratios are less likely to be rated than those with low ratios. All of the estimated coefficients of MTB are negative, as expected, and are statistically significant. Also, as expected, the ratio of fixed to total assets is positively related to rating probability SEO underpricing and the existence of credit ratings Table 6 reports maximum likelihood estimates of the parameters of the regression equation [2] where the dependant variable Y is SEO underpricing. Besides year dummy variables and a utility industry dummy variable, model 1 includes only the rating dummy variable DCR. The results confirm what we have reported in Figure 1 and Table 2. Expected SEO underpricing is 4.4% lower in firms with credit ratings than in firms

20 20 without credit ratings. The relation between the existence of credit ratings and SEO underpricing is statistically significant at the 1% level. Model 2 includes DCR and control variables for price pressure, manipulative trading, price rounding, underwriter and auditor reputation, and contemporaneous IPO underpricing. The estimated coefficient on DCR in model 2 is smaller in absolute value than in model 1, but it is still statistically and economically significant. Compared to firms without credit ratings, firms with credit ratings have 2.7% lower expected underpricing, holding other factors constant. Results on the control variables are consistent with those reported in prior research. SEO underpricing is higher for relatively large offers, which supports the price pressure argument. Underpricing also increases with pre-offer cumulative abnormal stock returns and IPO underpricing, and decreases with underwriter reputation. The effects of tick size and auditor reputation are not statistically significant. Model 3 includes firm size and volatility to control for the effect of information asymmetry. The absolute magnitude of the coefficient on DCR is somewhat smaller in model 3 than in models 1 or 2. Compared to firms without credit ratings, firms with credit ratings have 1.9% lower expected underpricing. The impact of the existence of a credit rating is still statistically significant at the 1% level. Volatility has a significant coefficient of Note that in model 3 the coefficient on the NASDAQ dummy variable is very small (0.001) and statistically insignificant. This sheds new light on the findings reported by Altinkiliç and Hansen (2003) and Corwin (2003) of a significant listing effect on SEO underpricing. DCR and NASDAQ have a correlation of 0.471, which differs

21 21 significantly from zero at the 1% level. This result suggests that the effect of credit ratings may explain why firms listed on NASDAQ tend to have higher underpricing than firms listed on the NYSE or AMEX. [Insert Table 6 here] We also test the hypothesis that ρ, the correlation between the disturbance terms in the treatment and regression equations, equals zero. The appropriate statistic for a test of this restriction is asymptotically distributed as a chi-square variable with one degree of freedom. The chi-square values are reported in the penultimate row of table 6. We reject the restriction at the 1% level (or smaller) for each of models 1, 2, and 3. The point estimates of ρ range between 0.28 and These results underscore the relevance of the selection bias problem associated with single equation regression methods in our current research application Announcement-period abnormal stock returns and the existence of credit ratings The results reported above support the hypothesis that the existence of credit ratings reduces information asymmetry. In the analysis, we focus on the impact of credit ratings on SEO underpricing. A similar argument applies to the stock price response at the announcements of SEOs. If investors anticipate the effects of credit ratings on underpricing at the announcements of SEOs, the announcement-period abnormal stock returns should change with credit ratings as well. Table 7 reports maximum likelihood estimates of the parameters of the regression equation [2] where the dependant variable Y is SEO announcement-period abnormal

22 22 stock returns. 8 In models 1 and 2, all offers are used in the estimations. Model 1 includes the dummy variable DCR, utility industry dummy variable, and year dummy variables. The estimated coefficient on DCR is positive and statistically significant at the 1% level. The expected abnormal stock return for firms with credit ratings is 5.8% larger in magnitude than that for firms without credit ratings. When we include firm size and relative offer size in the regression (model 2), the coefficient on DCR is 6.5% and is still statistically significant at the 1% level. Neither firm size nor relative offer size has a significant impact on the abnormal stock returns. Models 3 and 4 repeat the analysis in models 1 and 2, but include only offers without other announcement-day news. The coefficients on DCR are still positive and significant at conventional confidence levels. [Insert Table 7 here] In sum, the statistical results reported above support the hypothesis that the existence of credit ratings reduces information asymmetry in equity offerings. Compared to firms without credit ratings, expected underpricing for firms with credit ratings is lower by 1.9% to 4.3% depending on the model specification. Investors also anticipate the effects of credit ratings on SEO underpricing, and the expected abnormal stock returns at SEO announcements are larger for firms with credit ratings than for firms without credit ratings. 8 The corresponding estimates for the selection equation coefficients are very similar to those reported in table 5. For this reason we do not report them here. They are available on request.

23 23 4. SEO announcement-period abnormal stock returns and credit rating levels In this section we examine the impact of credit rating levels on SEO underpricing and announcement-period abnormal returns. Of course, rating levels are only observed for those firms that are rated. As a consequence, the treatment effects model of section 3 is not appropriate. Instead, we estimate a two equation selection model that is very similar to the treatment model. In the selection model the regression equation for Y i is observed only if DCR i equals one. This affects the form of the likelihood function. Except for this subtle difference, the two models are much the same. The form of the selection equation used to generate the results in this section is identical to that of the treatment equation in section 3. Moreover, the results overall for the selection equation are themselves quite similar to those reported in table 5 for the treatment equation. For this reason we do not report them here. 9 Instead, we focus on the results for the regression equations, which are our primary interest. The regression equations here are highly similar to those considered in section 3, differing only in the specification of the credit rating variable SEO underpricing and credit rating levels Table 8 reports the maximum likelihood estimates for the regression equation of the selection model where the dependant variable is SEO underpricing. Model 1 includes only the logarithm of credit rating levels (ln(cr)), utility industry, and year dummy 9 The results for the selection equation estimates are available on request.

24 24 variables. Ln(CR) has an estimated coefficient of , which is statistically significant at the 1% level. 10 Model 2 includes the logarithm of credit rating levels and control variables for price pressure, manipulative trading, price rounding, underwriter and auditor reputation, and contemporaneous IPO underpricing. The estimated coefficient on ln(cr) is negative, though it is statistically insignificant. SEO underpricing significantly decreases with preoffer price and increases with pre-offer cumulative abnormal stock returns. In model 3, we add control variables for uncertainty and information asymmetry. The estimated coefficient on ln(cr) is again negative and statistically insignificant. The coefficients on market capitalization and volatility are not statistically significant either. Though it is not reported in the table, when the model excludes ln(cr), the coefficients on both market capitalization and volatility are statistically significant at conventional levels of confidence. Similar to the results in Table 6, the coefficient on the NASDAQ dummy variable is insignificantly different from zero. Among firms with S&P credit ratings, ln(cr) and the NASDAQ dummy variable have a correlation of 0.420, which is significant at the 1% level. This result suggests that offers made by NASDAQ firms tend to be underpriced more than those made by NYSE or AMEX firms because the NASDAQ firms tend to have lower credit ratings. [Insert Table 8] 10 We also estimated models including the credit rating level variable CR instead of its logarithm. There are no material differences in the inferences drawn from the estimates using CR instead of ln(cr).

25 Announcement-period abnormal stock returns and credit rating levels Table 9 reports the effects of credit rating levels on the announcement-period abnormal stock returns. All offers are included in the estimation of models 1 and 2. When the model includes only ln(cr) its coefficient is and the estimate is statistically significant at the 5% level. When the model also includes market capitalization and relative offer size, the coefficient on ln(cr) is also positive. This estimate is not significant at conventional levels, but it is close. The marginal significance levels are 6.4% and 12.8% for one and two-tailed tests, respectively. The coefficients on both market capitalization and relative offer size are positive, and the coefficient on market capitalization is significantly different from zero. Models 3 and 4 are identical to models 1 and 2, respectively. The estimates, however, are derived from the subsample of offers without other announcement-day news. The estimated coefficients on ln(cr) remain positive and are significant at the 1% level. [Insert Table 9 here] Our results indicate that credit rating levels significantly affect SEO announcement-period abnormal stock returns. Firms with high credit ratings experience larger abnormal stock returns than firms with low credit ratings. These findings are consistent with the hypothesis that rating levels are negatively related to informational asymmetry or wealth transfers and that stock prices impound information about wealth transfers and predictable underpricing, at least partially, on the SEO announcement dates.

26 26 5. Subsequent credit rating changes In this section, we examine how firm credit ratings change during the post-seo period. Under the wealth transfer hypothesis, SEOs benefit bond holders by reducing the probability of default on their bonds or increasing their collateral value. The hypothesis, therefore, implies that SEOs reduce firm credit risk. If so, we would expect firm credit ratings to increase after SEOs. [Insert Table 10 here] Table 10 provides the average credit rating changes for the sample of rated firms subsequent to SEOs. As of one year after SEOs, firms had improved their credit ratings in 129 cases. On average, these firms are upgraded by about one credit rating level. Firm credit ratings deteriorate after 67 SEOs. On average, these firms are downgraded by more than one credit rating level. Firm credit ratings do not change after 484 offers. For all 680 offers, firms on average are upgraded after the offerings. The average change in credit ratings is small, but statistically significant at the 5% level. Table 10 also provides the credit rating changes from the time of SEOs to two years afterward. Credit ratings are available for 660 firms two years after their SEOs. Firm credit ratings are upgraded after 203 offers, improving on average by more than one credit rating grade. Credit rating levels for the 118 firms that are downgraded fall by an average of more than two grades. Credit ratings do not change following the other 339 offers. Overall, firm credit ratings improve on average during the two years after SEOs, but the change is insignificant.

27 27 The evidence in table 10 is consistent with the wealth transfer hypothesis in that firm credit risk tends to fall, on average, after SEOs. This evidence, however, is rather weak and indirect. There is an inherent survivorship bias in calculating the credit rating change measures. We do not know how this affects the average rating change, so we interpret these results cautiously. 6. Conclusion It is well known that credit ratings are related to bond yields and play an important role in the market for corporate debt. Our analysis suggests that ratings should also influence the equity market. Under the information asymmetry hypothesis, credit rating increases firm transparency and in this way reduces the extent of informational asymmetries. Under the wealth transfer hypothesis, the actions of firms with high credit ratings are liable to have only small effects on bond values because these firms are unlikely to default on their debts. Thus, we argue that credit ratings should be related to SEO underpricing and announcement period-abnormal stock returns through their linkage to informational asymmetries and the potential for wealth transfers from shareholders to bond holders. Specifically, the hypotheses imply that credit ratings should be negatively related to underpricing and positively related to announcement-period abnormal stock returns. We conduct several empirical tests of the hypotheses. First, we examine the impact of the existence of credit ratings on SEO underpricing and announcement-period abnormal stock returns. Our test design, a two equation treatment effects model, controls for the endogenous nature of credit ratings and for other factors believed to affect

28 28 underpricing and returns. We find that firms with credit ratings have lower underpricing than firms without credit ratings. Firms with credit ratings also have larger abnormal stock returns than firms without credit ratings at the announcements of SEOs. Moreover, our estimates are statistically significant and robust to alternative model specifications. These results are consistent with the hypothesis that credit rating reduces information asymmetry and that investors anticipate the effects of credit ratings on SEO underpricing when SEOs are first announced. The effects of credit rating levels on SEO underpricing and announcement-period abnormal stock returns are examined, also. This analysis is conducted within the framework of a two equation selection model because rating levels are only available for rated firms. We find that announcement-period stock returns are significantly positively related to rating levels. In addition, underpricing is negatively related to rating levels, though this result is not statistically significant at conventional confidence levels. We present additional evidence bearing on the wealth transfer hypothesis by investigating how firm credit ratings change after SEOs. A majority of firms improve their credit ratings or keep the same credit ratings after SEOs and, on average, firms are upgraded. This evidence, while indirect, is consistent with the notion that SEOs reduce firm credit risk and the entire body of evidence presented here is consistent with the information asymmetry and wealth transfer hypotheses.

29 29 Appendix Definition of CR CR is a numerical value assigned to each credit rating level. Data item 280 is from COMPUSTAT. CR Credit rating level Data item D 27 2 C 24 3 CC 23 4 CCC CCC 20 6 CCC B B 17 9 B BB BB BB BBB BBB BBB A A 8 18 A AA AA 5 21 AA AAA 2

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