IPO Prospectus Information and Subsequent Performance By Harjeet S. Bhabra Faculty of Commerce and Administration Concordia University 1455 de Maisonneuve Blvd. W. Montreal, Quebec H3G 1M8 Canada (514) 848-2909
[email protected] and Richard H. Pettway* College of Business and Public Administration University of Missouri-Columbia Columbia, MO 65211 (573) 882-3800
[email protected] October 12, 2000 Abstract Researchers have primarily focused on how IPO offering characteristics are related to the level of underpricing; however, the full array of prospectus information has not been related to long-term performance and subsequent outcomes. Using prospectus data, we compare firm and offering characteristics to subsequent performance and catalogue the most important variables. Generally, firm characteristics (size, research expenditures, free cash flow and leverage) are more significant than offering characteristics. We find that prospectus information is more useful in predicting extremely poor performers, firms that fail, and firms that reissue equity and less so for predicting winners. These results are robust to the choice of benchmark matched firm employed in measuring post-IPO stock return abnormal performance. Key words: IPOs, Prospectus, Performance JEL Classification: G3, G32. *Contact Author. We would like to thank seminar participants at Binghamton University (SUNY), University of Missouri-Columbia, Upinder Dhillon, Doug Emery, Rajesh Narayanan and Dogan Tirtiroglu for their valuable comments and suggestions. Bhabra acknowledges the financial support of the Faculty Research Development Program (FRDP) at Concordia University. Both authors acknowledge support to purchase the prospectuses from the Financial Research Institute at the University of Missouri-Columbia. All remaining errors are ours.
IPO Prospectus Information and Subsequent Performance 1. Introduction A detailed prospectus is required before initial public offerings. It provides information about the offering, business history of the firm, information related to past financial performance, ownership details and the risks associated with the investment. The investment community recognizes that the most detailed and precise information about the issuing firm is found in the offering prospectus.1 In addition, the prospectus is a legal document that protects the issuer and the underwriter since it is written proof that the investor was provided with all the material facts related to the offering. Despite the widely accepted view that information contained in a prospectus is valuable in assessing the risk of the offering, there has been no systematic study in the academic literature of the usefulness of the information contained in the prospectus vis-a-vis the subsequent performance of the issuer. This study aims to fill this gap by cataloging the most important prospectus information related to subsequent performance. Very little is known about how useful prospectus information is to investors in their decision to invest in a specific IPO. Given that a number of companies that raise funds in the IPO market do not have a well-known history of past revenues or earnings, many investors may be quite skeptical about the worth of the information contained in the prospectus. Nevertheless, since the prospectus has to be approved by the Securities and Exchange Commission (SEC) for material accuracy and the underwriter has to exercise due diligence when providing relevant disclosures of the issuer’s operations, information contained in a prospectus is often the first window to a potential investor about the firm’s past and its projected future performance. We examine the value of the information contained in the offering prospectus in identifying firms that succeeded or failed within 5 years of the IPO. Specifically, we investigate
the usefulness of the following prospectus information: (i) the accounting information related to financial and operating performance (firm characteristics such as firm size, spending on research and development etc.), and (ii) information pertaining to the offering itself (offering characteristics such as offer size, offer price, underwriter reputation, risk factors etc.). The role of offering characteristics and their relationship with underpricing has been examined in the extant literature (see Ibbotson, Sindelar and Ritter (1988), Jegadeesh, Weinstein and Welch (1993), Carter and Manaster (1990), Michaely and Shaw (1994), and Michaely and Shaw (1995) among others). On the other hand, few studies have researched the usefulness of accounting information found in the prospectus. Jain and Kini (1994) examine the accounting data of IPO firms (available on the COMPUSTAT tapes) and find that the post-issue operating performance of IPOs, measured by return on assets, declines from their pre-IPO levels. This finding is consistent with the long-run underperformance in stock returns documented by Ritter (1991) and Loughran and Ritter (1995). More recently, Kim and Ritter (1999), study the use of accounting numbers along with comparable firm multiples to value new issues. They show that historical accounting numbers without any adjustments result in large valuation errors. However, they use only sales, earnings and cash flow measures in their analysis and acknowledge that other accounting information may be potentially useful in valuing IPOs. In this paper, we examine the use of accounting information (from the pre-IPO years) and offering characteristics contained in an offering prospectus in determining the long-run success or failure of an IPO. Our objective is to determine if the information can be gainfully employed by an investor when investing in an IPO with a long-term perspective. Given that IPOs underperform in the long-run, a simple trading strategy could be to short sell every IPO.
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However, most investors are interested in knowing which firms they should be adding to their portfolios and which firms they should be avoiding. Hence, our analysis focuses on identifying potential future winners and losers. We select a random sample of 242 IPOs, approximately 25% of all IPOs between 1987 and 1991, for our study. For these firms, we purchase their offering prospectuses. After gathering the firm’s financial and operating performance and offering characteristics, we measure the relationship between the prospectus information and two measures of subsequent performance - subsequent stock return performance measured using the matched firm technique and subsequent outcomes such as if the firm reissues equity or delists within 5 years after the IPO. Results from logistic analysis show that it is more difficult to predict clear winners than to predict extremely poor performers. Furthermore, we document that prospectus information may be relevant in the short-run (one year after the IPO) in identifying which firms are likely to do better. There is no evidence to suggest any relationship between long-term stock return performance (3 years) and how the firm did prior to its IPO. The rest of the paper is organized as follows: Section 2 discusses the sample, research design and results from the univariate tests, section 3 presents the results from cross-sectional logistic regression analysis using firm and offering characteristics, and finally section 4 summarizes and concludes the study.
2. Sample and research design 2.1. Sampling procedure IPOs made between January 1, 1987 and December 31, 1991 are identified from the Investment Dealer’s Digest (various issues). A total of 979 firms met the following criteria: (1)
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the offer price was at least $1 and the offer size was at least $1.5 million, (2) the IPO was made on a firm commitment basis consisting of common stock only (all closed-end mutual fund, real estate investment trust, reverse LBOs, dual class shares or unit offerings were deleted), (3) the IPO firm’s SIC code was other than a regulated or financial firm, and (4) data on the firm’s first closing stock price was available within the first 5 trading days of the offering date. We refer to this set as the population of IPOs between 1987-91. Each firm was tracked for 5 years after the IPO to determine the eventual outcome. There were four categories of subsequent outcomes: IPO firms that, (1) made at least one seasoned equity offering (SEO) (the SEO group contained 252 firms),2 (2) were delisted due to an acquisition or a merger (the acquired group contained 62 firms), (3) were delisted due to financial distress (the failed group contained 108 firms), and (4) were still trading on the 5th anniversary of the IPO date and did not belong to the first three categories (the did-nothing group contained 557 firms). Since prospectus information is not available in a computer readable format and is expensive to acquire, it was decided that further analysis would be limited to a random sample consisting of 25 percent of the population. The firms in the population were sorted alphabetically and every fourth firm was selected, resulting in a random sample of 242 firms. Microfiches of IPO prospectuses for this random sample were purchased from Disclosure Information Incorporated. If a prospectus was a red herring document or a microfiche was not available for a firm on the initial list of 242 firms, it was replaced by the next firm from the original alphabetical ordering of the population. The final sample consists of 242 firms comprised of 63 SEO firms, 14 acquired firms, 27 failed firms, and 138 firms that were still trading on the 5th anniversary of the IPO date but did not belong to the first three classifications (did-nothing group). Table 1 provides
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a distribution of the firms in the sample by year and outcome type, the distribution of the population and the percentage of firms in the final sample in each category. The proportion of firms for each outcome type is close to 25 percent of the firms in the population in that outcome type. 2.2. Performance measure based on subsequent stock returns Post-IPO stock return performance is measured by computing the buy-and-hold abnormal returns (BHARs) using the matched firm technique as described in Loughran and Ritter (1995). For each IPO firm, we construct four separate benchmark matched firms as follows: firm size (measured by market value of equity), firm size and industry, book-to-market (B-M) ratio, and finally B-M ratio and industry. Similar to Loughran and Ritter (1995), the matched firms are selected on Decemeber 31 of the year preceding the IPO date for the sample firm. On December 31 of each year between 1986 and 1990 (IPOs are between 1987 and 1991), firms on CRSP and COMPUSTAT are ranked on the basis of size, size within the same 2-digit SIC code, B-M, and B-M within the same 2-digit SIC code, and a matched firm for each classification is selected that is closest in value (say size) and immediately higher than the corresponding value for the IPO firm. A firm is not selected as the benchmark if it issued equity in the previous five years. Furthermore, if a matched has insufficient stock return data over the five year post-IPO window, a second matched firm (and if need be a third) is substituted from that point onwards to the 5th anniversary of the IPO firm or its delisting date, whichever comes first. We also use the CRSP value-weighted index as an alternate benchmark. Canina, Michaely, Thaler and Womack (1998) suggest that the value-weighted index on the CRSP does not suffer from any compounding related bias when computing long-term buy-and-hold returns. We assume a buy-and-hold investment strategy excluding the initial day return. For each firm j, we assume that the investment is made
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at the end of the first day of trading and is held for 1, 3 and 5 years (corresponding to 252, 756 and 1260 trading days respectively) or the date of delisting, whichever comes first. (Results for the 5 year post-IPO window are not reported since they are qualitatively similar to those for the 3 year period.) For each benchmark, the BHARs are sorted from lowest to highest and placed into quartiles. Winners are defined as firms that belong to the uppermost quartile while losers are defined as firms that belong to the lowest quartile. 2.3. Classifications based on subsequent outcome The second performance measure used in the study defines success or failure in terms of subsequent outcomes within 5 years of the IPO. Sixty-three firms issued additional equity (SEO group) and are classified as successful.3
It must be noted, however, that there could potentially
be additional firms that may have significant amounts of cash flow for investment purposes from their operations and therefore do not have the need to seek external financing. These firms should also be rightfully classified as successful. Using an observable event such as an SEO therefore captures only a subset of the firms that have been successful after the IPO. For example, the 14 firms that were acquired after their IPO show superior excess stock return performance in the post IPO period and can therefore also be considered successful.4 Firms that delist and stop trading within 5 years of the IPO for reasons of financial distress are identified using the delisting codes available on CRSP.5 We classify these firms as failures. 2.4 Cross-sectional regression analysis In order to assess the usefulness of the information contained in the prospectus to identify subsequent winners and losers, we estimate logistic regression models using firm characteristics and offering characteristics as independent variables. As noted earlier, winners based on post-
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IPO stock return performance are defined as those firms that fall in the uppermost quartile based on BHARs while losers are defined as firms that belong to the lowest quartile. We use the oneyear and three-year BHARs based on size and industry matched firms and B-M and industry matched firms to define winners and losers.6 We also estimate logistics regressions based on firm characteristics and offering characteristics to determine if prospectus information is valuable in identifying firms that reissue equity and firms that delist due to financial distress within 5 years of the IPO date. To the extent that high quality firms are expected to reissue equity (see signaling models of IPOs such as Allen and Faulhaber (1989), Grinblatt and Hwang (1989) and Welch (1989)), we regard reissuing firms as successful. On the other hand, if the firm delists and stops trading within 5 years of the IPO date, we regard these firms as having failed. 2.5. Post-IPO performance Table 2 presents the results for 237 firms obtained from the analysis of the stock return performance, measured by wealth relatives, for the three year post IPO period. Data on stock returns was not available from the CRSP files for the remaining 5 firms. Results are presented for the full sample and sub-samples based on subsequent outcome for each benchmark. Consistent with the findings in Ritter (1991) and Loughran and Ritter (1995), we document that IPO firms underperform in the 3 years after the IPO. (Though not reported, results for the 5 year period document similar underperformance with all benchmarks.) It is interesting to note that there is no underperformance in the first year after the IPO when using B-M, size, and size and industry matched firms where the corresponding values of wealth relatives are 1.04, 1.05 and 1.01 respectively. The wealth relatives for the 3-year period vary from 0.69 when using the B-M matched firms to 0.93 when size is used as a benchmark.
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When the sample is broken up based on subsequent outcome, we observe systematic differences in the wealth relatives for the different groups. Consistent with our hypothesis that firms that reissue equity (labeled SEO firms) are likely to be the successful IPOs, we find wealth relatives greater than one, except in two instances, using all benchmarks for one, two and three years after the IPO. Over the three year period, using the size and industry matched firms, SEO firms show a wealth relative of 1.25. The overall mean using the B-M and the B-M and industry matched firms suggests that there is no underperformance for the SEO group. Results using the value-weighted index are similar to those obtained for the size matched portfolios. These results robustly indicate reissuing firms are successful as public firms and investors would have earned positive abnormal returns if they had invested in these IPOs in the immediate aftermarket. The 14 firms that subsequently merged or were acquired within 5 years also demonstrate superior performance relative to their matched firms over the 3 years after the IPO. Across all benchmarks, the wealth relatives are significantly above one, varying from 1.09 for the size and industry matched firms to a 2.07 for the B-M matched firms. Likewise, the wealth relatives are greater than one across all benchmarks for this group for the 2-year post-IPO window. Results for the group that “did-nothing” after the IPO are similar to those obtained for the full sample. The wealth relatives are consistently below one for all matched portfolios and across all the years. For the 3-year period, the wealth relatives range from 0.71 for the B-M matched firms to 0.96 for the size matched portfolios. Firms that subsequently delist as a result of financial distress (failed firms) predictably display the greatest degree of underperformance. For the 26 firms in this group, the one-year wealth relatives are even lower than the three-year wealth relatives for the full sample. Over the three years after the IPO, investors would have suffered significant losses in their wealth. The
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mean BHARs are significantly below one for the 3-year period with the results being relatively robust to the benchmark employed for measuring the abnormal performance. The mean wealth relatives range from 0.20 for the size matched firms to 0.29 for the size and industry and the B-M matched firms. Unlike other groups, for matched firm portfolios in this group, we find a monotonic decline in the performance of the IPO firms from the first year to the third year after the IPO. Though not reported in the table, it is interesting to point out that underperformance for this group is not limited to a few firms. From 22 firms that exhibit negative abnormal returns in the very first year the number grows to 25 for the 3-year period. The extreme poor performers in the IPO market exhibit underperformance relatively soon after they start trading. For the rest of the sample, more than 80% of the firms in the did-nothing group exhibit negative excess returns while 62% of the SEO firms exhibit positive excess returns over the 3 years after the IPO. For the overall sample of 237 firms, poor performance for IPO firms increases from 63% of the sample in the first year to 77% of the sample for the 3-year period after the IPO. The results in Tables 2 confirm the long-run underperformance documented in the IPO literature. However, two points are worth noting: first, there is no evidence of underperformance from our sample in the first year after the IPO, and second, there are clear differences between firms that reissue equity or get acquired and those that subsequently fail. Thus, even though over 77% of all the IPOs in this study underperformed, it may still be possible to identify IPO firms in the immediate aftermarket that are likely to show superior performance at least over the next one year. We undertake this analysis in Section 3.
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3. Empirical Results We begin first by presenting the results from the univariate analysis on firm characteristics and offering characteristics, categorized first by subsequent outcome and next by stock return performance. We next discuss the results obtained from the logistic regression analysis 3.1 Means tests based on subsequent outcome Table 3 presents the descriptive statistics and results from the means tests obtained for the variables from the IPO prospectus for the full sample and by outcome types. In Panel A, details of firm characteristics are shown. Firms in the SEO group and the acquired group are typically larger, older and have lower levels of debt compared to failed firms. Further, the fact that SEO firms are more likely to be growth oriented firms is supported by the high levels of spending on research and development (36%) compared to other categories. Acquired firms have the highest levels of operating income (15%) in the fiscal year prior to the IPO compared to a value of -12% for failed firms. The level of profitability for SEO firms is positive, but small. Finally, for SEO firms, the negative values of free cash flow coupled with the high levels of spending on R&D (which serves as a good proxy for growth opportunities) indicates that these firms are more likely to access the capital markets shortly after the IPO compared to other firms. Means tests show that there are significant differences in several firm-specific characteristics between firms that reissued equity versus those that did not, and firms that subsequently failed versus those that continued to trade. The mean age of 10.29 years for the SEO firms is significantly greater (at the 1% level) than the mean age of 6.39 years for the nonissuing firms. In addition, the SEO firms are significantly larger in terms of total assets and sales (at the 1% and 6% levels, respectively) compared to the non-issuing firms. Proportional spending on R&D at 36% and the ratio of total assets to sales at 2.56 are significantly greater than the
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corresponding numbers at 13.4% and 1.86 for the non-issuing firms. There is no difference in the proportions of long-term debt, tangible assets and free cash flow between the two groups. When the failed firms are compared against the non-failed firms, we again observe significant differences between the two groups. Failed firms are significantly younger, smaller in terms of total assets and sales, and have larger levels of long-term debt compared to the non-failed firms. Though not statistically significant, failed firms also show poor operating performance and free cash flow levels compared to the non-failed firms. Panel B of Table 3 shows the description of the offering characteristics also taken from the IPO prospectus. Once again, firms in the SEO and acquired groups sold larger amounts of equity at the IPO, had higher offering prices and employed more reputable investment banks to underwrite their issues compared to failed firms. However, the lower level of underpricing at 9.6% for SEO firms compared to an average underpricing value of 19.5% for failed firms is contrary to the predictions of the signaling models (Allen and Faulhaber (1989), Grinblatt and Hwang (1989) and Welch (1989)).7 Furthermore, the evidence that the percentage of shares sold by insiders, SECDOFF, of failed firms at 6.1% is significantly lower than the other three categories, which is contrary to the predictions of Leland and Pyle (1977). Means tests in Panel B show that SEO firms make significantly larger offerings at $49.28m and are underwritten by more reputable investment banks with a mean Carter-Manaster (CM) rank of 8.09 (at the 5% level) compared to the corresponding figures of $29.69m and 7.53 for the non-issuing firms. Furthermore, there are significant differences at the 1% level when we compare underwriting spreads and the Megginson and Weiss (MW) underwriter ranks for the two groups. Results for the means tests for the failed group versus the non-failed group in Panel B are striking. Seven variables relating to offering itself are significantly different between the two
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groups at the less than 1% level while the remaining two are differ at the 5% level. These significant differences suggest that information contained in the prospectus can play a useful role in determining the subsequent performance of the IPO firm. 3.2 Means tests based on subsequent stock return performance We use the 3-year BHARs based on size and industry matched firms and B-M and industry matched firms to place firms into quartiles. (Results using only size and B-M matched firms as benchmarks are similar and hence are not reported.) The mean values of the firm characteristics and the tests for the differences in the means between the highest and the lowest quartile for the two benchmarks are shown in Panel A of Table 4. Means tests for offering characteristics with the same portfolio benchmarks appear in Panel B of the table. Results in Panel A show that there are no significant differences in the values of the firm characteristics between the lowest and the highest quartiles when comparisons are based on size and industry matched firms. Only free cash flow is lower (at the 10% level) for firms in the highest quartile compared to the firms in the lowest quartile. Mean values across quartiles do, however, seem to suggest that firms in the lowest quartile are younger and smaller compared to firms in the highest quartile. Comparisons of firm characteristics between the lowest and the highest quartiles based on 3-year B-M and industry matched BHARs are similar except for firm size. We find that the mean values for total assets for firms in the lowest and highest quartiles are $35.56m and $191.64m and the difference is statistically significant at the 1% level. The corresponding values for sales revenue are $51.32m and $274.32m and the difference is statistically significant at the 5% level. None of the other variables are significantly different between the two quartiles. Panel B of Table 4 displays the results for offering characteristics between the lowest and
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the highest quartiles. Similar to the results reported in Panel A, we do not find any evidence of systematic differences in the offering characteristics when firms are ranked on the basis of BHARs computed using size and industry matched firms. On the other hand, when using B-M and industry matched firms, we find that firms in the lowest quartile make smaller offerings, with an average offer size of $20.57m, are underwritten by less reputable underwriters with a CM rank of 7.48, have an underwriting spread of 7.8% and underpriced around 13.1% on average. All these values are significantly different (at the 5% level) for the corresponding values of $55.05m for offer size, 8.24 for CM rank, underwriting spread of 7.2% and underpricing of 7.30% for firms in the highest quartile. Results in Tables 3 and 4 on variables taken from the IPO prospectuses for the different subgroups, formed on the basis of subsequent outcome and stock price performance, do appear to indicate systematic differences in the firm characteristics and the offering characteristics. The differences are more striking when the comparisons are based on subsequent outcomes than when they are based on the broader phenomenon of underperformance as measured by BHARs. Furthermore, the differences are more pronounced in firm characteristics than offering characteristics. We now turn our attention to investigating if the differences in the firm and offering characteristics of the different sub-groups are useful in predicting (a) a specific outcome type, and (b) classifying firms into winner and loser categories. 3.3 Logistic regressions to identify winners and firms that reissue equity We use logistic regressions to determine if the firms’ prospectus information is useful in predicting a specific outcome type or predicting subsequent winners and losers based on stock return performance. The logistic models are first estimated for predicting winners and firms that reissued equity. Results from this analysis are presented in tables 5 and 6.
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Table 5 depicts the results from the logistic regression analysis using firm characteristics as independent variables.8 In models 1 to 4, the binary dependent variable takes on a value of 1 when the firm is a winner and 0 otherwise, where a firm is classified as a winner if it belongs to the highest quartile of BHARs. Models 1 and 2 relate to using size and industry matched firms as benchmarks while models 3 and 4 are based on B-M and industry matched firms. For each benchmark, the BHARs for 1 year and 3 years are used when identifying winners.9 The results in Table 5 show that firms that spend more on research and development and those that have a higher ratio of total assets to sales are more likely to perform better, at least in the first year, after the IPO. Surprisingly, however, pre-IPO operating income is significantly negative related to the likelihood of a firm being a winner. Ex ante, one would expect that firms with positive operating income prior to going public should be indicative of better performance as public firms. The negative sign on this variable may suggest that IPO firms may be attempting to go public when their balance sheet fundamentals have improved considerably and are not likely to do any better in the foreseeable future. This is akin to the commonly proposed argument that managers make seasoned equity offerings when they consider their stock to be overvalued. None of the variables in model 2 are significant, clearly suggesting that prospectus information is probably valuable in the near term but becomes outdated fairly soon. Results for models 3 and 4 that use B-M and industry matched firms are similar to the first two models, with significant coefficients on R&D and operating income for predicting winners in the near term and no variable being significant for the long-term 3-year BHARs. Model 5 predicts a subsequent outcome type where the binary dependent variable takes on a value of 1 if the firm issues additional equity within 5 years after the IPO and 0 otherwise. We find that higher values for firm size, spending on research and development and levels of free
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cash flow are associated with a higher probability of the firm making additional equity offerings within 5 years of its IPO date. Spending on R&D has been used in the past as an indicator of growth firms (see for e.g. Indro, Leach and Lee (1999)). Therefore, it appears that growth firms are more likely to access the capital markets again as public firms by issuing additional equity. Though not significant, the negative sign on LTD/TA is also consistent with Myers’ (1977) argument that growth firms prefer to maintain low levels of debt and prefer to finance their investment opportunities with additional equity. Finally, the positive and significant coefficient on free cash flow suggests that firms that have performed well after their IPOs are more likely to approach the capital markets again with new equity offerings.10 Each of the five models were estimated again using offering characteristics as independent variables. The results are presented in Table 6. To minimize the impact of multicollinearity, we exclude offer price, offer size and underwriting spread from the regression analysis since these variables exhibit a high degree of collinearity with underwriter reputation. Furthermore, we use a relative measure of offer size (RELSIZE) measured as the ratio of offer size to total assets prior to the IPO rather than the actual dollar volume of the amount raised in the IPO.11 Except for RELSIZE in model 1, none of the other offering characteristics are statistically significant when using size and industry matched firms. However, when using B-M and industry matched firms, we find that coefficient on CM rank and RELSIZE are significantly positively related to first year BHARs while only underwriter reputation is important in the long-term. Thus, firms that offer more equity and are underwritten by more reputable banks are more likely to succeed as public firms. Finally, model 5 in Table 6 is estimated to identify offering characteristics that could be useful in predicting firms that reissue equity. The coefficient on the underwriter reputation variable is positive and statistically significant at the 5 percent level while
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the coefficient on SECDOFF is negative and statistically significant at the 10 percent level. IPOs that are underwritten by reputable investment banks and that have fewer shares sold by insiders at the initial public offering have a higher probability of making SEOs within 5 years of the IPO date. Note that underpricing is insignificant in predicting SEO actions by IPO firms. This result is inconsistent with the arguments put forward in the signaling literature but consistent with the findings in Jegadeesh, Weinstein and Welch (1993) and Garfinkel (1993). 3.4 Logistic regressions to identify losers and firms that fail We repeat the analysis in Tables 5 and 6 but this time set the binary dependent to predict losers and firms that fail after the IPO. As noted previously, losers are defined as firms belonging to the lowest quartile of BHARs. We estimate models using firm characteristic whose results appear in Table 7 and next using offering characteristics whose results appear in Table 8. Once again, five models are estimated using 1-year and 3-year BHARs based on size and industry matched firms and B-M and industry matched firms to define losers. Failed firms are defined as firms that delist due to financial distress within 5 years of the IPO date. Results in Table 7 show that firm characteristics are important only to the extent of predicting losers in the first year after the IPO. In model 1, the coefficients on total assets, spending on R&D and ratio of assets to sales are negative and statistically significant. Thus, smaller firms and firms that spend less on R&D are more likely to perform poorly after the IPO. These results are obtained when size and industry matched firms are used as benchmark returns. None of the variables in models 2, 3 and 4 are significant. Finally, in model 5, we estimate the regression to predict an outcome type of poor performance by setting the binary dependent equal to 1 if the firm failed and 0 otherwise. Several firm variables are found to be significant in predicting firms that failed. The results indicate that firms with higher leverage, smaller firms and
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firms that spend less on research and development are more likely to fail. Furthermore, firms with lower levels of free cash flow and a lower total assets to sales ratio are also more likely to fail.12 It is noteworthy that the pseudo-R2 value of 36.9% for model 5 is more than four times the next closest value of 9.3% for model 1. The regression overall is also significant at the less than 1% level. Table 8 presents the results for predicting losers and failed firms using offering characteristics. The lack of any predictive power for any of the variables in the first four models is striking. None of the offering characteristics are useful in predicting firms that show poor stock return performance over the one year and three years after the IPO. These results are robust to choice of the benchmark used. Finally, in model 5, we find that firms that are underwritten by less reputable underwriters and IPOs that have more number of risk factors listed in the prospectus are more likely to fail within 5 years of the offering. Model 5 which focused on failed firms versus all other IPOs again has the highest pseudo-R2 at 36.1% and most significant pvalues among all models investigated and the order of magnitude is much higher when compared with the next highest value of 3.1% for model 4. Overall, the results from logistic analysis from Tables 5 to 8 can be summarized as follows: (1) Firm characteristics have a higher discriminatory power compared to offering characteristics. (2) Prospectus information is more useful in predicting a subsequent outcome type, such as whether a firm will reissue equity or fail within a specified time period than the broader phenomenon of underperformance or superior performance based on BHARs. (3) Firm characteristics from the prospectus do appear to be useful in predicting winners at least over the first year after the IPO. Predictably, the usefulness of the information contained in the prospectus declines as the time horizon after the IPO increases. (4) Among the variables that are found to be
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important include firm size, spending on research and development, ratio of total assets to sales, free cash flow, underwriter reputation and the number of risk factors in the prospectus. Leverage and sale of secondary shares by insiders are also found to be marginally significant in some of the models. (5) The results are robust to the benchmark matched firm employed to compute BHARs when identifying winners and losers.
4. Summary and conclusions We study the usefulness of the information contained in the offering prospectus of IPOs (1) in separating the firms that show superior excess stock return performance (winners) and those that do poorly (losers), and (2) in predicting a specific outcome type such as an IPO firm reissuing equity or failing within 5 years of the IPO date. Using a random sample of all IPOs made between 1987 and 1991, and gathering data directly from the IPO prospectuses issued at the time of the offering, we find that firm size, research and development expenditure, free cash flow, underwriter reputation and the number of risk factors are significant in predicting the best and worst performers based on 1-year and 3-year buy-and-hold abnormal stock returns or a specific outcome type. In addition, we also document that leverage can be important in predicting a firm that is likely to fail and the proportion of shares sold by insiders can be important in predicting firms that later reissue additional equity or fail. The relationship between prospectus information and long-term (3-year) stock return performance is limited and very weak. However, prospectus information does appear to be useful in predicting superior and poor stock return performance in the first year after the IPO. We do not find underpricing to be an important variable in predicting the subsequent stock return performance or a specific outcome type. These results are robust to the choice of benchmark matched firms employed to compute buy-and-hold abnormal returns.
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In general, firm characteristics (financial statement information from the prospectuses) are more valuable than the information pertaining to the offering itself to measure subsequent performance. More specifically, we find that have higher levels of BHARs (winners) and firms that reissue additional equity within 5 years of the IPO date, are on average larger, spend more on research and development, have higher levels of free cash flow, are underwritten by more reputable investment bankers and have fewer number of risk factors listed in the prospectus compared to firms that are losers and failed firms. There is also a relationship between a specific outcome type and subsequent stock return performance. For example, firms reissue equity have wealth relatives substantially greater than one for all benchmarks (size, size and industry, B-M, BM and industry and CRSP value-weighted index) over the 3 years after the IPO. On the other hand, failed firms show substantial losses in stockholder wealth over the 3 years after the IPO as indicated by the low values on the wealth relatives for these firms.
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Footnotes 1.
Numerous investment houses, brokerage firms, analysts and government agencies regularly publish newsletters advising investors about the risks of investing in new issues. See for e.g. “How to Read a Prospectus: published by Missouri Securities Division of the Office of the Secretary of State, Jefferson City, MO. (http://mosl.sos.state.mo.us/sossec/prospec.html)
2.
We thank D.K. Speiss and J. Affleck-Graves for generously providing us with the data on seasoned equity offerings. Since the sample in Speiss and Affleck-Graves (1995) contains only observations until 1989, seasoned equity offerings made by our sample of IPO firms from 1990 to 1996 (five years after our last observation in 1991) were identified from various issues of the Investment Dealers’ Digest.
3.
Signaling models of IPO (Allen and Faulhaber (1989), Grinblatt and Hwang (1989) and Welch (1989)) suggest that issuers of higher quality willingly bear the costs of underpricing at the IPO since they plan to conduct SEOs once market prices for their stock are established. Hence, based on the predictions of these models, an ex post outcome where a firm conducts a SEO should indicate that the firm was successful after its IPO. Further, firms that make subsequent SEOs are not necessarily less risky. Consistent with Myers (1977), issuance of additional equity instead of debt suggests that these are more likely growth oriented firms that require additional investment capital but do not desire to be burdened with additional debt constraints.
4.
We also investigate models where firms with SEOs and acquired firms are combined into one group. We do this since firms that subsequently get acquired show significantly positive annual stock price performance over the 3 and 5 year windows after the IPO and thus clearly appear to be successful.
5.
CRSP identified seven reasons why firms were delisted due to financial distress: (1) insufficient number of market makers, (2) price fell below acceptable levels, (3) insufficient capital, surplus, and/or equity, (4) insufficient float or assets, (5) company request, bankruptcy, declared insolvent, (6) delinquent in filing, non-payment of fees, and (7) does not meet exchange’s guidelines for continued listing.
6.
We use only size and only B-M firms also as matched firms in all of the subsequent analysis. The results are always qualitatively similar to those reported for the size and industry and B-M and industry matched firms respectively. Hence, we do not report these results in the paper.
7.
Garfinkel (1993), Michaely and Shaw (1994) and Spiess and Pettway (1997) provide empirical evidence that is inconsistent with the predictions made by the signaling models. In fact, the evidence in Michaely and Shaw (1994) shows that empirical findings are more consistent with the adverse selection arguments proposed in Rock (1986).
21
8.
Age is not included in the regressions when using firm characteristics as independent variables since it is not part of the firm’s financial statements. When we repeated the regressions by including Age also as an independent variable (results not reported here), it was found to be insignificant.
9.
In each case where BHARs are used to determine winners and losers, as dependent variables, the analysis was repeated using the 5-year BHARs for the same two benchmark firms, size and industry and B-M and industry. In each case, the results do not change and hence are not reported in the paper.
10.
The positive and significant coefficient on free cash flow appears to be counter intuitive at first sight, since an increase in free cash flow should indicate an increase in agency costs for the firm (Jensen (1986)) which are best minimized through a debt offering. However, two issues are worth noting. First, we need to view the significantly positive sign on free cash flow together with the significantly positive sign on R&D spending. As noted earlier, these firms are more likely to be growth firms and prefer to issue equity to debt to avoid restrictive covenants. Secondly, even though our free cash flow measure is similar to the one proposed by Lehn and Poulsen (1989), other researchers (see Lang, Stulz and Walkling (1991)) have argued that this measure, though simple to compute, is more likely to be influenced by the effects of accrual accounting. Hence, this measure of free cash flow may simply be proxying for firm performance rather than the conceptual definition of free cash flow as proposed in Jensen (1986). In their study on bidder returns, Lang, Stulz and Walkling (1991) explore other definitions of free cash flow derived from operating income, inventories, accounts receivables, accounts payables, net income and depreciation. Since these data are not readily available in the offering prospectuses, we are unable to explore these alternative definitions of free cash flow in this study.
11.
The logistic regression were separately estimated by replacing relative offer size with offer size, and by replacing the Carter, Dark and Singh (1998) measure of underwriter reputation with the measure described in Megginson and Weiss (1991). The results were qualitatively similar and hence are not reported in the paper.
22
References Allen F., Faulhaber G., 1989. Signaling by underpricing in the IPO market. Journal of Financial Economics 23, 303-323. Canina L., Michaely R., Thaler R., Womack K., 1998. Caveat Compounder: A warning about using daily CRSP Equal-Weighted Index to compute long-run excess returns. Journal of Finance 53, 403-416. Carter R., Manaster S., 1990. Initial public offerings and underwriter reputation. Journal of Finance 45, 1045-1067. Carter R. B., Dark F.H., Singh A.K., 1998. Underwriter reputation, initial returns, and the longrun performance of IPO stocks. Journal of Finance 53, 285-312.. Fama E.F., 1998. Market efficiency, long-term returns, and behavioral finance. Journal of Financial Economics 49, 283-306. Garfinkel J.A., 1993. IPO underpricing, insider selling and subsequent equity offerings: Is underpricing a signal of quality? Financial Management 22, 74-83. Grinblatt M., Hwang C.Y., 1989. Signaling and the pricing of new issues. Journal of Finance 44, 393-420. Ibbotson R.G., Sindelar J., Ritter J.R., 1988. Initial public offerings. Journal of Applied Corporate Finance 1, 37-45. Indro D.C., Leach R.T., Lee W.Y., 1999. Sources of gains to shareholders from bankruptcy resolution. Journal of Banking and Finance 23, 21-47 Jain B. A., Kini O., 1994. The post-issue operating performance of IPO firms. Journal of Finance 49, 1699-1726. Jegadeesh N.M., Weinstein M., Welch I. 1993. Initial public offerings and subsequent equity offerings. Journal of Financial Economics 34, 153-175. Jensen M.C., 1986. Agency costs of free cash flow, corporate finance and takeovers. American Economic Review 76, 323-329. Kim M., Ritter J.R., 1999. Valuing IPOs. Journal of Financial Economics, forthcoming. Lang L.H.P., Stulz R., Walkling R.A., 1991. A test of the free cash flow hypothesis: The case of bidder returns. Journal of Financial Economics 29, 315-335. Lehn K., Poulsen A., 1989. Free cash flow and stockholder gains in going private transactions. Journal of Finance 44, 771-798.
23
Leland H., Pyle D., 1977. Informational asymmetries, financial structure and financial intermediation. Journal of Finance 32, 371-387. Loughran T., Ritter J.R., 1995. The new issues puzzle. Journal of Finance 50, 23- 51. Michaely R., Shaw W.H., 1994. The pricing of initial public offerings: Tests of adverse selection and signaling theories. Review of Financial Studies 7, 279-320. --------------------------, 1995. Does the choice of auditor convey quality in an initial public offering? Financial Management 24, 15-30. Megginson W., Weiss K., 1991. Venture capitalist certification in initial public offerings. Journal of Finance 46, 879-904. Myers S., 1977. Determinants of corporate borrowing. Journal of Financial Economics 5, 147175. Ritter J. R., 1991. The long-run performance of initial public offerings. Journal of Finance 46, 327. Rock K., 1986. Why new issues are underpriced. Journal of Financial Economics 15, 187-212. Spiess D. K., Affleck-Graves J., 1995. Underperformance in long-run stock returns following seasoned equity offerings. Journal of Financial Economics 38, 241-267. Spiess D. K., Pettway, R. H., 1997. The IPO and first seasoned equity sale: Issue proceeds, owner/managers’ wealth, and the underpricing signal. Journal of Banking and Finance 21, 967-988. Welch I., 1989. Seasoned offerings, imitation costs and the underpricing of initial public offerings. Journal of Finance 44, 421-449.
Total
1991
1990
1989
1988
1987
Year
Firms that made subsequent seasoned equity offerings 18 (63) 28.6% 4 (31) 12.9% 8 (38) 21.1% 10 (35) 28.6% 23 (85) 27.1% 63 (252) 25.0% Firms that subsequently were acquired 11 (41) 26.8% 1 (9) 11.1% 1 (7) 14.3% 1 (2) 50.0% 0 (3) 0.0% 14 (62) 22.6%
Firms that were delisted due to financial distress 11 (46) 23.9% 5 (25) 20.0% 6 (15) 40.0% 3 (10) 30.0% 2 (12) 16.7% 27 (108) 25.0%
Firms that were still trading and sold no additional equity 41 (158) 25.9% 16 (60) 26.7% 17 (73) 23.3% 17 (77) 22.1% 47 (189) 24.9% 138 (557) 24.7%
Total 81 (308) 26.3% 26 (125) 20.8% 32 (133) 24.1% 31 (124) 25.0% 72 (289) 24.9% 242 (979) 24.7%
A sample of 242 firms was randomly selected from a population of 979 firms that made initial public offerings between 1987 and 1991. Each firm was tracked for a period of 5 years after its IPO date to determine the eventual outcome. These outcomes included firms that: issued additional equity, were acquired or merged, were delisted due to financial distress and stopped trading, and were still trading on the 5th anniversary of the IPO but did not belong to the first three outcomes. The delisting codes available on CRSP were used to identify firms that delisted as a result of financial distress. The first, second and third rows in each cell indicate the number of firms in the final sample, the number of firms in the population and the percentage of population included in the final sample, respectively.
Table 1 Distribution of firms by year and subsequent outcome type
1 yr.
0.96
1.04
0.88
1.05
1.01
Benchmarks
VW Index
B-M Matched
B-M & Industry Matched
Size Matched
Size & Industry Matched 0.87
1.03
0.72
0.91
0.89
2 yrs.
All Firms (N=237)
0.91
0.93
0.80
0.69
0.84
3 yrs.
1.18
1.32
0.89
1.25
1.24
1 yr.
1.28
1.38
1.04
1.23
1.25
2 yrs.
1.25
1.15
0.96
1.04
1.12
3 yrs.
Firms that made subsequent SEOs (N=61)
0.80
0.86
0.99
1.63
0.95
1 yr.
1.16
1.47
1.75
2.45
1.38
2 yrs.
1.09
1.49
1.64
2.07
1.35
3 yrs.
Firms that were subsequently acquired (N=14)
0.96
0.99
0.98
0.95
0.89
1 yr.
0.74
0.93
0.84
0.76
0.77
2 yrs.
0.84
0.96
0.89
0.71
0.79
3 yrs.
Firms that did nothing (N=136)
1.04
0.78
0.72
0.86
0.68
1 yr.
0.42
0.46
0.39
0.46
0.39
2 yrs.
0.29
0.20
0.22
0.29
0.24
3 yrs.
Firms that delisted due to financial distress (N=26)
This table reports the wealth relatives over 1, 2 and 3 years from the IPO date. Mean buy-and-hold returns are computed for the IPO firm and a corresponding benchmark for the relevant post-IPO window or the delisting date, whichever occurs first, using the procedure described in Loughran and Ritter (1995). The benchmarks employed are the CRSP value-weighted index, and matched firms based on book-to-market (B-M), book-to-market plus industry, firm size (market capitalization), and firm size plus industry. Matched firm portfolios are constructed by identifying a matched firm that is closest (immediately above) to the IPO firm in terms of B-M or firm size. Classification for industry is based on the first two digits of the SIC code.
Table 2 Post IPO stock return performance by outcome type
236
234
234
236
229
239
234
226
Assets
Sales
LTD/TA
PPE/TA
R&D/Sales
OIBD/TA
TA/Sales
FCF
-0.12
2.74
0.06
0.19
0.21
0.26
184.06
117.68
All Firms 7.43
-0.16
2.56
0.01
0.36
0.24
0.28
388.09
249.69
0.03
0.96
0.15
0.05
0.23
0.37
282.11
226.79
-0.06
2.53
0.10
0.12
0.19
0.20
114.93
66.81
-0.41
2.84
-0.12
0.23
0.20
0.43
14.14
15.31
Mean values by subsequent outcome typeb Acquired Did-Nothing SEO Firms Firms Firms Failed Firms 10.29 12.15 6.12 4.64
0.7671
0.0040
0.6065
0.0668
0.1313
0.4793
0.0595
0.0030
0.1304
0.2698
0.1205
0.8155
0.8511
0.0326
0.0082
0.0002
t-test for testing the differences in the means (p-values reported) SEOs vs. NonFailed vs. NonSEO Firms Failed Firms 0.0146 0.0163
b
The number of observations in each sub-groups varies.
Variable definitions are as follows: Age is measured in years since inception to the IPO year, Assets and Sales are measured in $ million, LTD/TA is the proportion of long-term debt to total assets, PPE/TA is the proportion of property, plant and equipment to total assets, R&D/Sales is the proportion of research and development spending to sales, OIBD/TA is the proportion of operating income before depreciation to total asset, Sales/TA is the ratio of sales to total assets, and FCF is free cash flow measured as (OIBD less interest less taxes less dividends) divided by sales.
a
N 229
Variablesa Age
Mean values for all firms
Panel A: Firm characteristics
Mean values for firm characteristics (Panel A) and offering characteristics (Panel B) for the full sample and sub-samples by outcome type. Subsequent outcome type is determined by tracking each IPO firm for a period of 5 years after the IPO date or its delisting date, whichever came first. P-values from t-tests are also reported that test for the difference in means between the following groups: (a) firms that reissued equity within 5 years of the IPO date (SEO Firms) and those that did not reissue equity (Non-SEO Firms), and (b) firms that delisted due to financial distress within 5 years of the IPO date (Failed Firms) and those that were still trading on the 5th anniversary of the IPO date (Non-Failed Firms).
Table 3 Financial characteristics from the IPO prospectuses by outcome type and 3-year annualized excess HPRs
240
234
231
215
215
239
238
240
OFFPRICE
RISKFCTR
RELSIZE
UWRANK-CM
UWRANK-MW
UWSPREAD (%)
SECDOFF (%)
UNDPRC (%)
10.90
14.80
7.60
3.84
7.66
1.40
11.44
10.42
All Firms 34.51
9.60
13.10
7.20
5.07
8.09
1.25
11.10
11.27
6.60
15.90
6.70
4.53
8.59
0.58
9.40
14.93
10.50
17.90
7.50
3.74
7.75
1.39
10.82
10.66
19.50
6.10
9.50
0.71
5.29
2.26
16.04
5.00
Mean values by subsequent outcome typeb Acquired Did-Nothing SEO Firms Firms Firms Failed Firms 49.28 71.37 29.74 6.09
0.3021
0.3333
0.0009
0.0116
0.0384
0.3811
0.5086
0.1306
0.0176
0.0194
0.0001
0.0001
0.0002
0.0069
0.0001
0.0001
t-test for testing the differences in the means (p-values reported) SEOs vs. NonFailed vs. NonSEO Firms Failed Firms 0.0406 0.0001
b
The number of observations in each sub-groups varies.
Variable definitions are as follows: OFFSIZE is the total offer size in $ million, OFFPRICE is the offer price in $, RISKFCTR is the number of risk factors listed in the offering prospectus, RELSIZE is the ration of offer size to total assets prior to the offering, UWRANK-CM is the Carter, Dark and Singh (1998) underwriter rank, UWRANK-MW is the Megginson and Weiss (1995 ) underwriter rank, UWSPREAD is the underwriting spread, SECDOFF is the ratio of secondary shares as a proportion of the total number of shares sold in the offering, and UNDPRC is the degree of underpricing defined as the change in the stock price from the offering price to the close of the first trading day divided by the offering price.
a
N 240
Variablesa OFFSIZE
Mean values for all firms
Panel B: Offering characteristics
Table 3 (cont’d)
0.22
0.22
0.16
1.58
0.08
PPE/TA
R&D/Sales
OIBD/TA
TA/Sales
FCF
-0.12
1.67
-0.08
0.09
0.19
0.25
353.24
143.30
-0.18
1.78
0.10
0.15
0.17
0.25
93.71
59.87
-0.32
1.65
-0.01
0.32
0.20
0.27
221.36
164.73
0.1006
0.7212
0.1901
0.6371
0.4714
0.4983
0.2697
0.2910
-0.24
1.64
0.03
0.16
0.19
0.27
51.32
35.56
-0.20
1.66
-0.03
0.10
0.20
0.30
406.68
172.18
-0.07
1.92
0.12
0.20
0.18
0.26
71.68
78.21
-0.07
1.69
0.06
0.14
0.21
0.19
274.32
191.64
0.3811
0.8192
0.8097
0.8785
0.5237
0.1793
0.0450
0.0107
Mean values and test for differences in the means between highest and lowest quartiles of BHARs (Book-to-Market & Industry Matched Firms) p-value for Q2 Q3 Q4 Q1 Mean (highest) (lowest) (Q1 & Q4) 7.26 7.29 6.98 10.00 0.3473
Variable definitions are as follows: Age is measured in years since inception to the IPO year, Assets and Sales are measured in $ million, LTD/TA is the proportion of long-term debt to total assets, PPE/TA is the proportion of property, plant and equipment to total assets, R&D/Sales is the proportion of research and development spending to sales, OIBD/TA is the proportion of operating income before depreciation to total asset, Sales/TA is the ratio of sales to total assets, and FCF is free cash flow measured as (OIBD less interest less taxes less dividends) divided by sales.
a
0.23
104.72
Sales
LTD/TA
95.56
Assets
Variablesa Age
Mean values and test for differences in the means between highest and lowest quartiles of BHARs (Size & Industry Matched Firms) p-value for Q2 Q3 Q4 Q1 Mean (highest) (lowest) (Q1 & Q4) 7.26 6.57 8.89 8.90 0.5044
Panel A: Firm characteristics
Mean values for firm characteristics (Panel A) and offering characteristics (Panel B) by quartiles are reported. Quartile are constructed on the basis of three year buy-and-hold abnormal returns (BHARs) using the matched-firm methodology, where the matched firm is selected first on the basis of size (market capitalization) and industry and next on the basis of book-to-market and industry. P-values from t-tests are also reported that test for the difference in means between the highest and lowest quartiles.
Table 4 Financial characteristics from the IPO prospectuses by 3 year post-IPO Buy-and-Hold Abnormal Returns
7.40
UWSPREAD (%) 15.30
11.90
7.60
4.07
7.70
1.33
11.96
10.30
17.90
7.60
3.17
7.58
1.36
10.29
9.60
14.50
7.50
4.71
8.04
1.21
11.65
0.6293
0.8207
0.8008
0.2569
0.1351
0.2811
0.9272
13.10
16.20
7.80
3.83
7.48
1.55
11.48
9.76
13.06
12.70
7.70
3.29
7.59
1.24
11.82
10.75
8.40
15.40
7.60
3.08
7.50
1.47
10.48
10.44
7.30
13.80
7.20
5.11
8.24
1.16
10.98
11.18
0.0312
0.5718
0.0395
0.1550
0.0403
0.2966
0.6341
0.1698
Mean values and test for differences in the means between highest and lowest quartiles of BHARs (Book-to-Market & Industry Matched Firms) p-value for Q2 Q3 Q4 Q1 Mean (highest) (lowest) (Q1 & Q4) 20.57 35.12 21.78 55.05 0.0137
Variable definitions are as follows: OFFSIZE is the total offer size in $ million, OFFPRICE is the offer price in $, RISKFCTR is the number of risk factors listed in the offering prospectus, RELSIZE is the ration of offer size to total assets prior to the offering, UWRANK-CM is the Carter, Dark and Singh (1998) underwriter rank, UWRANK-MW is the Megginson and Weiss (1995 ) underwriter rank, UWSPREAD is the underwriting spread, SECDOFF is the ratio of secondary shares as a proportion of the total number of shares sold in the offering, and UNDPRC is the degree of underpricing defined as the change in the stock price from the offering price to the close of the first trading day divided by the offering price.
a
8.50
3.69
UWRANK-MW
UNDPRC (%)
7.39
UWRANK-CM
15.50
1.58
RELSIZE
SECDOFF (%)
11.56
0.2567
RISKFCTR
10.18
OFFPRICE
10.03
11.34
Variablesa OFFSIZE 10.67
Mean values and test for differences in the means between highest and lowest quartiles of BHARs (Size & Industry Matched Firms) p-value for Q2 Q3 Q4 Q1 Mean (highest) (lowest) (Q1 & Q4) 39.33 30.76 23.73 41.77 0.8672
Panel B: Offering characteristics
Table 4 (cont’d)
Table 5 Logistic regression analysis using firm characteristics as independent variables (Winners and firms that reissued equity) In this table, results from logistic regression analysis for five separate models are reported. The descriptions of the models are as follows. Models 1 and 2 use size and industry matched portfolios to calculate buy-and-hold abnormal returns (BHARs) for 1 and 3 years respectively to identify winners (highest quartiles). Models 3 and 4 use book-tomarket and industry matched portfolios to calculate buy-and-hold abnormal returns (BHARs) for 1 and 3 years respectively to identify winners (highest quartiles). For models 1 to 4, the dependent variable for winners is classified as one and zero otherwise. Model 5 classifies the dependent variable as 1 if the firm reissues equity within 5 years of the IPO date and zero otherwise. Description of variables is provided in Table 3. Standard errors are shown in parentheses.
Independent Variables
Size and Industry Matched Portfolios Model 2 Model 1 Highest Quartile Highest Quartile versus Others versus Others 3-year BHARs 1-year BHARs
B-M and Industry Matched Portfolios Model 4 Model 3 Highest Quartile Highest Quartile versus Others versus Others 3-year BHARs 1-year BHARs
Model 5 SEOs versus Non-SEO Firms
Intercept
-1.59*** (0.52)
-1.99*** (0.56)
-1.39*** (0.53)
-1.77*** (0.59)
-1.88*** (0.38)
LTD/TA
-0.24 (0.76)
0.56 (0.64)
-1.11 (0.77)
-1.55 (0.78)
-0.03 (0.63)
Log(ASSETS)
0.05 (0.11)
0.11 (0.10)
0.10 (0.11)
0.23 (0.11)
0.21** (0.10)
PPE/TA
-0.97 (0.79)
0.22 (0.93)
0.20 (0.66)
1.08 (0.94)
0.74 (0.55)
RD/SALES
0.77** (0.38)
0.09 (0.31)
0.62* (0.37)
0.08 (0.39)
0.91** (0.39)
OIBD/TA
-2.01** (1.03)
-0.16 (0.71)
-1.77** (0.88)
-0.21 (0.99)
-0.81 (0.54)
TA/SALES
0.41*** (0.16)
0.22 (0.17)
0.20 (0.15)
0.08 (0.18)
0.01 (0.03)
0.52 (0.46)
-0.02 (0.33)
0.46 (0.42)
0.04 (0.44)
0.47** (0.23)
211.44 0.072 0.0329 220
217.52 0.017 0.8220 220
205.20 0.040 0.3184 216
198.18 0.044 0.2428 216
241.023 0.094 0.0020 226
FCF -2 Log(L) Psuedo-R2 p-value N *** ** *
, , indicates significance at the 1, 5 and 10 percent levels respectively.
Table 6 Logistic regression analysis using offering characteristics as independent variables (Winners and firms that reissued equity) In this table, results from logistic regression analysis for five separate models are reported. The descriptions of the models are as follows. Models 1 and 2 use size and industry matched portfolios to calculate buy-and-hold abnormal returns (BHARs) for 1 and 3 years respectively to identify winners (highest quartiles). Models 3 and 4 use book-tomarket and industry matched portfolios to calculate buy-and-hold abnormal returns (BHARs) for 1 and 3 years respectively to identify winners (highest quartiles). For models 1 to 4, the dependent variable for winners is classified as one and zero otherwise. Model 5 classifies the dependent variable as 1 if the firm reissues equity within 5 years of the IPO date and zero otherwise. Description of variables is provided in Table 3. Standard errors are shown in parentheses.
Independent Variables
Size and Industry Matched Portfolios Model 1 Model 2 Highest Quartile Highest Quartile versus Others versus Others 1-year BHARs 3-year BHARs
B-M and Industry Matched Portfolios Model 3 Model 4 Highest Quartile Highest Quartile versus Others versus Others 1-year BHARs 3-year BHARs
Model 5 SEOs versus Non-SEO Firms
Intercept
-1.27 (1.14)
-2.00* (1.24)
-3.95*** (1.38)
-3.32** (1.45)
-2.79*** (1.11)
UWRANK-CM
0.08 (0.11)
0.14 (0.12)
0.31** (0.14)
0.03** (0.15)
0.21** (0.11)
SECDOFF
-1.55 (1.04)
0.03 (0.86)
-0.71 (0.99)
-0.69 (0.97)
-1.69* (0.95)
UNDPRC
1.13 (1.31)
-0.02 (1.40)
0.39 (1.47)
-2.77 (2.02)
-0.46 (1.30)
RISKFCTR
-0.07 (0.05)
-0.02 (0.05)
0.01 (0.05)
-0.00 (0.06)
0.05 (0.05)
RELSIZE
0.22* (0.12)
-0.11 (0.17)
0.21* (0.13)
-0.11 (0.19)
-0.09 (0.13)
-2 Log(L) Psuedo-R2 p-value N
187.13 0.037 0.2225 207
188.66 0.017 0.6778 207
178.08 0.045 0.1597 200
176.14 0.061 0.0564 200
221.37 0.039 0.1245 205
*** ** *
, , indicates significance at the 1, 5 and 10 percent levels respectively.
Table 7 Logistic regression analysis using firm characteristics as independent variables (Losers and firms that delisted due to financial distress) In this table, results from logistic regression analysis for five separate models are reported. The descriptions of the models are as follows. Models 1 and 2 use size and industry matched portfolios to calculate buy-and-hold abnormal returns (BHARs) for 1 and 3 years respectively to identify losers (lowest quartiles). Models 3 and 4 use book-tomarket and industry matched portfolios to calculate buy-and-hold abnormal returns (BHARs) for 1 and 3 years respectively to identify losers (lowest quartiles). For models 1 to 4, the dependent variable for winners is classified as one and zero otherwise. Model 5 classifies the dependent variable as 1 if the firm is delisted within 5 years of the IPO date due to financial distress and zero otherwise. Description of variables is provided in Table 3. Standard errors are shown in parentheses.
Independent Variables
Size and Industry Matched Portfolios Model 2 Model 1 Lowest Quartile Lowest Quartile versus Others versus Others 3-year BHARs 1-year BHARs
B-M and Industry Matched Portfolios Model 4 Model 3 Lowest Quartile Lowest Quartile versus Others versus Others 3-year BHARs 1-year BHARs
Model 5 Failed versus Non-Failed Firms
Intercept
0.58 (0.66)
-0.56 (0.58)
-0.04 (0.66)
-0.43 (0.59)
-0.67 (0.55)
LTD/TA
0.29 (0.78)
-0.45 (0.72)
0.69 (0.75)
-0.21 (0.75)
1.57* (0.87)
-0.24** (0.13)
-0.07 (0.11)
-0.16 (0.12)
-0.10 (0.12)
-0.70*** (0.19)
PPE/TA
-1.63 (1.06)
0.98 (0.92)
-1.54 (1.08)
-0.22 (0.99)
-0.39 (0.87)
RD/SALES
-1.01* (0.63)
0.20 (0.48)
-1.25 (1.05)
-0.19 (0.50)
-4.77** (2.15)
OIBD/TA
1.84* (1.05)
0.03 (0.83)
0.89 (0.78)
0.75 (0.81)
0.59 (1.03)
TA/SALES
-0.57** (0.23)
-0.25 (0.20)
-0.31 (0.21)
-0.14 (0.19)
-0.08** (0.04)
0.15 (0.56)
0.43 (0.48)
0.08 (0.53)
-0.06 (0.45)
-1.46** (0.64)
206.38 0.093 0.0076 220
214.78 0.037 0.3440 220
197.93 0.045 0.2564 216
197.93 0.029 0.5797 216
111.73 0.369 0.0001 226
Log(ASSETS)
FCF -2 Log(L) Psuedo-R2 p-value N *** ** *
, , indicates significance at the 1, 5 and 10 percent levels respectively.
Table 8 Logistic regression analysis using offering characteristics as independent variables (Losers and firms that delisted due to financial distress) In this table, results from logistic regression analysis for five separate models are reported. The descriptions of the models are as follows. Models 1 and 2 use size and industry matched portfolios to calculate buy-and-hold abnormal returns (BHARs) for 1 and 3 years respectively to identify losers (lowest quartiles). Models 3 and 4 use book-tomarket and industry matched portfolios to calculate buy-and-hold abnormal returns (BHARs) for 1 and 3 years respectively to identify losers (lowest quartiles). For models 1 to 4, the dependent variable for winners is classified as one and zero otherwise. Model 5 classifies the dependent variable as 1 if the firm is delisted within 5 years of the IPO date due to financial distress and zero otherwise. Description of variables is provided in Table 3. Standard errors are shown in parentheses.
Independent Variables Intercept
Size and Industry Matched Portfolios Model 2 Model 1 Highest Quartile Highest Quartile versus Others versus Others 3-year BHARs 1-year BHARs
B-M and Industry Matched Portfolios Model 4 Model 3 Highest Quartile Highest Quartile versus Others versus Others 3-year BHARs 1-year BHARs
Model 5 SEOs versus Non-SEO Firms
-0.95 (1.10)
-1.10 (1.07)
-0.45 (1.07)
-0.94 (1.09)
-2.82* (1.59)
0.02 (0.11
-0.07 (0.10)
-0.10 (0.10)
-0.05 (0.11)
-0.28** (0.13)
SECDOFF
0.42 (0.82)
0.60 (0.84)
0.03 (0.90)
0.34 (0.87)
-1.73 (1.81)
UNDPRC
-0.91 (1.45)
-1.95 (1.48)
-0.08 (1.33)
2.01 (1.28)
2.50 (1.76)
RISKFCTR
0.02 (0.05)
0.05 (0.05)
0.01 (0.05)
-0.01 (0.05)
0.20*** (0.07)
RELSIZE
-0.21 (0.17)
0.07 (0.12)
0.02 (0.13)
0.16 (0.12)
-0.23 (0.15)
-2 Log(L) Psuedo-R2 p-value N
191.70 0.014 0.7479 207
197.94 0.021 0.5194 207
188.71 0.008 0.9057 200
195.30 0.031 0.2921 200
86.09 0.361 0.0001 205
UWRANK-CM
*** ** *
, , indicates significance at the 1, 5 and 10 percent levels respectively.
