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Electronic Theses, Treatises and Dissertations The Graduate School

2010 Two Essays on the Intended Use of Proceeds of Seasoned Offerings David E. Bray

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COLLEGE OF BUSINESS

TWO ESSAYS ON THE INTENDED USE OF PROCEEDS

OF SEASONED EQUITY OFFERINGS

By

DAVID E. BRAY

A Dissertation submitted to the Department of Finance in partial fulfillment of the requirements for the degree of Doctor of Philosophy

Degree Awarded: Summer Semester, 2010 The members of the committee approve the dissertation of David E. Bray defended on July 27, 2010.

______David R. Peterson Professor Directing Dissertation

______Thomas W. Zuehlke University Representative

______Don M. Autore Committee Member

______Gary A. Benesh Committee Member

Approved:

______William A. Christiansen, Chair, Department of Finance

______Caryn Beck-Dudley, Dean, College of Business

The Graduate School has verified and approved the above-named committee members.

ii

I dedicate this to Mom, Dad, Amy, Poppa, Grannie, and Jackson.

iii ACKNOWLEDGEMENTS

I would like to acknowledge my dissertation committee members for providing guidance and helpful, timely feedback throughout the entire research process:

David R. Peterson (Chair): He would often say, “things will always take longer than you think.” And I have come to the realization that truer words have never been spoken. Thank you for the patience and direct guidance you provided as my chair.

Don M. Autore: Thank you for working closely with me on the equity issuance topic for this dissertation and providing insight and expertise on the subject.

Gary A. Benesh: Thank you for always encouraging me to “work harder” (other words may have been used); and at times, that is exactly what I needed to hear from someone I respect so highly as a Professor.

Tom W. Zuehlke: Thank you for the Econometrics training from the beginning and providing me with the best “outside” committee member one could hope for.

In addition, I would like to thank William A. Christiansen for providing me with an excellent Ph.D. advisor when I first entered the program, and now as an outstanding Department Chairman. I would like to thank all of my family, friends, and former students who often would ask, “when are you going to finish?” I look forward to letting them all know that I have successfully defended this dissertation. At times throughout the process, SAS (statistical software) and I were not seeing “eye to eye” and Adam Smedema offered some helpful advice; thus, thanks Adam. Thank you to the Florida State University for providing me with an excellent educational experience and the awarding of three degrees. Any errors in this manuscript are my own.

iv TABLE OF CONTENTS

List of Tables…………………………………………………………………… vi 1. INTRODUCTION………………………………………………………….. 1 2. INTENDED USE OF PROCEEDS AND THE LONG-RUN PERFORMANCE OF SEASONED EQUITY ISSUERS………………….. 3 Introduction…………………………………………………………….. 3 Sample selection, use of proceeds classification, and issuer characteristics…………………………………………………………… 7 Methodology……………………………………………………………. 9 Empirical Results………………………………………………………... 14 Conclusion………………………………………………………………. 18 3. DO INSTITUTIONAL INVESTORS PAY ATTENTION TO THE INTENDED USE OF PROCEEDS OF SEASONED EQUITY ISSUERS?.. 28 Introduction……………………………………………………………... 28 Literature Review………………………………………………………... 33 Hypotheses Development……………………………………………….. 43 Sample selection and descriptive statistics………………………………. 49 Methodology…………………………………………………………….. 51 Empirical Results………………………………………………………… 55 Conclusion……………………………………………………………….. 64 4. REFERENCES……………………………………………………………….. 89 5. BIOGRAPHICAL SKETCH…………………………………………………. 93

v LIST OF TABLES

CHAPTER 2 Table 1: Descriptive statistics of SEOs by intended use of funds categories… 20

Table 2: performance in the three years following issuance…………… 21

Table 3: Calendar time regressions of long-run stock returns………………... 23

Table 4: Median operating income scaled by sales…………………………… 25

Table 5: Median operating income scaled by total assets…………………….. 26

Table 6: Regressions of changes in operating income………………………… 27

CHAPTER 3

Table 1: Distribution of SEO issuances and sample descriptive statistics…….. 66

Table 2: Size and book-to-market buy-and-hold abnormal returns for SEOs…. 67

Table 3: Size and industry buy-and-hold abnormal returns for SEOs…………. 68

Table 4: Size and pre-issue run-up buy-and-hold abnormal returns for SEOs… 69

Table 5: Calendar-time portfolio regressions by change in institutional ownership of SEOs…………………………………………………… 70

Table 6: Regressions for changes in institutional ownership from pre- to post-Reg FD…………………………………………………………. 74

Table 7: Institutional ownership patterns in Seasoned Equity Offerings (Full Sample Period)…………………………………………………. 75

Table 8: Institutional ownership patterns in Seasoned Equity Offerings (Pre-Regulation Fair Discolsure)…………………………………….. 77

Table 9: Institutional ownership patterns in Seasoned Equity Offerings (Post-Regulation Fair Discolsure)…………………………………….. 79

Table 10: Intended use of proceeds of SEOs and buy-and-hold abnormal returns……………………………………………………………… 81

vi Table 11: Calendar-time portfolio regressions by the intended use of proceeds of SEOs………………………………………………….. 83

Table 12: Regressions for change in IO: Investment and (pre- vs. post-Reg FD)……………………………………………… 87

Table 13: Calendar-time portfolio regressions by institutional ownership and the intended use of proceeds of SEOs……………………………… 88

vii CHAPTER 1

INTRODUCTION

Financial economists have long been interested in uncovering predictable patterns in stock prices and the underlying factors which are the determinants of these potential patterns. By undertaking this area of research and analysis, financial markets arguably become more efficient and transparent for all market participants. The search for the explanatory variables that drive equity securities’ prices is the sole research agenda for many in academia. In this dissertation, I also investigate a potential determinant of stock prices in the seasoned equity (SEO) market: the intended use of proceeds of SEOs. Many researchers have explored the ex-post, or the actual uses of proceeds from SEOs by public firms, but the importance of the ex-ante, or intended use of proceeds remains an open empirical debate. In the first essay of this dissertation, I investigate the relation between the intended use of proceeds of SEOs and the long-run performance of these issuers. Can an investor use the intended use of proceeds variable to predict which SEO firm will outperform their peer SEO firms? The study investigates SEO performance based on a stock-return measure and from an operating performance standpoint. The “long-run” time-horizon is the three-year period subsequent to the SEO issuance. The results of the first essay provide market participants with knowledge that may be utilized when investing in SEO firms. The intended use of proceeds is a publicly-available data item that is provided to investors before the issuance of the SEO. And if the findings of the first essay are substantiated by other researchers in the future, market participants who invest in SEOs should pay attention to the intended use of proceeds.1 The research question for the second essay was generated via the literature review process for the first essay. Gibson et. al (2004) conclude that institutional investors are smart enough to select only those issuers, out of a pool of all SEOs, which outperform

1 The first essay of this dissertation is published in an academic journal. The citation is as follows: “Intended use of proceeds and the long-run performance of seasoned equity issuers” with Don M. Autore and David R. Peterson, Journal of , Vol. 15, Issue 3, June 2009, p.358-367.

1 their peers over the subsequent one-year period. The sample is 1980 to 1994, and this period does not include the passage of Regulation Fair Disclosure (Reg FD) in late 2000. The second essay studies a different sample period, 1997 through 2007, to shed light on the “smart money” debate, while incorporating Reg FD. Many researchers refer to institutional investors as “smart” investors based on findings that suggest institutions earn abnormal returns above what individuals may earn investing on their own (see Gibson et. al, 2004, and Grinblatt and Titman,1989). In addition, with the recent publications of Walker and Yost (2008) and Autore et. al (2009) the second essay incorporates the intended use of proceeds variable into the institutional investor framework to answer: (1) do institutions pay attention to the intended use of proceeds of seasoned equity issuers? and (2) has the reliance by institutions on the intended use of proceeds variable changed through time; namely, before and after the passage of Reg FD?

2 CHAPTER 2

INTENDED USE OF PROCEEDS AND THE LONG-RUN PERFORMANCE OF SEASONED EQUITY OFFERINGS

Introduction

Loughran and Ritter (1995) and Spiess and Affleck-Graves (1995) document that seasoned equity issuers experience poor long-run stock performance compared to matched non-issuers. Supportive evidence is provided by, among others, Eberhart and Siddique (2002) who compare equity issuers’ long-run stock returns to their returns, and Burch et al. (2004) who conduct out-of-sample tests using 1930s and 1940s issuers. The findings of these studies support the view that managers announce seasoned equity offerings (SEOs) when the company’s stock is overvalued, the market does not revalue the stock appropriately, and the stock is still overvalued when it is issued. The implication is that managers of equity issuing firms behave opportunistically by exploiting market misvaluation and investors are slow to react. Loughran and Ritter (1997) provide evidence that SEO issuers experience a post- issue decline in operating performance and link this decline to issuers’ poor subsequent stock returns. They explain that when issuing firms experience a decline in operating performance post-issue, investors recognize that the stock is still overvalued and adjust prices downward, and thus we observe the post-issuance stock underperformance. From an economic standpoint, it is important to understand why we observe poor long-run stock and operating performance subsequent to SEOs. The purpose of this study is to link the well-documented new issues puzzle with the intended use of proceeds as stated in the firm’s proxy statement. The stated use of proceeds on issuers’ registration statements (S-3 form) filed with the Securities and Exchange Commission (SEC) is almost always investment, debt repayment, or general

3 corporate purposes. An issuer’s stated use could provide insights about the firm’s motives. Firms raise external equity capital for a variety of reasons. One of the basic tenets of corporate financial theory is that firms with potentially value-adding investment projects, but not enough internal funds to finance the projects, should raise external capital to invest and expand. The equity markets provide a means for firms to sell stock and use the proceeds for investment purposes, such as acquisitions, capital expenditures, and research and development. An alternative use of proceeds to increasing investment is recapitalization; firms can issue equity and use the proceeds to repay debt obligations. Finally, some firms might not have a specific purpose for raising equity, or at least might not wish to reveal the intended purpose. We hypothesize that firms issuing equity specifically for investment purposes are more likely to use the proceeds for value-adding investments and less likely to be opportunistic market timers. Accordingly, we expect little evidence of long-run underperformance for these issuers. This hypothesis is consistent with the findings of Walker and Yost (2008) that issuers stating specific investment plans experience a relatively favorable market reaction at the offer announcement and an improvement in industry-adjusted operating performance. There are tax advantages to debt financing, and debt is typically a cheaper source of external financing than equity. Unless debt is excessive, recapitalizing by issuing equity to pay down debt might reduce shareholder value. To avoid this, management could recapitalize debt by issuing stock when investors are overly optimistic about the firm’s future prospects, potentially resulting in relative devaluation over the long-run. For example, Hertzel and Li (2007) find that issuing firms that are overvalued tend to reduce debt after the issue. A different insight is offered by Walker and Yost (2008), who find that firms with a stated intention of paying down debt actually have leverage ratios three years after the SEO that are similar to leverage ratios prior to the SEO. Thus, firms issuing equity to refinance may be opportunistic market timers and, therefore, we expect these firms to experience poor long-run performance. Often firms are vague and state only that the funds will be used for general corporate purposes. We hypothesize that these firms are more likely to be timing share

4 overvaluation compared to firms that have specific investment purposes. Consequently, we expect negative long-run performance for these firms. We examine buy-and-hold and calendar time abnormal returns during the three years following SEOs. We find significant negative average abnormal returns when the stated intended use of proceeds is recapitalization, weaker evidence of negative abnormal returns when the stated use is general corporate purposes, and insignificant average abnormal returns when the issuer cites specific investments plans for the proceeds. Moreover, we provide evidence of significant declines in industry-adjusted operating performance subsequent to the issue when the intended use the proceeds is recapitalization or general corporate purposes, but observe little evidence of significant declines when investment is the stated intention. We find significant differences in subsequent performance between firms with a classification of investment and those classified otherwise. These results suggest that issuers that cite specific investment plans in their S-3 form are credibly signaling profitable investment opportunities, whereas issuers that cite debt repayment or general corporate purposes are more likely to be signaling insiders’ beliefs that future firm prospects are less favorable than the current share price reflects. Our findings are consistent with Hertzel and Li (2007), who find that issuers with higher growth options invest more after the SEO and do not experience poor post-issue stock returns, but issuers with greater overvaluation decrease long-term debt and increase cash after the issue and suffer poor long-run stock performance. Our findings differ, however, from the result of Walker and Yost (2008) that issuers intending to decrease debt have subsequent improvements in industry-adjusted operating performance. Like us, several recent studies partition seasoned issuers into specific classifications when studying long-run stock performance to provide insights about issuer motivations. Cornett et al. (1998) find that banks that conduct voluntary SEOs experience poor subsequent stock performance, whereas banks that conduct involuntary SEOs exhibit no abnormal performance. Kahle (2000) finds that new sales in which there is abnormal insider selling prior to the issue underperform in the long run, whereas those with abnormal buying do not. Clarke et al. (2004) find that secondary SEOs underperform in the long-run only when the offering is sold by an insider. These

5 studies collectively indicate that managers are able to take advantage of temporarily high prices by issuing overvalued equity, and investors are not quick to react. Finally, Gibson et al. (2004) find that issuers with the greatest increase in institutional investment around the offer exhibit significantly more favorable abnormal stock returns in the following year than issuers with the greatest decrease in institutional investment. In a closely related study, Jeanneret (2005) examines post-SEO long-run stock returns of French issuers partitioned by the intended use of proceeds. He provides evidence of underperformance in issues where the stated purpose is investment, but reports no abnormal performance when the stated purpose is recapitalization. Our evidence on long-run returns differs markedly from his. We examine U.S. firms, study a larger sample of issuers, and corroborate our long-run stock return analysis with similar findings for operating performance. Our study contributes to the literature in four ways. First, this paper is the first, to our knowledge, to investigate the link between the ex-ante stated use of proceeds and post-SEO long-run stock performance in the U.S. equity markets. Next, we take a second look at changes in operating performance across ex-ante stated uses and provide important new insights by employing a broader sample than that studied by Walker and Yost (2008). Our third contribution is that the partitioning of SEOs on the basis of the intended use of proceeds provides an opportunity to distinguish between timing and non- timing motives. Fourth, we provide evidence on the predictive ability of publicly- available information (i.e. the stated use of proceeds) for long-run returns of SEOs. Providing evidence of such ex-ante predictive ability is important for both investors and researchers of market efficiency. In this regard, our study differs from those that examine firms’ ex-post use of proceeds, such as Hertzel and Li (2007) and Kim and Weisbach (2008).2

2 Our study is related to studies that examine the intended use of proceeds for IPOs. Ljungqvist and Wilhelm (2003) report greater underpricing when the use of proceeds is to fund operating expenses as opposed to capital expenditures, and Leone et al. (2007) find lower underpricing when there is greater specificity about the intended use of proceeds. Apparently investors view specificity about the intended use of proceeds favorably. Our evidence supports such a view in the context of SEOs, provided that the specificity regards to future investments. Busaba et al. (2001) provide evidence that IPOs in which the intended use of proceeds is to pay down debt are more likely to be withdrawn, supporting the hypothesis that timing is more important when the firm plans to pay down debt rather than use the proceeds for investments. Our findings are consistent with such a view.

6 The paper is organized as follows. Section 2 describes the data, our use of proceeds classification, and sample statistics. In Section 3 we explain the methodologies that we employ to examine long-run stock and operating performance. Section 4 provides the results and Section 5 presents concluding remarks.

Sample selection, use of proceeds classification, and issuer characteristics

The initial sample of seasoned offerings is obtained from Securities Data Company’s (SDC) Global New Issues Database and consists of traditional seasoned equity offerings during 1997–2003 that have at least some primary component. Excluded are initial public offerings, rights offers, shelf-registered offers, unit offers, ADRs, offerings by financials (SIC code 6000-6999) or utilities ((SIC code 4900-4999), and offerings by non-U.S. firms. To be included in the sample, firms must be listed on the NYSE, AMEX, or Nasdaq, and must have stock return data available from the Center for Research in Securities Prices (CRSP) during the three years subsequent to the issue. Also, the firm must have financial data available from COMPUSTAT. We manually collect issuers’ stated intended use of proceeds from their S-3 filings in EDGAR. Our sample begins in 1997 because the SEC phased companies into the EDGAR filing over a three-year period ending in May 1996, after which all companies were required to file electronically in EDGAR. We study three specific classifications for use of proceeds: investment, recapitalization, and general corporate purposes. Issuers in the first classification are those that prominently state that the proceeds will be used for investment purposes and that do not indicate that any of the proceeds will be used for debt repayment. Issuers in the second classification prominently list repayment of debt obligations as the intended use and make no mention of specific investment plans for the proceeds. Issuers in the third classification cite neither investment nor debt repayment, instead stating only general corporate purposes as the intended use of proceeds. A relatively small percentage of issuers specifically

7 mention both investment and debt repayment in their filing. To avoid ambiguity, we exclude these issuers from the sample.3 This information is located in two places on the filing. If the first statement of the intended use of proceeds is not clear, then we manually examine the body of the filing for more specific information to more accurately classify the sample firm into one of the four classifications. In some cases a firm’s initial filing is not complete and the use of proceeds is not provided. In these instances the company usually has a more complete supplemental filing and we collect the use of proceeds information from this filing. Table 1 provides descriptive statistics for our sample of 880 seasoned issuers partitioned by the use of proceeds. The size of the issuer and the offering proceeds are similar across investment and recapitalization classifications. The typical issuer that states ‘investment’ has a mean market value of $753 million compared to $632 million for the mean ‘recapitalization’ issuer; the medians are even closer. The corresponding mean proceeds are $102.0 million and $95.7 million. The size of the offering relative to the size of the issuer is approximately one-quarter. Firms that state ‘general corporate purposes’ are larger and raise more proceeds, but have smaller relative offer sizes. Notably, the ratio of debt to total assets is considerably higher for firms that have a stated intention of debt recapitalization, as we expect. The issuer’s debt as a proportion of total assets is, on average, 0.35 for recapitalization, 0.18 for investment, and 0.17 for general corporate purposes. Adjusted for the industry median debt ratio, these differences are large and indicate that issuers with high industry-adjusted debt ratios often say they intend to use the offer proceeds to pay down debt. Finally, we provide statistics on the percentage of shares in the offering that are secondary. Secondary shares are sold by a shareholder and, thus, the seller receives the proceeds rather than the issuing firm. Our sample restriction that at least some portion of the shares must be primary is necessary because we focus on the use of proceeds. In purely secondary offers (which we exclude), the issuer receives no proceeds from the offering. It is noteworthy that offerings in which the issuer states recapitalization or general corporate purposes as an intended use have a relatively high secondary

3 SDC also provides a use of proceeds variable. However, in the majority of cases this variable is listed as “general corporate purposes”. Our manual collection from S-3 filings provides more precise classifications.

8 component, with means of 22.4% and 27.8%, respectively. In contrast, offerings in which the issuer states investment as the intended use have a much smaller average secondary component, 13.1%. A similar relation exists with median percentages. The differences in the secondary component of the offering across use of proceeds classifications is consistent with a timing motive within recapitalization and general corporate purposes classifications, and a non-timing motive within the investment classification. Unreported statistical tests show that these differences in means and medians are each significant at the 1% level. This is consistent with Kim and Weisbach (2008), who find that SEO issuing firms are more likely to issue secondary shares when there is a timing motive, enabling insiders of the company to personally profit from issuing overvalued shares.

Methodology

Long-run stock returns

We conduct analyses of long-run abnormal stock performance using matching techniques and calendar time regressions.

Matched firm technique and buy-and-hold abnormal returns

We employ two matching techniques that follow previous researchers (e.g., Jegadeesh, 2000). In the first we select matched firms based on firm size and pre-issue stock performance, i.e., runup. In the second we choose matched firms based on firm size and the market-to-book ratio. Barber and Lyon (1997) argue that an examination of long-run abnormal stock returns using control firms based on size and book-to-market ratios yields test statistics that are well-specified. To obtain matching firms, each month we sort issuers and control firms into deciles based on the market value of equity. For the size / runup matching scheme we match issuers to control firms that are in the same size decile, based on market value of equity, and in the same decile of six-month compound returns prior to the calendar month of the issue. Of the control firms that meet these

9 criteria, we choose the match that has the closest runup to that of the issuer. We require that the runup of the matched firm is within 30% of the runup of the issuer. For the size / market-to-book matching scheme, we match issuers to control firms that are in the same size decile and have a market-to-book value of equity within 30% of the issuer in the same calendar month. Of the resulting matches, we choose the one with a market-to-book value of equity closest to that of the issuer. We calculate monthly buy- and-hold abnormal returns as the buy-and-hold return of a sample issuer minus the buy- and-hold return of the appropriate matched-firm:

τ τ BHARi,τ = ∏ 1[ + R ,ti ] − ∏ 1[ + MR ,ti ], (1) t =1 t =1  where BHARi,τ is the buy-and-hold abnormal return for sample firm i for length months, R ,ti is the return for sample SEO firm i in month t, where month t=1 is the

month immediately following the offer month, and MR ,ti is the return in month t of the matched, comparison firm associated with each sample SEO firm i. Mitchell and Stafford (2000) argue that cross-sectional correlations in issuer returns lead to unreliable statistical inferences using the BHAR approach. Using the correlation structure of Mitchell and Stafford, we calculate adjusted test statistics for BHARs. We examine BHARs for three years following the SEO and for a 30-month period beginning in the seventh month after the SEO. Our rationale for the latter is that studies that examine post-SEO stock returns often find no underperformance in the first six months after the offering (e.g., Loughran and Ritter, 1995). This could be due to underwriter price stabilization practices whereby the underwriter buys shares in the open market to create demand, potentially obscuring differences in abnormal returns partitioned by the stated intended use of proceeds.

Factor model regressions

We also provide tests of long-run stock performance using factor model regressions. This approach alleviates concerns that the matched-firm technique for BHARs does not control for differences in risk between the sample SEO firms and the

10 control firms (e.g., Eckbo et al., 2000; Brav et al., 2000). Fama (1998) suggests the use of the rolling calendar-month portfolio methodology to reduce some of the concerns about drawing inferences from BHARs. Starting with January 1997, each calendar month we construct portfolios of firms that conduct equity offerings in the past 36 months. The portfolios are rebalanced each month as firms exit and enter. We regress the monthly return for each of these portfolios, adjusted by the risk free rate, on the monthly three-factors of Fama and French (1993). Specifically, we estimate:

Rpt − Rft = α p + β p[MKTt ] + s p[SMBt ] + hp[HMLt ] + ε pt , (2)

where Rpt is the monthly return on the equally-weighted calendar-time portfolio of

4 equity issuers, R ft is the monthly return on the three-month Treasury bill, MKT is the return on the value-weighted CRSP market index minus the monthly return on the three- month Treasury bill, SMB is the difference in the returns of portfolio of small and big stocks, and HML is the difference in the returns of a portfolio of high book-to-market stocks and low book-to-market stocks. While the calendar-time approach avoids the problem of correlated BHARs, Mitchell and Stafford (2000) argue that even the three-factor model does not completely explain the cross-section of stock returns. They recommend an adjusted intercept approach where an expected intercept is subtracted from the actual estimated intercept. We calculate adjusted intercepts using this approach. We estimate expected intercepts following Mitchell and Stafford’s procedures by using 1000 replications, with randomly selected firms of similar size and book-to-market equity as the issuing firm. Additionally, we construct a four-factor model that also includes the momentum factor of Carhart (1997). In particular, we estimate:

4 We use equal-weighting because Loughran and Ritter (2000) argue that factor models with value- weighted portfolio returns as the dependent variable have low power to detect abnormal returns following managerial actions.

11 R pt − R ft = α p + β p [MKTt ] + s p [SMBt ] + hp [HMLt ] + m p [MOM t ] + ε pt , (3) where MOM is the Carhart momentum factor. Each of these variables is obtained from Kenneth French’s website. The intercept term, , provides a measure of the mean monthly abnormal return on the calendar-time portfolio. In the BHAR and calendar time approaches, we include delisting returns where necessary. Of our 880 sample SEOs, 697 remain in the sample for three years, while 183 delist.5 Delisting returns are available from CRSP for 172 of these firms. For the other 11, we follow Shumway (1997) and Shumway and Warther (1999) by using a delisting return of -30% for NYSE and AMEX firms and -55% for Nasdaq firms.

Operating performance

We use two measures of the operating performance of equity issuers: operating income scaled by sales and operating income scaled by total assets. We analyze median values because Barber and Lyon (1996) find that nonparametric tests are uniformly more powerful than parametric tests in studies of operating performance. We provide unadjusted measures, and also adjust the operating performance using two techniques to control for factors that have been shown to influence operating performance. First, we compute industry-adjusted measures by subtracting the median industry level of operating performance from the issuer’s level in each particular year. The median industry level is based on the two-digit SIC code. If there are fewer than three firms in the same industry, we assign a missing value to the industry measure. Second, we follow Barber and Lyon (1996) and Heron and Lie (2004) by using a matching scheme that uses industry and pre-issue operating performance as matching variables. In particular, we subtract the operating performance of a control firm that is in the issuer’s industry and has similar pre-issue performance. The rationale is that there exists mean reversion in operating performance at the industry-level. We select matching firms for this approach as follows. We match issuers to control firms that are in the same

5 Of the 183, 128 delist due to mergers. The remainder delist for other reasons, such as bankruptcy or failure to meet listing requirements.

12 two-digit SIC code and have an operating performance that is within 10% percent of the issuer’s operating performance. From these control firms, we choose the match with the operating performance measure closest to that of the issuer. If there is no match using a two-digit SIC code, we use a one-digit code, and if that does not generate any matches, we use the pre-issue performance and no SIC criterion. We focus on changes in post- issue operating performance over three windows around the offering: changes from the year prior to the issue to two years following the issue, the issue year to two years after the issue, and the issue year to three years after the issue. Finally, we estimate quantile regressions to assess the differential impact of the use of proceeds classifications on operating performance. Quantile regressions focus on medians and are therefore preferable to OLS estimations when studying operating performance. Ordinary least squares (OLS) regressions model the relation between one or more explanatory variables and the conditional mean of the dependent variable. Quantile regressions, introduced by Koenker and Bassett (1978), extend the regression model to conditional quantiles, or percentiles, of the dependent variable, such as the median or the 75th percentile. This approach is advantageous when the data are heterogeneous in the sense that the tails and the central location of the conditional distributions vary differently with the explanatory variables. Moreover, quantile regressions also offer a degree of data robustness. They make no distributional assumptions about the error term and are robust to outlier observations of the dependent variable. Quantile regressions are beneficial when studying operating performance because these data contain outliers. Motivated by prior operating performance studies that examine medians, we employ median quantile regressions (i.e. 50th percentile) and test for significance of the coefficients using bootstrapped standard errors based on 1000 replications. Our dependent variable in the regressions is the change in industry-adjusted operating performance from the year prior to the issue to two years following the issue, and our explanatory variables of interest are binary indicators for the recapitalization and general corporate purposes classifications. These variables take the value of one for the specified use and zero otherwise. The coefficient represents the difference between the specified classification and the investment classification. We also include as control

13 variables the log of the market value of equity, the log of the proceeds from the offer, and the relative offer size.

Empirical Results

Abnormal stock returns

Table 2 reports mean BHARs for a 36-month horizon following the SEO and for a 30-month horizon beginning in the seventh month after the SEO. Panel A provides results for firms matched on size and runup and Panel B shows results for firms matched on size and market-to-book ratios. For the means, we report t-statistics and adjusted t- statistics following Mitchell and Stafford (2000). We also provide generalized sign test z-statistics to test whether the proportion of negative BHARs is significantly different than the proportion of positive BHARs. Panels C and D show p-values for tests of differences in mean BHARs between categories for the size and runup (Panel C) and size and market-to-book (Panel D) matching schemes. For all issuers, we find significantly negative mean BHARs, ranging from -11% to -14%, for both horizons and using either matching method. Ordinary t-statistics are significant at the 1% level, and adjusted t-statistics are often significant at the 10% level. Moreover, the z-statistics indicate a significantly greater proportion of negative BHARs than positive BHARs. When investment is stated as the intended use of funds, mean BHARs are insignificant based on t-statistics and adjusted t-statistics. Sign test z-statistics are insignificant for months 1-36, but are significantly negative for months 7-36. In the recapitalization sample, the table reports significantly negative BHARs of approximately -21% to -23% using either matching procedure. Ordinary t-statistics are significant at the 1% level, adjusted t-statistics are significant at the 5% level, and sign test z-statistics are significant at the 10% level or better. Finally, in the general corporate purposes category, the evidence is mixed. Although the magnitudes of BHARs are always negative, they are only weakly significant with traditional t-statistics, insignificant with adjusted t-statistics, and only sometimes significant with sign test z-statistics.

14 In Panels C and D, for the 36-month analysis, differences between categories (1) versus (2) are significant at the 10% level, indicating that BHARs are significantly higher if investment is stated as an intended use of proceeds than if recapitalization is stated. Overall, Table 2 reveals that (i) recapitalization as an intended use of proceeds is associated with significantly negative abnormal BHARs, while investment is often not associated with abnormal performance and (ii) BHARs over the three-year horizon are significantly greater when investment is stated as opposed to recapitalization. Estimated coefficients from calendar-time regressions of three and four-factor models are presented in Table 3. Coefficients from a three-year horizon are in Panel A and those from a 30-month horizon beginning in the seventh month after the SEO are in Panel C. Our main focus is on the estimated intercepts (alphas). Adjusted intercepts for the three-factor model, based on Mitchell and Stafford (2000), for months 1-36 (7-36) are in Panel B (D). With a three-year horizon in Panel A, the alpha for the recapitalization sample in the three-factor model, -0.74%, is significantly negative at the 5% level. In Panel B, the adjusted alpha for recapitalization remains significant at the 5% level. No other alphas in Panels A or B enter significantly. For months 7-36 in Panel C, the alpha for the recapitalization sample enters significantly negative in both the three-factor model (- 0.89%; 5% significance level) and four-factor model (-0.58%; 10% significance level). In Panel D the corresponding adjusted alpha is significant at the 1% level. No other alphas in Panels C or D are significant. The significant alphas for the recapitalization category average about three- quarters of a percent per month, or about nine percent per year. For equivalent time periods these results are similar to the recapitalization BHARs reported in Table 2. The lack of significant alphas for the investment category is consistent with a similar lack of significance for these stocks for BHARs in Table 2. The general corporate purposes category has insignificant alphas, indicating a lack of negative abnormal returns. These findings are fairly similar to those in Table 2, in which negative BHARs are marginally significant with traditional t-statistics and insignificant with adjusted t-statistics. Overall, the results in Tables 2 and 3 are generally consistent with our hypotheses. The stocks of issuers that state debt recapitalization as the intended use of proceeds

15 experience abnormally low BHARs and are often associated with significantly negative alphas from calendar time regressions. In contrast, stocks of issuers that cite specific investment plans are associated with insignificant BHARs and alphas. When issuers state general corporate purposes, there is only weak evidence, at best, of abnormally low long- run stock returns. These findings support the notion that an investment statement of intended use is a credible signal of profitable investment opportunities, while statements of recapitalization and, perhaps, general corporate purposes are more consistent with opportunistic market timing. Carlson et al. (2006) and Lyandres et al. (2008) offer an investment-based explanation for the poor stock performance after equity offers. The investment-based explanation is premised on the notion that equity issuers convert risky growth options into less risky assets-in-place, which causes lower subsequent returns. Thus, assuming no misvaluation at the time of the issue, firm that invests more should have lower subsequent returns. This effect biases against finding support for our hypothesis. Under our hypothesis, firms that do not cite credible investment opportunities in their S-3 filing are more likely to be overvalued at the issue and therefore experience poorer subsequent performance. The supportive evidence we present therefore cannot be attributed to the investment-based explanation.

Operating performance

The literature reports that issuers conducting traditional SEOs experience a subsequent deterioration in operating performance (e.g., Hansen and Crutchley, 1990; McLaughlin et al., 1996; Loughran and Ritter, 1997; Heron and Lie, 2004). A notable exception is Walker and Yost (2008), who find that industry-adjusted operating performance either improves or remains unchanged. We examine changes in operating performance using the three categories of firms based on their stated intended use of proceeds. Tables 4 through 6 report the findings. In Table 4 we examine operating performance, measured as operating income scaled by the book value of sales, during each of the five years beginning the year prior to the issue and ending three years after

16 the issue. In Table 5 we provide the same analysis using operating income scaled by total assets. For each use of proceeds classification, we examine changes in operating performance using raw (unadjusted) measures, industry-adjusted measures, and industry- and pre-issue adjusted performance measures. From Tables 4 and 5, for all issuers we observe significant declines in operating performance in the two to three years following the issue, which is consistent with the findings of prior researchers, except for the industry-adjusted results of Walker and Yost (2008). For the three categories of use of proceeds, we find significant declines in performance for the recapitalization and general corporate purpose classifications, but virtually no evidence of declines for the investment group.6 In unreported tests, we find that issuing firms that state recapitalization or general corporate purposes are associated with significantly greater deterioration in post-issue performance than issuing firms that cite a specific investment use for the proceeds. These findings are generally consistent with our analysis of long-run abnormal stock returns. In Table 6, the first two columns report the results of quantile regressions in which the dependent variable is the change in industry-adjusted operating income, scaled by either sales or total assets, from the year prior to the issue to two years following the issue. While quantile regressions are preferred, we also present estimates from OLS regressions, with winsorizing at the 5th and 95th percentiles, in the last two columns of Table 6. Our explanatory variables include indicators for the recapitalization and general corporate purposes classifications. The coefficients on these variables represent differences from the investment category. In all models the coefficients of the recapitalization and general corporate purposes indicator variables enter significantly negative. This indicates that there are greater declines in operating performance when the intended use is recapitalization or general corporate purposes as opposed to investment. The control variables have no significant impact on changes in operating performance. In sum, the results indicate that the motivations for conducting SEOs differ across categories of intended use of proceeds. Based on long-run stock and operating performance, the strongest finding is better performance when investment is the intended

6 Walker and Yost (2008) do not find significant declines for their recapitalization group.

17 use of proceeds than when recapitalization is the intended use. A stated intention to use the proceeds for investment purposes conveys reliable information of a non-timing motive for the offering while, in contrast, a statement that does not include a specific intention for investment conveys to some degree a timing motive to market participants. These findings are important because the stated intended use of proceeds is an ex-ante measure that is available to market participants at or before the offering. In contrast, the actual use of proceeds is not known a priori.

Conclusion

We find that SEO issuers’ stated intention in their S-3 filing regarding the use of proceeds provides information about the motivation for the offering. For issuers that state debt repayment or general corporate purposes as the intended use, and make no mention of investment, we provide evidence of long-run stock underperformance and post-issue declines in operating performance. The underperformance is stronger when debt repayment is the intended purpose. In contrast, for issuers that state a specific investment purpose, we observe little evidence of long-run underperformance. Even though there is an apparent timing motive of issuers that cite debt recapitalization as the intended use of proceeds, these issuers actually do have higher pre- issue industry-adjusted leverage ratios compared to issuers that do not cite plans for recapitalization. Nevertheless, a value-maximizing firm that wishes to pay down its debt with proceeds from an equity issue should issue the equity when the market temporarily overvalues its stock. Our evidence supports such a financing strategy. Although the evidence is somewhat mixed, issuers that are vague in their S-3 filing by only stating general corporate purposes appear to have a similar timing strategy. These issuers could potentially stockpile cash for future debt repayments or investments. Given no immediate need for capital, offers in this classification may occur when the issuer’s shares are overvalued. Finally, firms that refer to specific investment expenditures in their S-3 filing are arguably more likely to be conducting the offer in response to arising investment opportunities, regardless of the degree of under- or over- of their shares.

18 Accordingly, these issuers are not expected to underperform after the offering. We find little significant evidence of underperformance. The advantage of our approach over studies that examine the actual use of proceeds is that our measure is available at the time of the issue, whereas the actual use of proceeds can only be observed years after the offering. This has important implications for the predictability of post-issue performance. Our results suggest that an easily collected, ex-ante piece of information provides insights about the future stock and operating performance of the issuing firm.

19

Table 1 Descriptive statistics of SEOs by intended use of funds categories

Investment Recapitalization General Corporate Purposes Mean Median Mean Median Mean Median Market Value ($ mil.) 753 336 632 359 1,306 547

Offer Proceeds ($ mil.) 102.0 62.1 95.7 74.0 122.2 88.8

Relative Offer Size 0.23 0.20 0.26 0.22 0.18 0.16

Debt Ratio 0.18 0.09 0.35 0.34 0.17 0.05

Industry-adjusted Debt 0.00 -0.06 0.14 0.12 0.04 -0.05

Percentage Secondary 13.1 0.0 22.4 10.0 27.8 17.5

This table presents descriptive statistics for 880 sample SEO issuers during 1997-2003. Three categories of issuers are presented based on the stated intended uses of funds. These categories are investment (N=283), recapitalization (N=257), and general corporate purposes (N=340). Market value is the stock price times the number of shares outstanding on the day prior to the offer. Offer proceeds equals the offer price times the number of shares offered. Relative offer size equals the number of shares offered divided by the number of shares outstanding on the day prior to the offer. Debt ratio is the ratio of long-term debt plus short-term debt to total book assets, and is the year-end figure in the year prior to the issue. Industry-adjusted debt is the debt ratio of the issuing firm minus the debt ratio of the median firm in the issuer’s industry. Percentage secondary is the percentage of total shares in the offering that are issued by selling shareholders, where the seller rather than the firm receives the proceeds. Sample firms are required to have at least some primary component.

20 Table 2 Stock performance in the three years following issuance

Panel A: BHARs (%) matched on size and runup

Months 1-36 Months 7-36

Mean t-stat Adjusted Sign test Mean t-stat Adjusted Sign test BHAR t-stat Z-stat BHAR t-stat Z-stat All Issuers -12.61 -3.39*** -1.76* -2.73*** -14.11 -3.60*** -1.87* -4.45*** Investment (1) -6.25 -0.89 -0.66 1.56 -9.27 -1.26 -0.91 -2.31** Recapitalization (2) -22.80 -3.44*** -2.54** -1.81* -21.04 -3.23*** -2.39** -2.85*** General (3) -9.80 -1.72* -1.22 -1.37 -12.67 -1.98** -1.41 -2.57***

Panel B: BHARs (%) matched on size and market-to-book

Months 1-36 Months 7-36

Mean t-stat Adjusted Sign test Mean t-stat Adjusted Sign test BHAR t-stat Z-stat BHAR t-stat Z-stat All Issuers -11.15 -2.90*** -1.46 -2.72*** -13.55 -3.42*** -1.72* -3.89*** Investment (1) -1.93 -0.28 -0.20 0.85 -8.23 -1.13 -0.81 -2.80*** Recapitalization (2) -20.69 -3.08*** -2.26** -1.79* -21.47 -3.41*** -2.50** -2.42** General (3) -11.59 -1.84* -1.25 -2.04** -11.98 -1.78* -1.21 -1.60

21 Table 2--Continued Panel C: P-values of differences in mean BHARs matched on size and runup

Months 1-36 Months 7-36 (1) versus (2) 0.0890 0.2321 (1) versus (3) 0.6953 0.7268 (1) versus (2 and 3) 0.2509 0.4046

Panel D: P-values of differences in mean BHARs matched on size and market-to-book

Months 1-36 Months 7-36 (1) versus (2) 0.0536 0.1695 (1) versus (3) 0.3026 0.7066 (1) versus (2 and 3) 0.1001 0.3581

This table presents buy-and-hold abnormal stock returns (BHARs) of issuing firms in relation to matched non-issuers during the three years after the offering. BHARS are reported for two periods: months 1-36 and months 7-36. Panel A presents buy-and-hold returns of the issuer in excess of the buy-and-hold return for non- issuers matched on market capitalization (size) and pre-issue stock performance (runup). Panel B presents similar statistics using matches based on size and market- to-book. The first row of each panel displays BHARs for all firms, and the subsequent three rows display BHARs for three categories of firms based upon their stated intended use of proceeds: (1) firms where investment is the stated use of proceeds; (2) firms where recapitalization is the stated use of proceeds; and (3) firms where general corporate purposes is the stated use of proceeds. In Panel A adjusted t-statistics are based on the methods of Mitchell and Stafford (2000), and the generalized sign test z-statistic examines whether the fraction of negative BHARs is significantly greater than the fraction of positive BHARs. ***, **, * indicate that the test statistic is statistically significant at the 1, 5, and 10% levels, respectively. Panels C and D show p-values for tests of differences in mean BHARs between categories for the size and runup (Panel C) and size and market-to-book (Panel D) matching schemes.

22 Table 3 Calendar time regressions of long-run stock returns

(1) (2) (3) All Issuers Investment Recapitalization General Corporate Purposes a b 1a 1b 2a 2b 3a 3b

Panel A: Coefficient estimates using months 1-36

-0.39 -0.02 -0.49 -0.02 -0.74** -0.50 0.01 0.34 Alpha (0.29) (0.21) (0.42) (0.35) (0.35) (0.33) (0.32) (0.28)

1.43*** 1.26*** 1.37*** 1.17*** 1.34*** 1.23*** 1.54*** 1.40*** MKT (0.07) (0.05) (0.10) (0.09) (0.09) (0.08) (0.08) (0.07)

0.93*** 1.03*** 1.07*** 1.19*** 0.81*** 0.88*** 0.92*** 1.01*** SMB (0.10) (0.05) (0.11) (0.09) (0.09) (0.08) (0.08) (0.07)

-0.21** -0.29*** -0.25* -0.35*** 0.50*** 0.45*** -0.64*** -0.71*** HML (0.10) (0.07) (0.14) (0.11) (0.12) (0.11) (0.11) (0.09)

-0.38*** -0.47*** -0.25*** -0.33*** MOM - - - - (0.04) (0.06) (0.06) (0.05)

Adjusted R2 0.90 0.95 0.82 0.88 0.77 0.80 0.91 0.94

Panel B: Adjusted alphas for the three-factor model using the Mitchell and Stafford (2000) approach for months 1-36

Adjusted -0.41 - -0.54 - -0.82** - 0.01 - Alpha (0.29) (0.42) (0.35) (0.32)

23 Table 3--Continued (1) (2) (3) All Issuers Investment Recapitalization General Corporate Purposes a b 1a 1b 2a 2b 3a 3b

Panel C: Coefficient estimates using months 7-36

-0.42 0.04 -0.49 0.04 -0.89** -0.58* 0.05 0.45 Alpha (0.36) (0.24) (0.50) (0.40) (0.38) (0.34) (0.38) (0.30)

1.45*** 1.22*** 1.37*** 1.10*** 1.34*** 1.18*** 1.58*** 1.38*** MKT (0.09) (0.06) (0.13) (0.10) (0.09) (0.09) (0.09) (0.08)

0.91*** 1.06*** 1.01*** 1.19*** 0.83*** 0.94*** 0.90*** 1.03*** SMB (0.09) (0.06) (0.13) (0.10) (0.10) (0.09) (0.10) (0.08)

-0.12 -0.23*** -0.18 -0.30** 0.60*** 0.53*** -0.61*** -0.70*** HML (0.12) (0.08) (0.16) (0.13) (0.12) (0.11) (0.12) (0.10)

-0.50*** -0.58*** -0.34*** -0.43*** MOM - - - - (0.04) (0.07) (0.06) (0.05)

Adjusted R2 0.86 0.94 0.75 0.85 0.74 0.80 0.89 0.93

Panel D: Adjusted alphas for the three-factor model using the Mitchell and Stafford (2000) approach for months 7-36

Adjusted -0.56 - -0.67 - -1.07*** - -0.08 - Alpha (0.36) (0.50) (0.38) (0.38) This table presents monthly estimates from regressing calendar-time portfolio monthly returns on the Fama-French (1993) three factors (MKT, SMB, HML) and the four-factor model that also includes Carhart’s (1997) momentum factor (MOM). The four factors are obtained from Ken French’s website. Panel A reports the coefficient estimates using a three-year horizon; Panel B uses the same horizon, but reports adjusted alphas for the three-factor model using the adjusted intercept approach of Mitchell and Stafford (2000). The adjusted alpha is the difference between the estimated alpha using sample firms and the expected alpha, which is estimated as the mean alpha from 1000 calendar-time portfolio regressions with randomly selected non-issuing firms that are in the same size / book-to- market group as the sample firms. Panels C and D report the respective estimates using only months 7-36. In each panel, the first set of columns (a and b) display estimates for all issuers, and the subsequent sets of columns (1a and 1b, 2a and 2b, and 3a and 3b) display coefficients for the three categories of firms based on their stated intended use of proceeds: (1) firms where investment is the stated use of proceeds; (2) firms where recapitalization is the stated use of proceeds; and (3) firms where general corporate purposes is the stated use of proceeds. Standard errors are in parentheses. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.

24 Table 4 Median operating income scaled by sales

Fiscal year relative to issue Changes -1 0 +1 +2 +3 -1 to +2 0 to +2 0 to +3 All issuers Unadjusted .100 .121 .110 .092 .098 -.008 -.014*** -.014*** Industry Adjusted .015 .035 .026 .008 .012 -.009 -.018*** -.023*** Industry/Performance .000 .015 .012 .007 .002 .007 -.011*** -.014** Adjusted Observations 831 789 708 631 538

Investment (1) Unadjusted .049 .091 .081 .066 .074 .007* -.000 -.005 Industry Adjusted -.044 -.003 -.003 -.010 .000 .011* -.002 -.003 Industry/Performance .000 .019 .023 .012 .008 .009 -.016 -.004 Adjusted Observations 253 242 212 180 155

Recapitalization (2) Unadjusted .122 .133 .121 .114 .120 -.011*** -.019*** -.012*** Industry Adjusted .035 .046 .036 .015 .025 -.016*** -.020*** -.019*** Industry/Performance .000 .011 .012 .001 .009 -.000 -.011 -.004 Adjusted Observations 254 250 233 214 182

General Corporate Purposes (3) Unadjusted .096 .123 .108 .084 .086 -.012 -.022*** -.036*** Industry Adjusted .018 .044 .033 .008 .003 -.011 -.019*** -.044*** Industry/Performance .000 .019 .006 .011 -.017 .009 -.010** -.028*** Adjusted Observations 324 297 263 237 201

This table presents median levels and median changes in operating income scaled by sales in the years surrounding the equity issue. Industry-adjusted operating income is computed by subtracting the median industry level (based on the two-digit SIC code) of operating income from the issuer’s level in each particular year. Industry and performance adjusted operating income is computed by subtracting from the issuer’s operating income the operating income of a control firm that is in the issuer’s industry and has similar pre-issue performance. For changes, we use a Wilcoxon signed rank test to examine whether the change is significantly different from zero; ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively. The significance levels reported are qualitatively similar to significance levels using generalized sign tests (unreported) that examine whether the fraction of negative changes is significantly greater than the fraction of positive changes.

25 Table 5 Median operating income scaled by total assets

Fiscal year relative to issue Changes -1 0 +1 +2 +3 -1 to +2 0 to +2 0 to +3 All issuers Unadjusted .105 .105 .097 .083 .087 -.020*** -.021*** -.020*** Industry Adjusted .003 .007 .004 -.011 .000 -.016*** -.019*** -.021*** Industry/Performance -.000 .004 -.001 -.014 -.017 -.012*** -.018*** -.015*** Adjusted Observations 843 796 711 633 541

Investment (1) Unadjusted .026 .058 .065 .048 .072 -.004 -.011*** -.007* Industry Adjusted -.068 -.034 -.027 -.034 -.015 .011 -.013** .001 Industry/Performance -.000 -.006 .003 -.002 -.007 -.006 -.013 .004 Adjusted Observations 262 248 216 183 158

Recapitalization (2) Unadjusted .129 .137 .121 .111 .118 -.019*** -.023*** -.017*** Industry Adjusted .032 .037 .024 .012 .020 -.023*** -.021*** -.015*** Industry/Performance -.000 .015 -.001 -.015 -.014 -.014* -.024*** -.007* Adjusted Observations 255 250 232 213 181

General Corporate Purposes (3) Unadjusted .096 .097 .080 .057 .058 -.032*** -.024*** -.043*** Industry Adjusted .005 .009 -.002 -.012 -.014 -.022*** -.018*** -.042*** Industry/Performance -.000 -.003 -.003 -.021 -.028 -.023*** -.023*** -.043*** Adjusted Observations 326 298 263 237 202

This table presents median levels and median changes in operating income scaled by total book assets in the years surrounding the equity issue. Industry-adjusted operating income is computed by subtracting the median industry level (based on the two-digit SIC code) of operating income from the issuer’s level in each particular year. Industry and performance adjusted operating income is computed by subtracting from the issuer’s operating income the operating income of a control firm that is in the issuer’s industry and has similar pre-issue performance. For changes, we use a Wilcoxon signed rank test to examine whether the change is significantly different from zero; ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively. The significance levels reported are qualitatively similar to significance levels using generalized sign tests (unreported) that examine whether the fraction of negative changes is significantly greater than the fraction of positive changes.

26 Table 6 Regressions of changes in operating income

Industry-adjusted Industry-adjusted Industry-adjusted Industry-adjusted operating income operating income operating income operating income / Sales / Total assets / Sales / Total assets (1) (2) (3) (4) Median Quantile Median Quantile OLS OLS 0.0029 -0.0476 Intercept -0.0534 -0.0536 (0.0511) (0.0581) (0.0918) (0.0410) -0.0785*** -0.0285*** Recapitalization -0.0229*** -0.0317*** (0.0083) (0.0106) (0.0237) (0.0107) General Corporate -0.0186** -0.0282** -0.613** -0.0288** Purposes (0.0094) (0.0121) (0.0283) (0.0122) - - 0.0199 0.0108 Log (Market value) 0.0183 0.0115 (0.0167) (0.0189) (0.0245) (0.0112) -0.0148 -0.0039 Log (Proceeds) -0.0160 -0.0056 (0.0169) (0.0181) (0.0272) (0.0120) 0.0391 0.0307 Relative offer size 0.0918 0.0602 (0.0777) (0.0702) (0.1015) (0.0500) Adjusted R2 - - 0.0133 0.0088

Likelihood Ratio (p- 11.3 (0.05) 13.0 (0.02) - - value) Number of Obs. 609 617 609 617

This table presents median quantile and OLS regressions in which the dependent variable is the change in industry-adjusted operating income scaled by sales or total assets from year -1 to year +2. Industry-adjusted operating income is computed by subtracting the median industry level (based on the two-digit SIC code) of operating income from the issuer’s level in each particular year. The main explanatory variables are: Recapitalization, which takes the value of 1 for firms where recapitalization is the stated use of proceeds and 0 otherwise; and General Corporate Purposes, which equals 1 where general corporate purposes is the stated use of proceeds and 0 otherwise. Also included are the natural log of the market value, calculated as the stock price times the number of shares outstanding on the day prior to the offer, the natural log of the offer proceeds equaling the offer price times the number of shares offered, and the relative offer size equaling the number of shares offered divided by the number of shares outstanding on the day prior to the offer. In the median quantile regressions (models 1 and 2) bootstrapped standard errors based on 1000 replications are reported in parentheses. In the OLS estimations (models 3 and 4) heteroskedasticity- adjusted standard errors based on White's (1980) procedure are in parentheses. In the OLS estimations, operating income is winsorized at the 5th and 95th percentiles. Coefficients are presented with standard errors in parentheses. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.

27 CHAPTER 3

DO INSTITUTIONAL INVESTORS PAY ATTENTION TO THE INTENDED USE OF PROCEEDS OF SEASONED EQUITY ISSUERS?

Introduction

Institutional investors, referred to as institutions, are often thought of as possessing superior information-collection skills by financial economists. Researchers identify these market participants as “smart money” (i.e. Gibson et. al (2004), among others). Institutions spend many hours and large sums of money, in the billions of dollars each year, collecting information about companies in which they may invest their clients’ wealth.7 All of these resources are devoted with the desired end result in mind—positive average returns above what can be earned investing with one’s own information set. If individuals can invest their money with institutions and experience yields which are above average, then one would be remiss if the opportunity to do so is passed up. Grinblatt and Titman (1989, 1993) provide support for this conjecture. They find higher gross returns for mutual funds which are actively-managed and conclude the fees charged to individuals by management may be warranted. Many researchers, however, have found that institutions such as pension funds are not able to produce abnormal returns to individual investors after accounting for the fees and transactions costs (e.g., Lakonishok et. al, 1992). Whether or not institutions should be called “smart” investors remains an open debate. As researchers continue to investigate the performance metric of institutions, they also seek to uncover the sources of information utilized by institutions. In other words, if we can conclude that institutions are able to earn above average returns and deem them “smart”, then what information are they using that individuals do not incorporate into their investment decisions? Cook and Tang (forthcoming, Financial Management) seek

7 See Gibson et. al (2004) for an example of College Retirement Equities Fund (CREF) spending over $100 million a year on research activities.

28 to explain the information set of institutions and whether it has changed over time. If institutions are utilizing publicly-available data sources, as stated by management, and are experiencing superior abnormal returns, researchers are provided an excellent opportunity to identify the underlying determinants or predictive variables. The role of institutions has gained importance in the literature recently due in part to the amount of the U.S. equity market that they have control over—more than 50% as of December 1996 (Gompers and Metrick, 2001). Institutions have large amounts of capital to invest in many different areas of financial markets. And yet they appear to invest heavily in seasoned equity offerings (SEOs) which, on average, tend to underperform their benchmarks. Financial economists have been extremely interested in whether or not SEOs by publicly traded firms are subsequently followed by long-run underperformance. The idea of the “new issues puzzle”, documents severe underperformance for SEO issuers for up to five years post-issuance (Loughran and Ritter, 1995, Spiess and Affleck-Graves, 1995, and others). The main implication of these studies is that managers can time equity offers to exploit market misvaluation, and investors are slow to react. If institutions are truly smart, they should be able to identify excellent investment opportunities and ignore the poor choices. As with any research area, a more refined approach is both inevitable and necessary to get a better understanding of whether SEOs do indeed underperform. Many recent studies share a common method of partitioning issuing firms into more specific classifications within the sample of firms (Cornett, et. al, 1998, Clark, et. al, 2004). These studies are in accord with the notion that managers often conduct SEOs when market participants overvalue their stock, lending support to Loughran and Ritter’s conclusion of investor under-reaction to the announcement of the SEO. If market timing is at play, would financial managers be able to dupe the smart institutional investors into purchasing the over-priced shares offered? Gibson et. al (2004) partition SEOs into classifications based on institutional ownership. Seasoned equity issuers that are heavily purchased by institutional investors around the offering date outperform other issuers. These SEO firms experience positive abnormal returns over the year subsequent to issuance, measured relative to a benchmark

29 portfolio, of 7.87%. This may suggest that institutions are in fact smarter, or make better investment decisions, than individual investors. The debate is still active in the literature about whether or not institutional investors have superior stock-picking ability. Gibson et. al (2004) suggest that institutions are selecting and purchasing those SEOs that perform well, relative to other SEOs, subsequent to issuance. If there are certain publicly available variables that allow institutions to select excellent performing SEOs, then future research would benefit from the identification and informational content of such variables. This essay proposes to test the relation between the level of institutional investment among SEOs and the intended use of proceeds as stated by management. More specifically, I will examine the degree to which institutions incorporate the intended use of proceeds variable into investment decisions and whether the decisions are financially successful. One concern, which has been addressed in the literature, is the possibility that institutions are privy to selective disclosure of material information by companies that are potential investment opportunities.8 If this is the case, then institutions aren’t any “smarter” than individual investors and do not possess superior information- collection skills. Rather, institutions would simply possess superior, private information that is not known to all market participants, especially individual investors. In late 1999, the SEC introduced legislation which ultimately led to the passing of Regulation Fair Disclosure (Reg FD) in October of 2000. It mandates that all publicly-traded firms must disclose material information to all investors simultaneously.9 Reg FD is designed to eliminate selective disclosure to large institutional investors and provides transparency and enhanced communications between firms and all investors. This should have leveled the playing field among all market participants. Institutional investors were not in support of Reg FD because they were used to benefiting from the private information that firms had provided in the past. Firms are now required to disclose all material information to both institutions and individual investors at the same time. If institutions were once privy to private information, they must now rely more on publicly-available

8 See Krigman et. al (1999) for evidence that suggests institutions may have a private-informational advantage over individual investors at the time of an initial (IPO). 9 Material information is any information that would lead investors to either purchase or sell securities on the basis of the information (see SEC Rule 100(b)(1)).

30 data sources. And we still have yet to uncover whether institutions are making “smarter” investment decisions in a post-Reg FD world. Many believed that Reg FD would lower the quality of the informational environment by not encouraging publicly traded firms to disseminate useful information.10 Heflin et al. (2003) provide support for the opposite effect. They find that firms increased both the quality and quantity of public information disclosures. Institutional investors may have to modify their investment strategies absent any material-informational advantage over individuals in a post-Reg FD environment. With the recent studies by Walker and Yost (2008) and Autore et. al (2009), the economic significance of the intended use of proceeds of SEOs has been highlighted in the literature. Walker and Yost (2008) find that regardless of what firms state as their intended use of proceeds in the SEC filings, they increase their investments through both capital expenditures and research and development after issuance. The market reaction of firms stating that a specific investment will be undertaken is more favorable than firms choosing to be ambiguous about the exact use of the funds. These results suggest that the intended use of proceeds may be utilized to predict future returns among SEOs. Autore et al. (2009) also find that the intended use of proceeds contains significant information. By classifying firms by their intended use of proceeds, the authors find that the market does value the specificity of future investment plans. These SEO firms do not underperform non-issuing benchmark firms, both in terms of future stock returns and future operating performance. The authors contend that market timing may be at play when financial managers decide to issue equity for firms stating the intended use of proceeds is to repay debt obligations or for general corporate purposes. The value of the predictive nature of the intended use of proceeds may already be incorporated by institutional investors, but so far this topic is unexplored. In this essay, I seek to link the aforementioned literatures in a comprehensive empirical study about SEOs: institutional investors, the intended use of proceeds, the managerial action of issuing equity via SEOs, and the effects of Reg FD. More specifically, I will investigate whether institutions incorporate the ex-ante intended use of proceeds when making investment decisions and whether the utilization of this variable

10 See the survey by the Association for Investment Management and Research (2001).

31 has changed through time. This is an open empirical issue and the results could provide evidence on the importance of the intended use of proceeds. Evidence will also be provided about the information set used by large institutions. Also, this essay may shed light on the discussion of market timing by financial managers and the extent to which institutional investors may be aware of its existence. In addition, this essay will contribute to the literature on the effectiveness of Reg FD. I propose that post-Reg FD, institutions will utilize the intended use of proceeds variable more because of less selective disclosure on the part of SEO issuers. The debate in the literature is still active as to whether institutions lost their informational-advantage over individual investors with the passage of Reg FD. A natural extension in this empirical arena is to first uncover whether institutions are large purchasers of shares of SEOs post-Reg FD, especially relative to before Reg FD, as documented by Gibson et al. (2004). If institutions significantly reduce their investment in SEOs post-Reg FD, then maybe they no longer receive private information about the SEO firm and have decided not to invest. This may have implications for the effectiveness of legislation similar to Reg FD. Institutional investors are considered to be among the most intelligent market participants and provide efficiency to financial markets. If institutions decide to participate less in the SEO market because they feel hindered, markets may become less efficient. A second contribution of this essay is to shed light on the debate concerning the “smartness” of institutional investors. If this study can link the intended use of proceeds variable to the investment activity of institutions, then it would not be superior- information processing skills, rather, it would simply be superior variable-selection; and all market participants could benefit from this knowledge. If institutions select their SEO investments based on the intended use of proceeds and are financially rewarded for it, then individuals may be able to replicate their strategy (in theory). The study would also provide further evidence on the SEO underperformance mechanism, while trying to understand why institutions would be drawn to invest in these equity issues. There are many investments that institutions can throw their money at; however, empirically we see them invest heavily in SEOs. A possible explanation could be what the financial

32 managers of the firm publicly disclose as the intended use of the proceeds raised via the SEO. The remainder of this essay is organized as follows. Section 2 provides a review of the relevant literature and develops the testable hypotheses. Section 3 describes the sample selection, descriptive statistics, and the methodologies utilized in this essay. Section 4 provides the empirical results of the study. Section 5 presents concluding remarks.

Literature Review

Institutional investors are considered to be the largest market participants in terms of the amount of equity shares they control in financial markets. Large institutions, according to the Securities and Exchange Commission (SEC), are those with at least $100 million under discretionary management. And these institutions, as of late 1996, owned more than half of the total U.S. equity market (Gompers and Metrick, 2001). Many researchers believe that institutions are not only the largest players in the stock market, but are also the “smartest” and make the best investment decisions (Gibson et. al, 2004, among others). Gompers and Metrick (2001) explore the extent to which these large institutions are able to drive equity prices and how significant these impacts on share prices may be. Banz (1981) was the first paper to document the small-stock phenomenon; specifically, small-stocks outperformed large-stocks by about 4% during the period from 1926 to 1979. This pattern reversed itself during the 1980s and 1990s causing researchers to question the underlying determinants. Gompers and Metrick (2001) analyze the institutional trading behavior and seek to shed light on whether institutions are partly accountable for the disappearance of the premium once earned by small capitalization firms. The authors are the first to combine two effects to investigate price formation, the time-series changes in the share of the market held by institutions and the cross-sectional variation or demand for different types of stocks. Gompers and Metrick (2001) use a firm-level approach of institutional holdings to uncover whether institutions have similar or different preferences for stock characteristics than do individual investors.

33 The authors use proxies for prudence, liquidity, and historical returns to try and capture the demand of institutions when investing in the cross-section of all equity securities. They find the coefficients on the liquidity variables to be positive and significant, suggesting that institutions prefer liquidity in the stocks they hold. Also, the authors find that institutions are not momentum investors. The overall results of this analysis indicate that institutional investors prefer stocks with large market capitalization, high liquidity, and low past returns. If institutions are in fact driving equity prices in the financial markets, then we would hope that these investment decisions are informed, correct decisions resulting in positive abnormal returns. Many researchers explore this notion of “smart” institutional investors. Gibson et. al (2004) is one such study. The authors investigate the corporate finance event of a seasoned equity offering (SEO) to determine whether institutional investors possess a superior stock-picking ability or not. Before discussing Gibson et. al (2004) and its implications, a review of the SEO literature and the contentious results is needed. I will briefly summarize some of the main papers in the SEO underperformance literature.11 Loughran and Ritter (1995) present evidence that firms issuing equity, either through an (IPO) or SEO, underperform their benchmarks significantly in the long-run for up to five years. The authors also show that firms issuing equity during years when there is high issuance activity “severely underperform”, suggesting time variation in underperformance. Their evidence supports the view that managers announce stock issues when the company’s stock is extremely overvalued, the market does not revalue it appropriately, and the stock is still overvalued when it is issued. If markets were truly “efficient”, according to their study there would need to be an announcement effect of -33% for SEO issuing firms, which is much larger than the well-known effects of -2 to -3% observed empirically. Spiess and Affleck-Graves (1995) find that firms that issue equity through an SEO exhibit underperformance in their long-term stock returns as well. The median buy- and-hold return for issuing firms in the five-year period following the SEO is 10%,

11 For a more through examination of the SEO literature, see the literature review section of the first essay of this dissertation.

34 compared to 42.3% for the matching non-issuer. The explanation given for the results is that investors are not receiving the full signal from the seasoned equity offering. One economic implication of these studies is that managers can time equity offers to exploit market misvaluations. Loughran and Ritter (1997) explore the relation between operating performance of seasoned equity offerings and subsequent stock returns. They explain that when issuing firms experience a decline in operating performance post-issue, investors recognize that the stock is still overvalued and adjust prices downward, thus we observe the post-issuance underperformance. From an economic standpoint, it is important to understand why researchers empirically observe long-run underperformance of SEOs. Rangan (1998) provides evidence that supports this argument. Earnings management in the year around the offering can predict earnings changes and market- adjusted stock returns in the year following the SEO. In a similar vein, Teoh, Welch, and Wong (1998) find that issuing firms that adjust discretionary current accruals just before the SEO significantly underperform, as measured by both long-run stock returns and net income. The underperformance of SEOs is challenged by other researchers. Eckbo et. al (2000) support the notion that issuing firms have lower overall risk after the SEO is conducted and the matched-firm technique does not adjust for the reduction in risk. They find that equity issuers lower their leverage and have less exposure to unexpected inflation and default risk. Brav et. al (2000) employ various methodologies for testing for abnormal performance in the long-run after SEOs. The ultimate conclusion they reach is that factor-models do a much better job in pricing SEO issuers and that underperformance is not detected using these methods. Much of the academic evidence supports the notion that SEOs tend to underperform subsequent to issuance, barring some criticism. A natural question that arises focuses on institutional investors. Why would “smart” institutions choose investments known to underperform in the subsequent time period? Perhaps they only select the superior performers among all SEOs and this is precisely what Gibson et. al (2004) investigate. The authors look at SEOs during the period 1980 to 1994 and sort these SEOs based on institutional ownership (low, moderate, high). In the paper, the

35 authors state that institutions increase their holdings in the SEO firms by 6.67% during the quarter of the issuance, while matched, non-issuers experience an increase in institutional holdings of only 0.39%. If one believes that SEOs underperform subsequent to issue, then the conclusion that institutions are investing in value-destroying assets is easy to make. However, as the authors point out, this assumes that institutions have no superior selectivity and are not able to pick the outperforming SEOs relative to the ones driving the average result of underperformance subsequent to issuance. Gibson et. al continue to explore this conjecture by splitting their sample of SEOs into those with high, moderate, and low institutional ownership. By doing so, the authors are able to better understand the investment decisions of SEOs made by institutions. They find that while institutions, on average, increase their holdings of all SEO firms leading up to issuance, institutions also decrease their holdings of the low ownership sub- group (by 4.59%). The highest holdings subgroup experiences an increase in institutional holdings of 21.06% from quarter -1 to +1, relative to the offer quarter “0”. This suggests a spectrum of buying and selling activity of institutions just before the issuance. All of the SEO firms in the study experience the run-up in share price previously documented in the literature over the preceding months (30.05% for low-institutional holdings and 54.84% for high-institutional holdings, both are benchmark-adjusted return measures). However, the post-issuance performance is not similar for both subgroups. The stocks belonging to the low-institutional ownership subgroup underperform their benchmark portfolio by 6.77% in the year following the issuance. The stocks in the high-institutional ownership subgroup outperform their control portfolio by 7.87% over the same time period. This evidence suggests that institutions may have superior stock-picking capabilities, or are privy to material information not shared with individuals, since they are able to select and purchase the outperforming SEOs and experience the subsequent benefits over the following 12-month period. These results Gibson et. al (2004) present are similar to the results previously documented in the IPO literature (e.g. Field, 1995, and Krigman et. al, 1999). Institutions may possess an informational-advantage, either legally or illegally, over individuals. Momentum investment strategies and a possible size effect robustness check still leads to the above results.

36 The Gibson et. al (2004) study uses a sample of SEOs beginning from 1980 through 1994. One key piece of legislation that may affect their main conclusions is Regulation Fair Disclosure (Reg FD). Reg FD was signed into law in late October of 2000 and mandates that all publicly traded firms who intentionally disclose material, private information to a select group of investors must simultaneously disclose the information to all market participants, namely the public (SEC, 2000). If institutions are able to make superior decisions in the SEO market due to private information they possess at issuance, this should change in a post-Reg FD environment. One such study seeking to discover whether institutions are able to select the overperforming SEOs after the passage of Reg FD is Cook and Tang (forthcoming, Financial Management). In the study, the authors wish to discern between the private information hypothesis or the institutions superior active management skills hypothesis. To provide a reasonable empirical test, the authors look at both pre- Reg FD (1990-1997) and post-Reg FD (2001-2004) periods. Their results for the pre-Reg FD period are consistent with Gibson et. al (2004), in that, institutions significantly increase their holdings in SEO firms from quarter -1 to quarter 0, where quarter 0 is the SEO issuance quarter. Cook and Tang find that all institutions increase their holdings of SEO firms by 9.31%, mutual funds increase holdings by 2.61%, and non-mutual fund institutions increase holdings by 5.65% during these preceding quarters. Once again, this provides evidence that despite the literature on SEO underperformance, institutions still actively invest in SEOs. The authors then take a glimpse of the latter subsample of SEOs to gauge whether institutions are behaving in a similar way after Reg FD. During this time period all institutions, mutual funds, and non-mutual funds also appear to increase their holdings of the SEO firms by 8.19%, 2.85%, and 4.27%, respectively. And up to four quarters after the quarter of issuance, institutions tend to keep similar levels of holdings in the SEO firms. This suggests that the passage of Reg FD has not deterred institutional buying activity of firms issuing SEOs. The next issue Cook and Tang (forthcoming, Financial Management) investigate is the stock performance of the SEO firms based on institutional ownership levels. They utilize the methodology first employed by Daniel et. al (1997) to come up with benchmark portfolios based on size, momentum, and book-to-market characteristics.

37 Similar to Gibson et. al (2004), Cook and Tang sort SEO firms based on institutional holdings around the offer date into high, moderate, and low subgroups. The authors find consistent results for the pre-Reg FD sample. For the group of SEOs experiencing the greatest institutional ownership, they find abnormal (excess) returns of 1.22% in the year following the issuance. This result pails in comparison to that of Gibson et. al (2004) who find 7.87%. The low-ownership group underperforms by 14.61% over the same period, creating a significant difference between the high- and low-ownership return measures. The positive abnormal returns for the high-ownership group disappear in the post-Reg FD period. The authors conclude that, perhaps, institutions do not possess the apparent stock-picking ability in a post-Reg FD environment. This may suggest that institutions were once privy to the material information, but no longer are afforded this informational advantage over individuals. Adjusting for market capitalization, Cook and Tang (forthcoming, Financial Management) find that institutions increase their holdings more in small-stock SEOs from quarters -1 to 0. However, regardless of market capitalization, institutions purchase shares of all SEOs over this preceding quarter. And regardless of market capitalization, the SEOs experiencing the highest institutional ownership changes outperform the lowest institutional ownership change SEOs. The authors conclude that institutions may be able to avoid selecting the worst performing SEOs in the post-Reg FD environment, but they can no longer select the outperforming SEOs. To further our understanding of what might have changed after Reg FD took effect, Cook and Tang (forthcoming, Financial Management) look at the informational sources that institutions may utlilize when selecting which SEOs to invest in. The empirical setup they offer to investigate this issue is to see whether institutions rely more heavily on publicly-available data variables in a post-FD world. The authors employ SEO-related variables and non-SEO related variables as possible explanatory factors driving institutions purchasing activities. All institutions appear to invest heavily in SEOs that have lower offer prices, experience upward offer price revisions, and have a higher amount of shares. When comparing the two variable sets, SEO- versus non-SEO variables, it seems that institutions rely much more heavily on information specific to the SEO issuance in the pre-Reg FD period. Looking at the post-Reg FD period, the authors

38 conclude that institutional buying activity is much more dependent on non-SEO specific variables such as turnover rate, long-term debt rate, and cash dividend payout ratios. Cook and Tang (forthcoming, Financial Management) suggest this may be due to the loss of the private information that once existed before the passage of Reg FD. If institutions have lost access to private information and must depend more heavily on publicly-available data sets, then researchers will continue to explore potential explanatory variables that might drive investment decisions. In a recent study, Walker and Yost (2008) investigate the informational content of one such variable, the intended use of proceeds of the SEO. The authors seek to answer three questions: (1) What do firms state they will use the money for? (2) What do firms actually use the money for? (3) Is the market’s reaction associated with what firms state as the intended use of proceeds? Utilizing the issuing firms’ SEC filings, the authors classify each firm according to the intended use of proceeds statement made by the financial managers of the firm. The three classifications are: investment, debt reduction, or general corporate purposes. Walker and Yost find that regardless of the stated intended use of proceeds, all three classifications increase investment levels during the three year-period beginning the year prior to the issuance year (years -1 , 0, and +1). Also, the firms stating debt reduction as the reason for the SEO actually experience an increase of 56% of their long term debt levels. More importantly, how does the market view the intended use of proceeds offered by managers? The market reaction for firms stating a specific investment plan for the funds raised through the SEO is -2.18%. And the market reaction to firms that are vague or plan debt reduction is less favorable at -3.2% and -3.26%, respectively. The difference between the market reaction of investment firms and the other two classifications is not statistically significant. While the market reaction, as measured by the two-day cumulative abnormal returns, in Walker and Yost (2008) is not incredibly informative, Autore et. al (2009) provide a more thorough analysis on the informational content of the intended use of proceeds and its effect on equity prices. We investigate whether the intended use of proceeds of SEOs contains predictive information for the long-run performance of these issuances. We first split the sample of SEO firms into similar classifications for the

39 intended use of proceeds of investment, recapitalization, and general corporate purposes. The sample for this study is much broader than the sample of Walker and Yost (2008), including the years 1997-2003. The intended use of proceeds information is collected from the SEC’s EDGAR database. The market capitalization of the SEO firms and the offer size are very similar for the investment and recapitalization classifications. However, the size of the firms stating general corporate purposes as the intended use of proceeds is about double the other SEO firms. And the SEO firms stating debt reduction as the intended use of proceeds have much higher debt ratios, as expected. Autore et. al (2009) examine the long-run stock and operating performance of the issuing firms according to their specific classifications of the intended use of proceeds. For the stock performance analysis, the authors employ a buy-and-hold abnormal return calculation and a rolling-calendar time regression approach. The results of the buy-and- hold investment approach suggest that firms stating a specific investment as the intended use of proceeds do not underperform their non-issuing matched peers over the subsequent three-year period. However, the other two classifications of recapitalization and general corporate purposes significantly underperform their benchmark counterparts, especially the recapitalization group. Perhaps this suggests the market is slow to react to the possibility that these issuing firms’ shares are overvalued and it takes some time (36- months) to correct the overvaluation. The authors also examine the long-run stock performance of SEOs according to the different classifications utilizing calendar time regressions with factor models. Using either the three-factor, Fama and French (1993), model or the four-factor model with momentum added, the results are consistent. The sample SEO firms stating the reason for raising the capital is to repay debt obligations significantly underperform (-0.74% per month). The authors skip the first six-months after issuance, and the underperformance is even larger for months 7 through 36 (-0.89% per month) than the full 36-month period. The underperformance of the general corporate purposes classification disappears using this methodology. Autore et. al (2009) proceed to look at the operating performance of these SEO firms by classification using operating income scaled by the book value of sales in their first approach. The results are as above. The investment classification does not experience a significant decline in operating performance during the three-year period

40 subsequent to issuance. However, the recapitalization and general corporate purposes groupings’ operating performance deteriorates over the same period. Many of the changes in operating performance using this approach are significantly negative at the 1% level. The authors also examine a different measure of operating performance, operating income scaled by total assets. Taking all SEO sample firms together, regardless of the intended use of proceeds, shows a significant deterioration of the operating performance of these issuing firms for the 36-month period following the SEO. Once the classifications of the intended use of proceeds are examined, the results for the other measure still hold. It appears that both the long-run stock performance and the operating performance significantly deteriorate after issuance for the recapitalization firms. Autore et. al (2009) explore the intended use of proceeds and how it may be priced into equity securities from an individual perspective. One would think that institutions, who are often considered much more sophisticated than individuals, surely would not be duped by the overpricing of the SEO securities. During the sample period used in Autore et. al (2009), the aforementioned legislation (Reg FD) took affect. Since the passage of Reg FD, many studies have attempted to quantify the informational effects felt by investors, namely institutions. One such study was conducted by Jorion et. al (2005). In their study, the authors examine credit rating agencies to detect whether the informational environment for all investors was affected by this new law. One of the exclusions of Reg FD is that nonpublic information can still be passed on to credit analysts at rating agencies. However, equity analysts are no longer privy to this nonpublic information. The authors wish to test whether credit ratings in a post-Reg FD world contain more information than before and whether the informational content drives changes in stock prices. Prior to this study, researchers found that only a downgrade by a credit rating agency provided a significant stock price movement downward, but nothing was detected for upgrades. Jorion et. al (2005) discover that post-Reg FD downgrades (upgrades) are met with a significant downward (upward) market reaction measured by stock price movements. These results suggest that the lost information to the equity analysts and other market professionals due to Reg FD is capitalized by the debt agencies. Thus, Reg FD did not destroy the flow of information from public companies to the market place. Jorion et. al (2005) focused on

41 an exempted group of market participants (rating agencies) to detect if the lost, selectively-disclosed information impeded financial markets in any way. There are two hypotheses presented in the literature on the trading activity of institutional investors for SEOs (first offered in IPO studies). The first is the manipulative trading strategy (see Gerard and Nanada, 1993). Under this hypothesis, institutions would sell shares in the pre-offer market to drive equity prices down, receive a larger SEO discount (offer price when compared to closing price on previous day) at issuance, and reap the benefits when prices rise back to fundamental levels. Under this scenario, institutions would trade in the opposite direction of their favorable information about these issuers in the pre-offer SEO market. The second hypothesis is presented in Chemmanur and Jiao (2005) and argues that “institutional investors engage in costly information production about firms making SEOs, request allocations in those SEOs about which they obtain favorable information, and buy shares in these firms before and after the offering.” Trading behavior under this view would be in the same direction as the information gathered (favorable or unfavorable). In a recent study, Chemmanur et. al (2009) investigate what role, if any, institutional investors play in the SEO market. The authors are the first to utilize transaction-level institutional trading data to examine the trading behavior in SEOs. The time period examined is 1999 to 2005, thus there may not be adequate data to test hypotheses based on pre- and post-Reg FD and to test for differences in institutional trading activities. However, given their results, institutional investors appear to be informed, intelligent traders in the SEO market, and the authors believe institutions possess private information. Institutions seek significant allocations in issuing firms who are larger in size and underwritten by more reputable investment banks. More notably, these higher allocations of SEO shares are in firms experiencing better long-term performance, relative to their issuing peers. This is consistent with the findings of Gibson et. al (2004) and the results provide support for the institutional trading hypothesis first presented in Chemmanur and Jiao (2005). In this essay, Reg FD and its effectiveness to level the playing field for all market participants is examined. This study is designed to fill a gap that currently exists in the literature. Do institutions incorporate not only the underperfomance of SEOs

42 documented in the literature, but do institutions also utilize the intended use of proceeds stated by the issuing firms? Dividing the sample period by the passage of Reg FD, this essay explores the extent to which institutions may rely more on publicly available data sources, namely the intended use of proceeds.

Hypotheses Development

The objective of this essay is to provide financial researchers with a thorough investigation of the association between institutional investors’ investment decisions when selecting SEO firms for their portfolio and the intended use of proceeds that the issuing firms provide to all investors. The SEO sample firms are assigned into the following three intended use of proceeds classifications: investment, recapitalization, and general corporate purposes (similar to Essay 1). For comparison to other institutional studies, an analysis of all three classifications taken together is completed as well. The hypotheses incorporate the recent empirical findings in the literature on institutional behavior when investing in SEOs and the intended use of proceeds studies. In addition, the important date of October 23, 2000, when Regulation Fair Disclosure took effect, is incorporated in the empirical hypotheses. Institutional investors are considered by many to be some of the most sophisticated market participants involved in daily stock trading activity. In order to corroborate the findings of Gibson et. al (2004), I first explore whether institutions are able to select the best performing SEO firms out of the entire group of issuers (All SEOs). Explicitly, the first hypothesis is:

H1: High change in institutional ownership (IO) sample firms do not outperform low change in IO sample firms during the year following issuance

H1a: High change in IO sample firms do outperform the low change in IO sample firms during the year following issuance

43 The first set of hypotheses focus on the entire sample period (1997-2007). If there are statistically higher returns for the high change in IO firms relative to the low change in IO firms, then institutions may have superior SEO stock-picking abilities. This study differs markedly from Gibson et. al (2004) because the time period examined is from 1997 to 2007. Gibson et. al (2004) examine the period from 1980 to 1994, and this does not include the passage of Reg FD. The passage of Reg FD provides a significant event date to further investigate the “smart money” effect. More specifically, this study also tests whether the previously documented prowess of institutions’ ability to select outperforming SEOs still holds in recent years. The full sample period is split into two subperiods: pre-Reg FD (1997- 1999) and post-Reg FD (2001-2007). The second and third hypotheses, H2 and H3, result:

H2: High change in IO sample firms do not outperform low change in IO sample firms during the year following issuance during the pre-Reg FD period

H2a: High change in IO sample firms outperform low change in IO sample firms during the year following issuance during the pre-Reg FD period

H3: High change in IO sample firms do not outperform low change in IO sample firms during the year following issuance during the post-Reg FD period

H3a: High change in IO sample firms outperform low change in IO sample firms during the year following issuance during the post-Reg FD period

If this study finds support for both H2 and H3, the private information and selective disclosure argument that has been made in the literature would not be a valid explanation for the observed trading behavior of SEOs by institutions. If institutions were privy to

44 the selective disclosure by issuing firms before Reg FD, but not subsequent to its passage, differences in their SEO-stock picking abilities may be detected when comparing the two subperiods. Furthermore, if the findings support H2a, but not H3a, Reg FD and its intended consequences of a more level playing field among all investors may be validated and Cook and Tang’s (forthcoming, Financial Management) results would be supported. The fourth hypothesis, H4, investigates the extent to which Reg FD may affect institution’s ability to select the superior performing issuers after its implementation:

H4: The difference in the returns of high change in IO sample firms, from the pre-Reg FD period to the post-Reg FD period, is not different from the difference in returns for the low change in IO sample firms over the same horizon

H4a: The difference in the returns of high change in IO sample firms, from the pre-Reg FD period to the post-Reg FD period, is different than the difference in returns for the low change in IO sample firms over the same horizon

If evidence is found supporting H4a, Reg FD may have produced the effects that legislators were hoping for; a more fair and balanced information flow to all investors simultaneously. The expectation is high change in IO firms will be affected significantly by the passage of Reg FD, whereas the low change in IO firms should not have a dramatic change in returns. Further, the high change in IO firms should experience a decrease (negative direction) in their returns if institutions have lost some of the informational advantage they are believed to possess prior to Reg FD. After examining the differences between the high change in IO and low change in IO firms, this essay seeks to uncover whether institutional investors may be selecting the SEO firms to invest in based on the intended use of proceeds variable. The classification scheme of the intended use of proceeds allows the study to distinguish whether or not institutions are incorporating the informational content of the intended use of proceeds into their decision-making. The empirical findings that institutions are able to select the

45 “best” SEO firms out of the overall SEO group, coupled with the results from the first essay of this dissertation on the intended use of proceeds, produces the fifth testable hypothesis:

H5: For the entire sample period (1997-2007), IO changes are not different for the investment firms versus the recapitalization firms

H5a: The IO changes for the investment firms are different than the IO changes for the recapitalization firms

If institutional investors are able to select the best performers (higher returns to the high change in IO firms), the expectation is that institutions would invest more heavily in the investment classification of SEOs. Thus, one should expect a positive sign on the difference in changes in IO of investment versus recapitalization firms. A negative sign on this difference would suggest that institutions are investing more in the recapitalization firms, which are shown to be the worst long-run performers in the first essay. While examining both the pre- and post-Reg FD periods together provides a complete analysis of the last decade, one might suspect that the reliance on the intended use of proceeds by institutional investors increases in the latter part of the sample period. If Reg FD is successful in achieving its goal of creating fair information among all market participants, namely institutions and individuals, then institutions may utilize publicly available data sources, specifically the intended use of proceeds, more in the post-Reg FD period. The above argument leads to the sixth hypothesis:

H6: The difference between changes in IO of investment firms and recapitalization firms is the same for both pre-Reg FD and post-Reg FD periods

46 H6a: The difference between changes in IO of investment firms and recapitalization firms is greater for the post-Reg FD period compared to the pre-Reg FD period

Thus far, the aforementioned hypotheses seek to answer which SEO firms institutional investors are selecting based on the changes in institutional ownership and how the entire SEO sample performs post-issuance. But additional hypotheses are needed to answer whether these institutional decisions, potentially based on the intended use of proceeds classifications, are being financially rewarded via higher, positive abnormal returns subsequent to issuance. The stock-return performance hypotheses, based on the intended use of proceeds classifications, begin with H7:

H7: For the entire sample period (1997-2007), investment firms do not outperform recapitalization firms during the year following issuance

H7a: For the entire sample period, investment firms do outperform the recapitalization firms during the year following issuance

After examining the entire sample period, I further explore the stock-return analysis for the subperiods based on the passage of Reg FD: pre-Reg FD (1997-1999) and post-Reg FD (2001-2007). If there are differences in the information set that institutions utilize to make investment decisions in SEO firms for the pre- versus post-Reg FD periods, then detection of different returns to the SEO firms they invest heavily in may result. Specifically, the eighth and ninth hypotheses, H8 and H9, are:

H8: There is no difference between the returns of the investment firms and recapitalization firms during the year following issuance in the pre-Reg FD period

47 H8a: There is a difference in the returns between the investment and recapitalization firms during the year following issuance in the pre-Reg FD period

H9: There is no difference between the returns of the investment firms and recapitalization firms during the year following issuance in the post-Reg FD period

H9a: There is a difference in the returns between the investment and recapitalization firms during the year following issuance in the post-Reg FD period

If institutions are forced to utilize publicly available data sources, such as the intended use of proceeds of SEOs, in a post-Reg FD environment, then institutional investors should select more investment firms, thus leading to higher subsequent returns to these issuers. This, of course, is assuming that institutional investors are sophisticated, select the investment firms, and are financially rewarded for their better decision making. These hypotheses and their empirical results will be informative to how institutional investors are selecting which SEO issuers to invest in. Also, this essay provides insight to the debate on whether institutions are truly “smart”, or if they were simply privy to private information before the passage of Reg FD. If the study detects that the institutional ownership patterns that were followed by superior stock-return performance in Gibson et. al (2004) disappear in the latter part of the sample period, it would cast doubt on their investing ability. This result would suggest that their advantage no longer exists in a post-Reg FD world. If the study finds significant differences between institutional ownership changes among the classifications of the intended use of proceeds, resulting in statistically different returns subsequent to issue, then this essay may have important implications. These results would suggest that individuals should incorporate what institutions have known over the past decade; the intended use of proceeds of SEOs matters. And institutions may be intelligent investment machines that individual investors should seek to replicate.

48 Sample Selection and Descriptive Statistics

The sample period for this essay is from 1997 to 2007. I first collect from the Securities Data Corporation’s (SDC) New Issues database an initial listing of all SEOs undertaken from 2004 to 2007, resulting in 3,181 SEOs.12 Upon removing financial and utility firms, initial public offerings (IPOs), shelf-registered offerings, and unit offerings there are 545 SEOs left in the sample. And similar to the first essay, I am interested in the primary component of the seasoned equity offerings; therefore, purely secondary offerings are excluded (369 SEOs remain).13 Before 1996, publicly traded firms were not required by the SEC to file their proxy statements electronically on the EDGAR database. And during the first year of the new requirement, 1996, many of the filings of SEO firms were incomplete or missing. The above sample period is chosen because 12 months of post-issuance stock return data must be available to conduct the empirical tests. I manually collect the intended use of proceeds information for the SEO firms in the sample from the EDGAR database.14 This further eliminates some SEO firms from the sample due to missing filings in EDGAR, or a foreign-firm filing. The final sample results in 1,049 unique issuances over the sample period. I collect the stock returns of the sample SEO issuing firms, as well as the benchmark control firms, from the Center for Research in Security Prices (CRSP) database. The book value of equity and any other relevant financial data for the sample and control firms is collected from COMPUSTAT. Book value is defined as “the COMPUSTAT book value of stockholders equity, plus balance sheet deferred taxes and investment tax credits (if available), minus the book value of . Depending on availability, we use the redemption, liquidation, or par value (in that order) to estimate the value of preferred stock” (Fama and French, 1993, p.8).

12 The sample of SEOs from the first essay is used for 1997 to 2003 (880 SEOs) and I append the sample I collect from SDC for 2004 to 2007 to construct the entire sample of SEOs for this essay. 13 To remain in the sample, the SEOs must meet the following requirements: (1) a share code of 10 or 11 on CRSP, (2) be listed on CRSP at time of SEO issuance, (3) the SEO is a primary seasoned offering, (4) the issue is a firm commitment and underwritten offering, (5) the firm has not made a previous equity offering in the prior three years, (6) non-negative book value of equity is available on COMPUSTAT two years prior to issuance, and (7) the firm is not a financial firm and the issue is not a shelf-offering. 14 See the first essay of this dissertation for a detailed description of the manual collection procedure for classifying the sample SEO firms into their respective groupings: INV, RECAP, or GCP.

49 In 1978, an amendment to the Securities and Exchange Act of 1934 changed how institutional investors reported their holdings information. Institutions subject to this new requirement are all institutions with more than $100 million of financial securities under discretionary management. These institutions are required to file any significant holdings information quarterly with the SEC on the 13f filings. “Significant” is defined to be all common-stock positions greater than 10,000 shares or investments in one particular firm worth more than $200,000 (NYSE, AMEX, or Nasdaq). I obtain the institutional holdings data from the quarterly 13f filings within the Thomson Financial Ownership database (CDA/Spectrum) provided by the Wharton Research Data System (WRDS). For the sample period, I collect 44 consecutive quarters of institutional holdings for the institutions in the database. For each institutional investor and for each quarter, the 13f filings not only provide the holdings data of the institutions, but also the type, or classification of the institution itself. These descriptions of the institutions are based on Standard and Poor’s definition of the institution’s primary line of business. If the institution is coded as Type 1, then it is a large bank holding company. Type 2 institutions are those listed as insurance companies. Type 3 are those investment companies classified as mutual funds. Type 4 are independent investment advisers but excludes commercial banks whose main business is mutual fund management. And Type 5 includes foundations, individuals who invest others’ funds who don’t fall into one of the aforementioned groups, and employee stock ownership plans. Beginning in the late 1990s, these classifications become somewhat “muddied” and are not reliable, distinct classifications for an empirical examination of the differences amongst the groupings. Therefore, all of the subsequent methodologies will be performed for the entire group of institutional investors taken as a whole. In addition, Cook and Tang (forthcoming, Financial Management) report results for all institutions; they discover no difference in their findings utilizing the classification scheme above. In Table 1, Panel A presents the timing of the SEOs throughout the sample period under examination. The majority of the issuance activity takes place in the beginning of the sample period, the late 1990s through 2000. The most active issuance year is 1997 with 215 unique issuances compared to only 47 in 2007. Clearly, the differing states of

50 the financial economy could possibly explain the slow-down in the SEO market. The issuances in the first four years of the sample period are almost 60% of the total seasoned equity offerings in the sample. Furthermore, most firms tend to issue SEOs in the first and second calendar-quarters during the sample period. Whereas, the months of July, August, and September represent the least active quarter for seasoned equity issuance. Panel B displays descriptive statistics for both the full sample and the distinct classifications for the intended use of proceeds of the issuance. Similar to the first essay, those firms stating the intended use of proceeds is for general corporate purposes are much larger than their peer issuers (investment or recapitalization). Intuitively, the mean (median) debt ratio for the recapitalization firms is 0.54 (0.52) which is much higher than either of the other two classifications. Investment firms have a mean (median) debt ratio of 0.33 (0.26) and general corporate purposes firms have a mean (median) debt ratio of 0.31 (0.25) In addition, Panel B shows those firms believed (in the first essay) to be “timing” the equity market by issuing when shares may be overvalued, have a higher secondary component to the issuance. The recapitalization and general corporate purposes firms have a mean secondary component of 22.66% and 28.92%, respectively. Whereas, investment firms have a mean of 14.04% of their total offering in secondary shares.

Methodology

Measuring institutional holdings

Analysis to investigate the institutional holdings patterns, beginning two quarters before (quarters -2 and -1) and ending four quarters after the SEO issuance (quarters +1, +2, +3, and +4) is performed in this essay15. I explore the level of holdings, as a percentage of the SEO firms’ total shares outstanding, that all institutions hold according to the 13f filings available each quarter-end. In addition, evidence is presented on the mean change in holdings (%) from quarter to quarter over the same time horizon.

15 The SEO issuance quarter is defined as quarter zero (0).

51 Matched non-issuer technique

In post-SEO abnormal performance studies, there are many alternative benchmarks that are possible candidates for inclusion in the matching procedure. In this essay, three of the matching techniques that are widely recommended in the literature are utilized: (1) an size-and-industry match, (2) a size-and-book-to-market equity match, and (3) a size-and-pre-issue stock price run-up match. The first benchmark is a size-and- industry matched control non-issuer, similar to Spiess and Affleck-Graves (1995). Matched firms are selected from common stocks on CRSP that did not issue equity in the prior three years. The possible matching firms are ranked by their market capitalization at the end of the month prior to the issuance. In order to select the matched-firm for each sample SEO firm, I select the firm with the same two-digit SIC code and with the closest market capitalization that is larger than the sample firm.16 If the sample SEO firm happens to be the largest firm in the two-digit SIC group, then the next largest firm is chosen as the matched-firm. Over the post-issuance performance period, if a matched- firm subsequently issues new equity or is deleted from CRSP, then it is replaced at that time with a different matched-firm. According to Barber and Lyon (1997), choosing control firms based on size and book-to-market equity ratios provides test statistics that are well specified and alleviates the new listing, rebalancing, and skewness biases. For the second matching technique, the size-and-book-to-market equity matched-firm approach, a procedure similar to Eckbo, Masulis, and Norli (2000), which is based on Fama and French’s (1993) portfolio ranking technique, is used. First, a list is compiled of all firms that have total market capitalization within 30% of the total market capitalization value of the sample SEO firm at the end of the year prior to the offering date. The firm with the closest book-to-market equity ratio to that of the sample SEO firm is chosen as the matched-firm under this approach. In order to control for reporting lags, I use book values from COMPUSTAT that are appropriate depending upon when in the calendar-year the sample firm issues the

16 Spiess and Affleck-Graves (1995) recommend choosing a larger matched firm because the size of the sample firm is expected to increase between the date of the size measurement and the post-issuance period. Issuing firms will likely increase in market capitalization due to the capital raised by the firm through the SEO process. In addition, the stock price run-up before issuance, but after December 31 of the prior year, will also increase the value of the sample firm.

52 SEO. If the equity offer occurs in the first six months of the year, then the book value is for the fiscal year-end two years earlier. In contrast, if the equity offer occurs in the latter six months of the year, the book value is for the prior fiscal year-end. A replacement matched-firm is chosen from the original list, using the above procedure, if the first matched-firm either delists or subsequently issues equity during the abnormal performance detection period. The third matching technique, a size-and-pre-issue-run-up match, is suggested in Jegadeesh (2000). I match the sample SEO firms to non-issuing firms within their same size decile, based on the market value of equity. In addition, the control firm must be in the sample firm’s same decile of six-month compound returns prior to the calendar month of the issue. The non-issuer that has the closest stock price run-up to that of the issuing sample firm is chosen as the match, as long as the run-up is within 30% of the issuing firm. Buy-and-hold abnormal returns are calculated as the buy-and-hold return of the sample SEO firm minus the buy-and-hold return of the appropriate matched-firm as follows:

τ τ BHARi,τ = ∏ 1[ + R ,ti ] − ∏ 1[ + MR ,ti ] , (1) t =1 t =1

 where BHARi,τ is the buy-and-hold abnormal return for sample firm i for length

months, R ,ti is the return for sample SEO firm i in month t, where month t=1 is the month immediately following the offering date month, and MR ,ti is the return in month t of the matched, comparison firm associated with each sample SEO firm i. I measure the buy-and-hold abnormal return calculations for the 12-month period subsequent to the issuance of the SEO.17 For example, if the issuance is January 15th, then the calculation begins with the monthly return for February and ends with the subsequent January’s return.

17 In other words,  = 12.

53 In the case of a sample SEO firm delisting, the missing return value is replaced first with the available CRSP delisting return for that month. If the delisting return is not available on CRSP, then the Shumway (1997) approach is used for the month of delisting. Specifically, if the delisting sample firm was traded on the NYSE or AMEX, the delisting return is replaced with -0.30, and if the firm was listed on NASDAQ, the return is replaced with -0.55. Any returns still missing for the sample firms, but are needed in the calculation of the one-year post-issuance detection period, are replaced with the associated matched-firm’s return measure for that month. The results of this analysis will provide evidence for the stock performance-based hypotheses.

Factor-Model Regressions

Many recent studies posit that the matched-firm technique does not control for differences in risk between the sample SEO firms and the control firms (see Eckbo, et al. (2000) and Brav, et al. (2000), among others). In order to address these concerns, I test whether the abnormal performance that is detected in prior studies using the matched- firm approach is also present when utilizing a calendar-time factor model. Fama (1998) suggests the use of the rolling calendar month portfolio methodology, introduced by Jaffe (1974) and Mandelker (1974), to reduce some of the concerns about drawing inferences from BHARs. In each calendar month, a portfolio is formed of sample SEO firms that conducted a seasoned equity offering in the previous one-year period. Since my sample of SEO firms spans the period from January 1997 to December 2007, and the financial and returns data are available through the end of 2008, I begin the portfolio formations in February of 1997 for the one-year abnormal return detection analysis. For example, the portfolio formed in January 2000 will be composed of those sample firms that issued a SEO in the prior one-year period, from January 1999 to December 1999. Each month of portfolio formation, sample firms will enter the portfolio, as described above, and certain sample firms will exit the portfolios at the point in time of their first anniversary month of the SEO or upon delisting. The last portfolio that is constructed for the factor-regressions is December 2007, in order to have return data sufficient to calculate 12-month post-issuance returns for the portfolio.

54 The regression analysis is utilized on equally-weighted portfolios.18 The calendar-time returns on these portfolios are used to estimate the following factor-model introduced by Fama and French (1993):

Rpt − R ft = α p + β p[MKTt ] + s p[SMBt ] + hp[HMLt ] + ε pt , (2)

where Rpt is the monthly return on the calendar-time portfolio of sample equity issuers,

R ft is the monthly return on the three-month Treasury bill, MKT is the return on the value-weighted CRSP market index minus the monthly return on the three-month Treasury bill, SMB is the difference in the returns of a value-weighted portfolio of small stocks and big stocks, and HML is the difference in the returns of a value-weighted portfolio of high book-to-market stocks and low book-to-market stocks. The three Fama and French factors (MKT, SMB, and HML), as well as the three-month Treasury bill rates, are obtained from Kenneth French’s website. The intercept term, , provides a measure of the mean monthly abnormal return on the calendar-time portfolio of SEO firms. In addition to the above three-factor model, a fourth factor will be added to the regression model to produce the following:

R pt − R ft = α p + β p [MKTt ] + s p [SMBt ] + hp [HMLt ] + m p [MOM t ] + ε pt , (3) where MOM is the Carhart momentum factor. This factor represents the difference in returns of past winners and losers, as measured over the previous 12-month period ending one month prior to formation and is obtained from Kenneth French’s website.

Empirical Results

The initial analysis of this essay is needed to confirm results offered in the literature regarding institutional investors’ ability to select outperforming seasoned equity

18 Equal-weighting of the portfolios is used because Loughran and Ritter (2000) argue that factor models with value-weighted portfolio returns as the dependent variable have low power to detect abnormal returns following managerial actions.

55 offerings. Namely, Gibson et. al (2004) and Cook and Tang (forthcoming, Financial Management) uncover that institutions are able to select superior-performing SEOs and avoid those that will significantly underperform benchmarks in the near future. Stock return analysis is utilized, using the aforementioned methodologies, to shed light on whether institutions are indeed being rewarded for their superior-selection ability in the SEO market. Before the analysis begins, sample firms are sorted into quintiles based on the institutional ownership (IO) changes from quarters -1 to +1. The high change in IO quintile represents those issuers with the highest changes in IO around the offering quarter. The middle three quintiles are grouped into the moderate change in IO group and those issuers with the lowest change in IO are in the low change in IO quintile. Tables 2 through 4 present the results for the three matching techniques: the size-and- book-to-market equity match, the size-and-industry match, and the size-and-pre-issue- run-up match, respectively. Panels A, B, and C of each table report results for the entire sample period, pre-Reg FD, and post-Reg FD time periods, respectively.19 Tests of means and medians are presented.20 Unreported in Table 2, institutions increase their holdings by 35.93% in the high change in IO quintile, while reducing their holdings by 10.04% in the low change in IO quintile, over the full sample period.21 The results in Table 2 for the size-and-book-to- market equity matching technique do not support previous findings that institutions are able to select the outperforming SEOs. In Panel A, though the mean BHARs are different in magnitude, 11.21% for the high change in IO quintile and 2.92% for the low change in IO quintile, they are not significantly different. Hypothesis H1 is not rejected in this instance. However, in most cases, for both the pre-Reg FD and post-Reg FD

19 Buy-and-hold abnormal returns are winsorized at the top and bottom 0.05% to control for possible outlier effects. 20 The t-statistics reported test for the difference in means. All t-statistics are reported utilizing the Satterthwaite method. Under this approach, the underlying populations are not assumed to have equal variance. Cook and Tang (2010) also report test statistics for this approach. Differences of medians are analyzed using the Wilcoxon rank sum test and produces the z-statistic reported in each table. 21 The difference of the mean change in holdings between the high change in IO quintile and low change in IO quintile is always significant, regardless of the time period or matching technique used, and is similar in magnitude to previous studies (see Gibson, et. al, 2004, and Cook and Tang , forthcoming Financial Management).

56 periods, the high change in IO quintile experiences BHARs that are greater than their low change in IO quintile counterparts. One striking result is offered in Panel C. Those issuers in the high change in IO quintile actually underperform their benchmark by a mean (median) of 12.29% (14.67%). Compared to overperformance of the high change in IO quintile of 48.68% in the pre-Reg FD period, this may suggest a disappearance of “ability” for institutions in the SEO market. The results for the difference in median BHARs are similarly insignificant, regardless of time period examined. The second and third hypotheses developed in this essay, H2 and H3, are not rejected based on the findings for the size-and-book-to-market equity matching technique. Utilizing the size-and-industry matching technique does, however, confirm earlier findings in the extant literature and leads to different conclusions concerning hypotheses H1 and H2. In Table 3, Panel A, high change in IO quintile firms outperform their benchmarks by a mean BHAR of 9.02% over the subsequent year, whereas, the low change in IO quintile firms underperform their benchmarks by a mean BHAR of 18.63%; this difference of 27.65% is significant at the 5% level. Therefore, hypothesis H1 is rejected. This disparity in performance between the high change in IO and low change in IO quintiles can also be seen when focusing on Panel B, the pre-Reg FD period. The difference between the mean BHARs (high change in IO – low change in IO) is 54.48% and statistically significant; thus, H2 is rejected. In most cases, the tests of the difference in median BHARs support the findings for the mean BHARs. In Panel C, during the post-Reg FD period, institutions are not displaying the same ability in their choices of SEOs. The high change in IO quintile firms underperform their benchmarks by a mean (median) of 8.41% (12.24%). The difference between the mean, or median, BHARs for the high change in IO and low change in IO quintiles is no longer significant. This result lends support for the private information argument presented in previous studies and hypothesis H3 is not rejected. Perhaps Reg-FD eliminated some of the private information being passed from the issuing firms to the large, institutional investors. Table 4 presents findings for the size-and-pre-issue-run-up BHAR analysis. The results confirm the rejection of hypotheses H1 and H2 above, and are qualitatively similar to those reported in Table 3. Over the entire sample period, high change in IO quintile firms significantly outperform low change in IO quintile firms by a difference of 21.51%

57 (Panel A). For the subperiod analysis, institutions are able to select the superior SEOs out of the entire group of SEOs prior to Reg FD. The high change in IO quintile significantly outperforms the low change in IO quintile by 51.18% (Panel B). However, this ability dissipates after the passage of Reg FD and hypothesis H3 is not rejected. This artifact may be due to the evaporation of the private information once possessed by institutional investors before the legislation took effect. Many researchers believe that the buy-and-hold abnormal return approach possesses a serious flaw in its ability to select the proper matched firm and suggest the use of factor models as an alternative (see Eckbo et. al, 2000, amongst others). Table 5 presents results for the Fama-French three-factor model, and the addition of the Carhart momentum factor in the four-factor model. Evidence is provided for all SEOs and for the different groups based on changes in institutional ownership: the high change in IO quintile, the moderate change in IO (middle-three quintiles of change in IO) group, and the low change in IO quintile. Panels A, B, and C investigate the full sample period, the pre-Reg FD period, and the post-Reg FD period, respectively. Panel D reports results for testing the difference of alphas (abnormal returns) between the high change in IO quintile and low change in IO quintile for each time period examined. To accomplish this, for each month under analysis, the dependent variable is the equally-weighted return on the portfolio of the high change in IO quintile minus the equally-weighted return on the portfolio of the low change in IO quintile (long-short strategy). In addition, Panel D also presents results for testing the difference of alphas across subperiods by inclusion of the post variable in the regression (post = 1 if the portfolio return is from the post-Reg FD period). For the full sample period, the low change in IO quintile of issuers underperforms significantly (at the 5% level for both factor models). The annualized abnormal return is -18.72% and -15.36% for the three-factor and four-factor models, respectively. While the alpha is slightly positive for the high change in IO quintile, it is not significantly different than zero. Panel B shows that during the pre-Reg FD period, the high change in IO quintile issuers experience significant abnormal returns of 27.96% (three-factor model) over the 12-months subsequent to issuance. This suggests that institutions have superior ability to select the outperforming SEOs. However, this ability seems to disappear after

58 the passage of Reg FD. The high change in IO quintile experiences negative abnormal returns during the post-Reg FD period, albeit not significant. In addition, Panel C provides evidence to support the conjecture that institutional investors are able to avoid holding those SEOs that will significantly underperform over the following year. The one-year abnormal return for the low change in IO quintile of SEO firms is -26.04% (three-factor) and -25.80% (four-factor) for issuances conducted after Reg FD’s implementation. Cook and Tang (forthcoming, Financial Management) produce similar findings using the Daniel et. al (1997) portfolio approach for benchmarking. While institutions were able to select SEOs that would experience positive abnormal returns before the passage of Reg FD, they no longer have the ability to do so. However, institutional investors are still able to avoid holding the worst performing issuers. Panel D shows the abnormal returns (alphas) for the high change in IO quintile minus the low change in IO quintile are 22.32% (three-factor model) and 16.32% (four-factor model) on an annual basis over the entire sample period; both are significant at the 5% and 10% level, respectively. Hypothesis H1 is rejected. When examining the subperiods, for the pre-Reg FD period utilizing the three-factor model, the one-year abnormal return is 41.88% and significant at the 5% level; thus, hypothesis H2 is rejected. For either model, the post-Reg FD results are not significant and provide support for H3. Further, the effect of Reg FD on issuers’ abnormal performance is magnified, and significant, only for the high change in IO quintile of firms. For both factor models, the abnormal performance for the high change in IO quintile firms is significantly less after the passage of Reg FD. This return differential present for the high change in IO quintile, but not significant for the low change in IO quintile allows for the rejection of hypothesis H4. Table 6 presents results for the cross-sectional regression of the dependent variable, one-year buy-and-hold abnormal returns, on dummy variables to capture both the post-Reg FD effect (Post = 1 if the issuance takes place during the post-Reg FD period, Post = 0 if the issuance is from the pre-Reg FD period) and the difference between the high change in IO quintile and the low change in IO quintile firms (High = 1 if the issuance is in the high change in IO quintile, High = 0 if the issuance is in the low change in IO quintile). An interaction term is included as well, Post*High, to capture the incremental effect of Reg FD on the high change in IO quintile of firms. The interaction

59 term tests how the implementation of Reg FD affects BHARs of firms in the high change in IO quintile compared to how it affects BHARs of firms in the low change in IO quintile. The coefficient of the interaction term indicates the differential effect of Reg FD on BHARs for the high change group as opposed to the low change group. Thus, it tests for statistical significance in the differences observed in Panels B and C in Tables 2 through 4. Panels A, B, and C, report results for the size-and-book-to-market equity match, the size-and-industry match, and the size-and-pre-issue-run-up match, respectively. The coefficient on Post*High is -0.52 in Panel A and -0.76 in Panel C; both are significant at the 1% level. The coefficient on the interaction variable in Panel B is -0.36 and significant at the 5% level. The -0.36 provides evidence of a significant decline in returns for the high change in IO firms after Reg FD, when compared to before Reg FD. This suggests that institutions may no longer be able to select the best performing SEOs after the implementation of Reg FD which is consistent with the informational loss hypothesis presented in the extant literature. Hypothesis H4 is rejected with these results. In Tables 7 through 9, results are presented for the institutional holdings data in and around the offering quarter. Panel A of each table reports the levels, as a percentage of the outstanding shares, institutional investors hold. Panel B of each table presents the changes in institutional ownership across calendar quarters. Institutional holdings data is displayed for all SEO firms and by classification of the intended use of proceeds. For the entire sample period (Table 7), institutional investors increase their ownership levels in the sample of all SEO firms from the quarter-end preceding the issue to the quarter-end of the issuance (from 37.83% to 50.35%). These patterns and levels are similar to those observed in previous studies. Cook and Tang (forthcoming, Financial Management) examine a similar time period and find a surge from 37.13% to 46.30% in the pre-Reg FD period. Similarly, Gibson et. al (2004) find an increase in institutional ownership from 30.59% to 36.68%; they utilize an earlier time period (1980-1994) and find a similar pattern in institutional ownership, albeit at lower levels. The patterns uncovered here suggest that institutions are increasing their holdings in seasoned equity offerings, even though underperformance of these securities has been widely published. Key to this study is the intended use of proceeds of the sample SEOs. Segregating the sample firms by their intended use of proceeds reveals similar findings in

60 the institutional ownership changes in the SEO market. In Panel B of Table 7, regardless of whether firms state investment, recapitalization, or general corporate purposes as their intention, institutions increase holdings over the entire sample period of all three classifications from quarter -1 to 0 (12.61%, 13.68%, and 11.57%, respectively). The difference in mean, or median, change in institutional ownership between investment and recapitalization firms is not significant for any quarter-to-quarter period. According to the findings of Autore et. al (2009), the recapitalization firms are the significant underperformers over the subsequent three years when compared to their issuing counterparts. However, as Table 7 shows, institutions tend to hold more shares, both as levels and changes in institutional ownership, of those issuers stating recapitalization as the intended use of proceeds. Hypothesis H5 is not rejected with the results in Panel B. Comparing the institutional holdings data over the pre-Reg FD (Table 8) and post- Reg FD (Table 9) periods, institutions are increasing their holdings of SEOs more after the passage of Reg FD.22 The change in institutional ownership, for all sample firms, from quarter -1 to quarter 0 increased from a mean of 12.46% (pre-Reg FD) to 13.70% (post-Reg FD). Up to four-quarters after the issuance, institutional investors hold 63.92% of the outstanding shares of recapitalization issuers during the post-Reg FD period. The only significant difference in the change in institutional ownership between investment and recapitalization firms is between quarters +2 and +3 during the pre-Reg FD period. This difference is 2.87% and significant at the 10% level. The positive sign indicates that institutions were significantly increasing their holdings in investment firms when compared to their recapitalization peers (refutes hypothesis H5). This is consistent with the expectation derived from the first essay: investment firms are the “better” performers over the long-run when compared to recapitalization firms; thus, “smart” institutions should invest in these issuers. The inclusion of the intended use of proceeds variable to the stock return analysis is presented in Table 10 for the BHAR methodology. Panels A, B, and C, report findings for the entire sample period, the pre-Reg FD period, and the post-Reg FD period, respectively. Given the findings of the first essay and the idea that financial managers

22 Based on numerical values only; no tests of significance in the levels of institutional ownership are reported.

61 may wish to exploit overvalued securities through an SEO, a natural extension is to investigate whether institutions are paying attention to the intended use of proceeds of SEOs. It does not appear that institutions are increasing their holdings in the investment firms more significantly than the recapitalization issuers from quarters -1 to +1.23 Examining the entire sample period in Panel A, none of the mean or median BHARs for the investment firms are significantly different than those for the recapitalization firms. Hypothesis H7 is not rejected. The results in Panel B support hypothesis H8 and show no difference between the BHARs of investment and recapitalization firms, regardless of matching technique. However, there is one instance where the BHARs for the investment firms are significantly different than the recapitalization classification. When issuers are matched to control firms based on size-and-book-to-market equity, and examining the post-Reg FD period (Panel C), investment firms outperform their benchmarks by 8.17% during the year following issuance. Further, their recapitalization peers underperform their benchmark, on average, by 14.19% over the same horizon. This difference in the mean (median) BHARs of 22.36% (17.76%) is significant at the 5% level. This is the only instance in the BHAR analysis which leads to rejection of Hypothesis H9. The calendar-time regression results for the intended use of proceeds analysis are presented in Table 11. Similar to the findings of Autore et. al (2009), the recapitalization firms are the significant underperformers during the entire sample period (Panel A). The alpha for the recapitalization firms is -0.80 (three-factor model) and -0.92 (four-factor model). On an annual basis, this corresponds to abnormal returns of -9.60% and -11.04% (both significant at the 5% level). However, when examining Panels B and C, this underperformance is restricted to the post-Reg FD period. The recapitalization firms experience significant, annual abnormal returns of -18.36% (three-factor model) and - 20.04% (four-factor model). Panel D provides results for an investment strategy of taking a long position in investment firms while simultaneously taking a short position in recapitalization issuers. For the full sample period, utilizing the four-factor model, investment issuers significantly (at the 10% level) outperform their recapitalization peers by 11.88% over the subsequent 12-months; thus, hypothesis H7 is rejected. However,

23 Unreported in Table 10, there is no difference in mean or median changes in institutional ownership between the investment and recapitalization classifications, regardless of time period examined.

62 when viewing the subperiod results, hypotheses H8 and H9 are not rejected; no difference in the returns of investment versus recapitalization firms is detected for either the pre-Reg FD or post-Reg FD period. In Table 12, results are presented from a cross-sectional regression of the dependent variable, change in institutional ownership, on dummy variables to capture both the post-Reg FD effect (Post = 1 if the issuance takes place during the post-Reg FD period, Post = 0 if the issuance is from the pre-Reg FD period) and the difference between investment and recapitalization firms (INV = 1 if the issue is an investment firm, INV=0 if the issue is a recapitalization firm). An interaction term is included as well (Post*INV). Panels A, B, and C, report results for Model 1 (INV only), Model 2 (INV and Post), and Model 3 (INV, Post, Post*INV), respectively. The coefficient on Post is 2.18 and significant at the 5% level in Panel B and 1.70 and significant at the 10% level in Panel C. This suggests that institutions are increasing their holdings more in the post- Reg FD period. However, the coefficient on Post*INV (Panel C) is -0.004 and not significant. This implies no difference in the changes of institutional ownership for investment versus recapitalization firms when moving from the pre-Reg FD and post-Reg FD periods. Hypothesis H6 is not rejected. The results in Table 13 provide further evidence that institutional investors are able to select superior performing SEOs, more so during the pre-Reg FD period. The sample SEO firms are not only grouped based on the level of institutional ownership changes from quarters -1 to +1, but also further classified within change in IO quintiles by the intended use of proceeds classification. Alphas (standard errors) are reported for the calendar-time regressions in Panel A (three-factor model) and Panel B (four-factor model). Given the previous results, the significant alphas in Table 13, with their appropriate sign, are just as expected.24 For the high change in IO quintile firms, the previously documented overperformance of these issuers is restricted to the recapitalization and general corporate purposes firms during the pre-Reg FD period. Further, institutions are able to hold relatively lower amounts of shares in those issuers

24 There is no formal hypothesis developed in this essay for the results presented in Table 13. Some of the calendar-time portfolios for the pre-Reg FD period only have one or a few observations (i.e. one or two issuing firms that belong to that grouping for a given month). Thus, the statistical precision is weak and the results presented may not be reliable.

63 which underperform during the post-Reg FD period. However, for high change in IO quintile firms whose intended use of proceeds is general corporate purposes, there is significant underperformance detected during the post-Reg FD period. The annualized abnormal return of these issuers is -23.28% (three-factor model) and -24.84% (four-factor model). This provides evidence suggesting institutions, at times, may hold high levels of SEO shares in firms they should be avoiding. This may be due to a loss of private information in a post-Reg FD world.

Conclusion

Institutional investors play a significant role in financial markets; they hold more than 50% of the outstanding shares of equity in publicly traded U.S. firms.25 Therefore, financial researchers actively search for a better understanding of the informational advantages possessed by these large, sophisticated (“smart”) investors. This essay explores whether the passage of Reg FD may have stifled any informational advantage; namely, private and material information once believed to have been passed from SEO firms to these influential market participants. Coupled with the results from the first essay (published in the Journal of Corporate Finance), this essay investigates the extent to which institutions may be selecting which SEO firms to invest their clients’ wealth based on the intended use of proceeds stated in the proxy statement. The results from this study support prior findings in the literature.26 Institutions are able to select the superior performing SEOs, while foregoing the biggest losers. Issuing firms belonging to the high change in IO quintile significantly outperform the firms in the low change in IO quintile, irrespective of the methodology employed. For the buy-and-hold technique, the one-year abnormal return difference ranges from 26.35% (size-and-book-to-market matching ) to 54.48% (size-and-industry matching). Utilizing the calendar-time factor models to detect abnormal performance, the same pattern exists; the high change in IO quintile outperforms the low change in IO quintile by 3.49% per month ( over 41% annualized, Panel D of Table 5). These significant findings are

25 See Gompers and Metrick (2001). 26 See Gibson et. al (2004), Cook and Tang (forthcoming Financial Management), amongst others.

64 isolated in the pre-Reg FD period, suggesting that institutions may no longer be able to select the “winners” out of the entire SEO group of firms. In each of the post-Reg FD analyses, regardless of empirical methodology, there is no difference between the performance of the high change in IO and low change in IO quintiles. Institutional investors tend to hold more shares, as a percentage of shares outstanding (levels), of the recapitalization issuers than the investment issuers.27 This is counterintuitive to the findings of the first essay; investment issuers are the relatively “better” performers over the long-run, when compared to their recapitalization peers. The only time period where there is no difference in the level of IO between investment and recapitalization firms is the pre-Reg FD period, which coincides with the aforementioned superior performance of the high change in IO quintile. Perhaps institutions should invest more in the investment issuers (i.e. raise their level of holdings) and less in the recapitalization issuers in a post-Reg FD world. If private information once passed to these institutional investors has evaporated like many researchers imply, it may be advantageous for institutions to incorporate the findings of the first essay when investing in SEOs. Cook and Tang (forthcoming Financial Management) find institutions rely more on public information that is not related to the issuance itself in a post-Reg FD environment. Future research is needed to uncover exactly which publically-available data institutional investors are using when selecting SEOs to invest in. As time passes, it may not be surprising to see institutional investors significantly reduce their holdings in all SEOs, given they can no longer select issuers who experience superior performance. Institutional investors are believed to be incredibly “smart” and sophisticated market participants, however, they still continue to hold shares of SEO firms, which, on average, experience negative abnormal performance.

27 Unreported in Panel A of Tables 7 (full sample period) and 9 (post-Reg FD), the difference between investment and recapitalization levels of IO is always statistically significant with a negative sign.

65 Table 1. Distribution of SEO Issuances and Sample Descriptive Statistics Panel A presents the timing of seasoned equity offerings throughout the entire sample period (1997-2007), as well as the percentage of the entire SEO sample being conducted in any given calendar year. Panel B provides summary statistics for the means (medians) for sample firms. Market value is measured the month-end prior to the equity issuance. Primary shares (%) are the percentages of total shares offered in which the proceeds are directly placed with the issuing firm. Secondary shares (%) are the percentages of total shares offered in which the proceeds go directly to the selling shareholder/company employee. Debt ratio is the ratio of long-term debt plus short-term debt to total book assets, and is the year-end figure in the year prior to the issue.

Panel A: Distribution in Timing for Seasoned Equity Offerings Year Quarter Total Percent

1 2 3 4

1997 60 36 47 72 215 20.50 1998 35 60 9 5 109 10.39 1999 28 37 23 33 121 11.53 2000 97 26 25 18 166 15.82 2001 18 20 19 15 72 6.88 2002 24 26 4 4 58 5.53 2003 8 8 16 19 51 4.86 2004 33 28 15 13 89 8.48 2005 14 18 16 14 62 5.91 2006 17 18 8 16 59 5.62 2007 9 17 7 14 47 4.48

Total 343 294 189 223 1049 100

Panel B: Descriptive Statistics of SEO firms Means with Medians in Parentheses Market Value Primary Shares Secondary Shares Debt Ratio ($Thousands) (%) (%)

All SEO Firms 457,643 77.45 22.55 0.38 (N=1049) (202,991) (91.29) (8.71) (0.35)

Investment 445,698 85.96 14.04 0.33 (N=315) (153,384) (100.00) (0.00) (0.26)

Recapitalization 348,454 77.34 22.66 0.54 (N=317) (185,527) (90.66) (9.34) (0.52)

General Purposes 549,451 71.08 28.92 0.31 (N=417) (237,918) (82.11) (17.89) (0.25)

66 Table 2. Size and Book-to-Market Buy-and-Hold Abnormal Returns for SEOs This table presents the mean and median buy-and-hold abnormal returns for sample firms matched to control firms based on market capitalization and book-to-market equity ratios. Sample firms are sorted based on institutional holdings changes around the issue quarter (quarter 0). Change in holdings is measured from quarter -1 to quarter +1. Low, moderate, and high IO are the sample firms in the lowest, middle three, and highest quintile based on the change in institutional holdings, respectively. The buy-and- hold abnormal returns reported are the one-year post- issuance buy-and-hold returns of the issuing, sample firm minus the buy-and-hold return of the control firm over the same time period. Panel A reports results for the entire sample period. Panels B and C present pre- and post- Regulation Fair Disclosure results, respectively. T-statistics for means, z-statistics for medians, and p-values are reported to test if the difference in means or medians is zero. The t-statistic reported is adjusted using the Satterthwaite method and the z-statistic is for the Wilcoxon rank sum test. ***, **, * denote significance at the 1%, 5%, and 10% levels, respectively. Mean Median Change in Buy-and-Hold Buy-and-Hold Institutional Ownership (IO) Abnormal Returns Abnormal Returns

Panel A: Full Sample Period (1997-2007)

Low change in IO 2.92% 1.94% Moderate change in IO 3.32 3.08 High change in IO 11.21 -1.65

High ∆IO – Low ∆IO 8.30% -3.59% Test statistic t = 0.71 z = -0.05 (p-value) (0.4800) (0.9562)

Panel B: Pre-Regulation FD Sample Period (1997-1999)

Low change in IO 22.33% 3.09% Moderate change in IO 5.71 3.32 High change in IO 48.68 26.36

High ∆IO – Low ∆IO 26.35% 23.27% Test statistic t = 0.92 z = 1.00 (p-value) (0.3589) (0.3166)

Panel C: Post-Regulation FD Sample Period (2001-2007)

Low change in IO -12.59% -7.22% Moderate change in IO 1.37 2.84 High change in IO -12.29 -14.67

High ∆IO – Low ∆IO 0.30% -7.45% Test statistic t = 0.02 z = 0.40 (p-value) (0.9803) (0.6906)

67 Table 3. Size and Industry Buy-and-Hold Abnormal Returns for SEOs This table presents the mean and median buy-and-hold abnormal returns for sample firms matched to control firms based on market capitalization and industry classification. Sample firms are sorted based on institutional holdings changes around the issue quarter (quarter 0). Change in holdings is measured from quarter -1 to quarter +1. Low, moderate, and high IO are the sample firms in the lowest, middle three, and highest quintile based on the change in institutional holdings. The buy-and-hold abnormal returns reported are the one-year post-issuance buy-and-hold returns of the issuing, sample firm minus the buy-and-hold return of the control firm over the same time period. . Panel A reports results for the entire sample period. Panels B and C present pre- and post-Regulation Fair Disclosure results, respectively. T-statistics for means, z-statistics for medians, and p-values are reported to test if the difference in means or medians is zero. The t-statistic reported is adjusted using the Satterthwaite method and the z- statistic is for the Wilcoxon rank sum test. ***, **, * denote significance at the 1%, 5%, and 10% levels, respectively. Mean Median Change in Buy-and-Hold Buy-and-Hold Institutional Ownership (IO) Abnormal Returns Abnormal Returns

Panel A: Full Sample Period (1997-2007)

Low change in IO -18.63% -21.57% Moderate change in IO 1.23 -3.22 High change in IO 9.02 -4.58

High ∆IO – Low ∆IO 27.65% 16.99% Test statistic t =2.53** z = -2.69*** (p-value) (0.0118) (0.0072)

Panel B: Pre-Regulation FD Sample Period (1997-1999)

Low change in IO -9.51% -15.62% Moderate change in IO 14.18 4.31 High change in IO 44.97 14.89

High ∆IO – Low ∆IO 54.48% 30.51% Test statistic t =1.97* z = 2.21** (p-value) (0.0519) (0.0273)

Panel C: Post-Regulation FD Sample Period (2001-2007)

Low change in IO -14.22% -16.07% Moderate change in IO -4.34 -5.74 High change in IO -8.41 -12.24

High ∆IO – Low ∆IO 5.81% 3.83% Test statistic t = 0.59 z = -0.77 (p-value) (0.5582) (0.4396)

68 Table 4. Size and Pre-Issue Run-up Buy-and-Hold Abnormal Returns for SEOs This table presents the mean and median buy-and-hold abnormal returns for sample firms matched to control firms based on market capitalization and six-month compound returns leading up to the equity issuance. Sample firms are sorted based on institutional holdings changes around the issue quarter (quarter 0). Change in holdings is measured from quarter -1 to quarter +1. Low, moderate, and high IO are the sample firms in the lowest, middle three, and highest quintile based on the change in institutional holdings. The buy-and- hold abnormal returns reported are the one-year post-issuance buy-and-hold returns of the issuing, sample firm minus the buy-and-hold return of the control firm over the same time period. Panel A reports results for the entire sample period. Panels B and C present pre- and post-Regulation Fair Disclosure results, respectively. T-statistics for means, z-statistics for medians, and p- values are reported to test if the difference in means or medians is zero. The t-statistic reported is adjusted using the Satterthwaite method and the z-statistic is for the Wilcoxon rank sum test. ***, **, * denote significance at the 1%, 5%, and 10% levels, respectively. Mean Median Change in Buy-and-Hold Buy-and-Hold Institutional Ownership (IO) Abnormal Returns Abnormal Returns

Panel A: Full Sample Period (1997-2007)

Low change in IO -9.28% -12.43% Moderate change in IO -1.04 -5.73 High change in IO 12.23 -2.74

High ∆IO – Low ∆IO 21.51% 9.69% Test statistic t =1.85* z = -1.46 (p-value) (0.0656) (0.1445)

Panel B: Pre-Regulation FD Sample Period (1997-1999)

Low change in IO 7.44% -7.94% Moderate change in IO -0.59 -6.48 High change in IO 58.62 24.77

High ∆IO – Low ∆IO 51.18% 32.71% Test statistic t =1.93* z = -1.92* (p-value) (0.0565) (0.0553)

Panel C: Post-Regulation FD Sample Period (2001-2007)

Low change in IO -19.10% -26.54% Moderate change in IO -3.61 -5.73 High change in IO -23.92 -24.79

High ∆IO – Low ∆IO -4.82% 1.75% Test statistic t =-0.43 z = -0.01 (p-value) (0.6687) (0.9887)

69 Table 5. Calendar-Time Portfolio Regressions by Change in Institutional Ownership of SEOs This table presents monthly estimates from regressing calendar-time portfolio (equally-weighted) monthly returns on the Fama-French (1993) three factors (MKT, SMB, HML) and the four-factor model that also includes Carhart’s (1997) momentum factor (MOM). The four factors are obtained from Ken French’s website. Panel A reports the coefficient estimates using a one-year period beginning the month after issuance for the full sample period (1997-2007). Panel B uses the same time horizon, however, it includes only those SEOs conducted during the pre-Regulation FD time period (1997-1999). Panel C presents the results for the post-Regulation FD time period. In each panel, the first set of columns (a and b) display estimates for all issuers, and the subsequent sets of columns (1a and 1b, 2a and 2b, and 3a and 3b) display coefficients for the three categories of firms based on their change in institutional ownership (IO): (1) firms belonging to the highest quintile of change in IO around the SEO (quarters -1 to +1), (2) firms belonging to the middle three quintiles of change in IO, and (3) firms belonging to the lowest quintile of change in IO. Panel D presents the alphas from a long (high change in IO) and short (low change in IO) strategy as well as results for pre- versus post-Reg FD for the high change in IO and low change in IO quintiles. Standard errors are in parentheses. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.

(1) (2) (3) All Issuers High Change in IO Moderate Change in IO Low Change in IO a b 1a 1b 2a 2b 3a 3b

Panel A: Full Sample Period (1997-2007)

Alpha -0.15 -0.15 0.22 0.00 0.11 0.09 -1.56** -1.28** (0.28) (0.28) (0.53) (0.53) (0.35) (0.36) (0.62) (0.62)

MKT 1.30*** 1.30*** 1.27*** 1.36*** 1.25*** 1.26*** 1.52*** 1.41*** (0.06) (0.07) (0.12) (0.13) (0.08) (0.08) (0.14) (0.15)

SMB 0.97*** 0.97*** 1.10*** 1.06*** 0.90*** 0.90*** 1.06*** 1.12*** (0.08) (0.08) (0.14) (0.14) (0.09) (0.10) (0.17) (0.17)

HML -0.52*** -0.52*** -0.36** -0.30* -0.57*** -0.56* -0.54*** -0.62*** (0.09) (0.09) (0.17) (0.17) (0.11) (0.11) (0.20) (0.20)

MOM - -0.01 - 0.21** - 0.02 - -0.26** (0.05) (0.10) (0.07) (0.11)

Adjusted R2 0.89 0.89 0.67 0.69 0.83 0.83 0.68 0.69

70 Table 5--Continued (1) (2) (3) All Issuers High Change in IO Moderate Change in IO Low Change in IO a b 1a 1b 2a 2b 3a 3b

Panel B: Pre-Regulation FD (1997-1999)

0.24 0.71 2.33** 1.83 -0.25 0.24 -1.20 0.46 Alpha (0.62) (0.66) (1.07) (1.16) (0.58) (0.62) (1.49) (1.51)

1.13*** 1.10*** 1.33*** 1.39*** 1.12*** 1.08*** 1.41*** 1.20*** MKT (0.15) (0.15) (0.27) (0.28) (0.14) (0.14) (0.38) (0.36)

0.74*** 0.80*** 0.90*** 0.87*** 0.74*** 0.80*** 0.65* 0.76** SMB (0.14) (0.14) (0.25) (0.25) (0.13) (0.13) (0.35) (0.33)

-0.65*** -0.81*** -0.68* -0.45 -0.57*** -0.74*** -0.67 -1.44** HML (0.20) (0.22) (0.37) (0.43) (0.19) (0.20) (0.52) (0.56)

-0.25* 0.28 -0.26* -0.93*** MOM - - - - (0.14) (0.26) (0.13) (0.34)

Adjusted R2 0.87 0.88 0.76 0.76 0.88 0.89 0.58 0.64

71 Table 5--Continued (1) (2) (3) All Issuers High Change in IO Moderate Change in IO Low Change in IO a b 1a 1b 2a 2b 3a 3b

Panel C: Post-Regulation FD (2001-2007)

-0.58 -0.66* -0.74 -0.78 -0.06 -0.18 -2.17** -2.15** Alpha (0.36) (0.36) (0.62) (0.63) (0.49) (0.48) (0.84) (0.85)

1.22*** 1.32*** 1.07*** 1.11*** 1.20*** 1.36*** 1.49*** 1.46*** MKT (0.08) (0.10) (0.14) (0.17) (0.11) (0.13) (0.20) (0.23)

0.95*** 0.95*** 1.18*** 1.18*** 0.83*** 0.82*** 0.94*** 0.94*** SMB (0.14) (0.14) (0.23) (0.24) (0.18) (0.18) (0.32) (0.33)

-0.14 -0.19 0.54** 0.51* -0.38** -0.47** 0.00 0.02 HML (0.13) (0.13) (0.26) (0.26) (0.18) (0.18) (0.36) (0.36)

0.17* 0.08 0.26** -0.05 MOM - - - - (0.09) (0.16) (0.12) (0.21)

Adjusted R2 0.81 0.81 0.55 0.55 0.69 0.70 0.49 0.48

72 Table 5--Continued

Panel D: Tests of Difference of Alphas Three-Factor Model Four-Factor Model

Full Sample Period High ∆IO – Low ∆IO alpha 1.86 1.36 t-statistic 2.59** 1.94* (p-value) (0.0106) (0.0544) Pre-Reg FD (1997-1999) High ∆IO – Low ∆IO alpha 3.49 1.33 t-statistic 2.04** 0.79 (p-value) (0.0472) (0.4320) Post-Reg FD (2001-2007) High ∆IO – Low ∆IO alpha 1.48 1.42 t-statistic 1.54 1.47 (p-value) (0.1268) (0.1460)

Pre-Reg FD vs. Post-Reg FD Post Coefficient (High ∆IO) -2.80 -2.42 t-statistic -2.36** -2.08** (p-value) (0.0197) (0.0399) Post Coefficient (Low ∆IO) -0.74 -0.99 t-statistic -0.48 -0.63 (p-value) (0.6327) (0.5278)

73 Table 6. Regressions for Changes in Institutional Ownership from Pre- to Post-Reg FD This table presents results from a linear regression regression model of the dependent variable, the buy-and-hold abnormal return, and the independent variables: Post (dummy for post-Reg FD issuances), High (dummy for high change in IO), and Post*High (interaction term for high change in IO and post-Reg FD). Panels A, B, and C, report results for the size-and-book-to-market equity, the size-and-industry, and the size-and-run-up matching techniques, respectively. The t-statistic reported is adjusted using the Satterthwaite method . ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.

Independent Coefficient Standard t-statistic p-value Variable Estimate Error

Panel A: Dependent Variable: Size-and-Book-to-Market Equity BHAR

Intercept 0.08 0.06 1.38 0.17 Post -0.09 0.08 -1.05 0.29 High 0.41 0.14 2.99*** 0.003 Post*High -0.52 0.18 -2.87*** 0.004

Panel B: Dependent Variable: Size-and-Industry BHAR

Intercept 0.11 0.05 2.15 0.03 Post -0.18 0.08 -2.26** 0.02 High 0.34 0.13 2.56** 0.01 Post*High -0.36 0.17 -2.06** 0.04

Panel C: Dependent Variable: Size-and-Run-up BHAR

Intercept 0.003 0.05 0.05 0.96 Post -0.07 0.08 -0.84 0.40 High 0.58 0.13 4.56*** <0.0001 Post*High -0.76 0.18 -4.33*** <0.0001

74 Table 7. Institutional Ownership (IO) Patterns in Seasoned Equity Offerings (Full Sample Period) This table presents the mean (median) level of shares, as a percentage of shares outstanding from CRSP, institutional investors hold of the sample firms. In Panel A, the institutional ownership levels are presented beginning two calendar quarters before the issuing quarter (quarter 0) and ending four calendar quarters after the issuance. Panel B presents the changes in institutional ownership levels between quarters over the same time horizon. Data is presented for the entire sample of SEO firms, and by intended use of proceeds classifications: investment (INV), recapitalization (RECAP), and general corporate purposes (GCP). T-statistics for means, z-statistics for medians, and p-values are reported to test if the difference in means or medians is zero in Panel B. The t-statistic reported is adjusted using the Satterthwaite method and the z-statistic is for the Wilcoxon rank sum test. ***, **, * denote significance at the 1%, 5%, and 10% levels, respectively.

Quarter

-2 -1 0 1 2 3 4 Panel A: Levels of IO (1997-2007)

All SEO Firms 36.74% 37.83% 50.35% 50.40% 50.72% 50.44% 50.60% ( N = 1049) (33.63) (35.01) (52.65) (53.24) (53.26) (52.18) (53.16)

INV 33.03 33.90 46.51 46.07 46.55 46.59 46.84 (N=315) (29.70) (30.79) (48.89) (47.89) (48.90) (48.28) (48.51)

RECAP 38.09 38.96 52.64 52.43 52.91 52.26 52.59 (N=317) (37.31) (35.29) (55.17) (55.17) (55.46) (54.30) (54.83)

GCP 38.51 39.94 51.51 52.14 52.20 51.97 51.93 (N=417) (34.45) (37.68) (53.91) (55.48) (57.34) (53.96) (55.16)

75 Table 7--Continued

Between Quarters

-2 to -1 -1 to 0 0 to 1 1 to 2 2 to 3 3 to 4 Panel B: Changes in IO (1997-2007)

All SEO Firms 1.09% 12.52% 0.05% 0.30% -0.28% 0.16% (0.90) (11.05) (0.00) (0.00) (0.00) (0.00)

INV 0.87 12.61 -0.44 0.48 0.04 0.26 (0.58) (10.65) (0.00) (0.00) (0.00) (0.00)

RECAP 0.87 13.68 -0.21 0.47 -0.65 0.33 (0.51) (11.16) (0.00) (0.38) (0.00) (0.00)

GCP 1.43 11.57 0.63 0.06 -0.23 -0.04 (1.37) (11.24) (0.10) (0.00) (0.00) (0.00)

INV – RECAP t = 0.00 t = -0.80 t = -0.23 t = 0.01 t = 0.68 t = -0.08 z = 0.14 z = -0.63 z = 0.19 z = -1.32 z = 0.89 z= -0.25

76 Table 8. Institutional Ownership (IO) Patterns in Seasoned Equity Offerings (Pre-Regulation Fair Disclosure) This table presents the mean level of shares, as a percentage of shares outstanding from CRSP, institutional investors hold of the sample firms for the pre-Reg FD period (1997-1999). In Panel A, the institutional ownership levels are presented beginning two calendar quarters before the issuing quarter (quarter 0) and ending four calendar quarters after the issuance. Panel B presents the changes in institutional ownership levels between quarters over the same time horizon. Data is presented for the entire sample of SEO firms, and by intended use of proceeds classifications:investment (INV), recapitalization (RECAP), and general corporate purposes (GCP). T-statistics for means, z-statistics for medians, and p-values are reported to test if the difference in means or medians is zero in Panel B. The t-statistic reported is adjusted using the Satterthwaite method and the z-statistic is for the Wilcoxon rank sum test. ***, **, * denote significance at the 1%, 5%, and 10% levels, respectively.

Quarter

-2 -1 0 1 2 3 4 Panel A: Levels of IO (1997-1999)

All SEO Firms 31.71% 32.65% 45.11% 44.07% 43.93% 42.29% 41.42% ( N =448) (28.94) (30.05) (48.88) (47.56) (45.33) (43.81) (42.60)

INV 29.58 31.48 43.85 42.65 42.11 41.94 40.81 (N=153) (28.14) (29.53) (47.53) (44.75) (44.82) (43.57) (41.59)

RECAP 31.06 31.96 45.28 44.29 44.88 41.84 41.78 (N=152) (27.97) (28.68) (51.15) (48.73) (46.39) (43.79) (43.12)

GCP 34.67 34.65 46.29 45.37 44.87 43.15 41.68 (N=143) (31.78) (32.68) (47.85) (48.14) (47.16) (46.53) (44.10)

77 Table 8--Continued

Between Quarters

-2 to -1 -1 to 0 0 to 1 1 to 2 2 to 3 3 to 4 Panel B: Changes in IO (1997-1999)

All SEO Firms 0.95% 12.46% -1.04% -0.14% -1.64% -0.87% (0.12) (11.74) (0.00) (0.00) (0.00) (0.00)

INV 1.90 12.37 -1.20 -0.54 -0.17 -1.12 (0.00) (10.68) (0.00) (0.00) (0.00) (0.00)

RECAP 0.91 13.31 -0.99 0.59 -3.04 -0.06 (0.10) (12.93) (0.00) (0.00) (0.00) (0.00)

GCP -0.03 11.65 -0.93 -0.50 -1.72 -1.47 (0.71) (10.86) (0.00) (0.00) (0.00) (0.00)

INV – RECAP t = 0.51 t = -0.50 t = -0.13 t = -0.84 t = 1.70* t = -0.70 z = -0.21 z = 0.48 z = 0.02 z = 1.29 z = -1.32 z = 1.02

78 Table 9. Institutional Ownership (IO) Patterns in Seasoned Equity Offerings (Post-Regulation Fair Disclosure) This table presents the mean level of shares, as a percentage of shares outstanding from CRSP, institutional investors hold of the sample firms for the pre-Reg FD period (2001-2007). In Panel A, the institutional ownership levels are presented beginning two calendar quarters before the issuing quarter (quarter 0) and ending four calendar quarters after the issuance. Panel B presents the changes in institutional ownership levels between quarters over the same time horizon. Data is presented for the entire sample of SEO firms, and by intended use of proceeds classifications: investment (INV), recapitalization (RECAP), and general corporate purposes (GCP). T-statistics for means, z-statistics for medians, and p-values are reported to test if the difference in means or medians is zero in Panel B. The t-statistic reported is adjusted using the Satterthwaite method and the z-statistic is for the Wilcoxon rank sum test. ***, **, * denote significance at the 1%, 5%, and 10% levels, respectively.

Quarter

-2 -1 0 1 2 3 4 Panel A: Levels of IO (2001-2007)

All SEO Firms 42.99% 43.70% 57.40% 58.42% 59.46% 61.12% 62.08% ( N = 434) (41.46) (41.46) (58.94) (61.19) (62.14) (64.93) (64.39)

INV 39.40 38.63 52.75 52.86 55.07 57.29 58.81 (N=99) (37.76) (36.36) (54.19) (57.48) (58.60) (60.07) (61.21)

RECAP 44.98 45.34 59.94 60.92 61.37 62.96 63.92 (N=142) (43.57) (43.30) (65.32) (65.85) (64.47) (66.33) (68.93)

GCP 43.37 45.09 57.91 59.43 60.30 61.72 62.40 (N=193) (41.61) (44.63) (58.87) (61.32) (62.99) (65.32) (64.12)

79 Table 9--Continued

Between Quarters

-2 to -1 -1 to 0 0 to 1 1 to 2 2 to 3 3 to 4 Panel B: Changes in IO (2001-2007)

All SEO Firms 0.71% 13.70% 1.02% 1.04% 1.66% 0.96% (1.32) (12.39) (0.26) (0.77) (0.25) (0.17)

INV -0.77 14.12 0.12 2.21 2.22 1.52 (2.27) (12.86) (1.26) (0.32) (1.01) (0.08)

RECAP 0.36 14.60 0.97 0.46 1.59 0.96 (0.53) (11.07) (0.00) (0.72) (0.00) (0.00)

GCP 1.72 12.82 1.51 0.87 1.42 0.68 (1.59) (13.99) (0.23) (1.16) (0.19) (0.68)

INV – RECAP t = -0.43 t = -0.62 t = -0.20 t = 0.94 t = -1.16 t = 0.95 z = 0.12 z = -0.50 z = 0.26 z = -0.49 z = 0.13 z = 0.69

80 Table 10. Intended Use of Proceeds of SEOs and Buy-and-Hold Abnormal Returns This table presents the mean change in institutional holdings levels and the buy-and-hold abnormal returns for sample firms matched to control firms based on market capitalization and: (1) book-to-market ratio, (2) industry classification, and (3) six-month compound returns leading up to the equity issuance. The buy-and- hold abnormal returns reported are the one-year post-issuance buy-and-hold returns of the issuing, sample firm minus the buy-and- hold return of the control firm over the same time period. T-statistics for means, z-statistics for medians, and p- values are reported to test if the difference in means or medians is zero. The t-statistic reported is adjusted using the Satterthwaite method and the z-statistic is for the Wilcoxon rank sum test. ***, **, * denote significance at the 1%, 5%, and 10% levels, respectively.

Size-and-Book- Size-and- Size-and- to-Market Industry Run-up BHARs (1) BHARs (2) BHARs (3)

Mean Median Mean Median Mean Median Panel A: Full Sample Period (1997-2007)

INV 5.94% 3.49% -0.36% -4.84% 4.56% -2.45%

RECAP -1.68 -0.58 -2.00 -2.12 -4.70 -10.25

GCP 8.84 3.11 2.00 -9.49 1.58 -7.21

INV - RECAP 7.62% 4.07% 1.64% -2.72% 9.26% 7.80% Test Statistic 0.99 0.79 0.22 -0.29 1.27 1.33 (p-value) (0.3234) (0.4280) (0.8235) (0.7755) (0.2042) (0.1838)

Mean Median Mean Median Mean Median Panel B: Pre-Reg FD Period (1997-1999)

INV 3.84% -2.82% 4.50% -1.16% 3.32% -1.50%

RECAP 7.12 2.58 9.09 0.27 4.22 -5.01

GCP 32.81 15.13 39.04 17.20 25.06 1.81

INV - RECAP -3.28% -5.40% -4.59% -1.43% -0.90% 3.51% Test Statistic -0.24 -0.78 -0.36 -0.39 -0.07 0.08 (p-value) (0.8074) (0.4376) (0.7162) (0.6966) (0.9409) (0.9366)

81

Table 10--Continued Size-and-Book- Size-and- Size-and- to-Market Industry Run-up BHARs (1) BHARs (2) BHARs (3)

Mean Median Mean Median Mean Median Panel C: Post-Reg FD (2001-2007)

INV 8.17% 5.22% 3.09% -1.7% -0.53% -8.28%

RECAP -14.19 -12.54 -11.20 -8.62 -15.23 -13.60

GCP -2.25 -4.46 -8.58 -11.73 -11.07 -11.51

INV - RECAP 22.36% 17.76% 14.29% 6.92% 14.70% 5.32% Test Statistic 2.30** 2.01** 1.50 0.94 1.57 0.96 (p-value) (0.0230) (0.0442) (0.1370) (0.3497) (0.1189) (0.3383)

82 Table 11. Calendar-Time Portfolio Regressions by the Intended Use of Proceeds of SEOs This table presents monthly estimates from regressing calendar-time portfolio (equally-weighted) monthly returns on the Fama-French (1993) three factors (MKT, SMB, HML) and the four-factor model that also includes Carhart’s (1997) momentum factor (MOM). The four factors are obtained from Ken French’s website. Panel A reports the coefficient estimates using a one-year period beginning the month after issuance for the full sample period (1997-2007). Panel B uses the same time horizon, however, it includes only those SEOs conducted during the pre-Regulation FD time period (1997-1999). Panel C presents the results for the post-Regulation FD time period. In each panel, the first set of columns (a and b) display estimates for all issuers, and the subsequent sets of columns (1a and 1b, 2a and 2b, and 3a and 3b) display coefficients for the three categories of firms based on their stated intended use of proceeds: (1) firms where investment is the stated use of proceeds, (2) firms where recapitalization is the stated use of proceeds, and (3) firms where general corporate purposes is the stated use of proceeds. Panel D presents the alphas from a long (investment) and short (recapitalization) strategy. Standard errors are in parentheses. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.

(1) (2) (3) All Issuers Investment Recapitalization General Corporate Purposes a b 1a 1b 2a 2b 3a 3b

Panel A: Full Sample Period (1997-2007)

-0.15 -0.15 0.53 0.62 -0.80** -0.92** 0.05 0.07 Alpha (0.28) (0.28) (0.54) (0.55) (0.39) (0.39) (0.34) (0.35)

1.30*** 1.30*** 1.16*** 1.12*** 1.37*** 1.42*** 1.41*** 1.40*** MKT (0.06) (0.07) (0.12) (0.13) (0.09) (0.09) (0.08) (0.08)

0.97*** 0.97*** 1.05*** 1.07*** 0.89*** 0.87*** 0.88*** 0.88*** SMB (0.08) (0.08) (0.15) (0.15) (0.11) (0.11) (0.09) (0.09)

-0.52*** -0.52*** -0.65*** -0.68*** 0.22 0.25* -0.81*** -0.81*** HML (0.09) (0.09) (0.18) (0.18) (0.13) (0.13) (0.11) (0.11)

-0.01 -0.09 0.12 -0.02 MOM - - - - (0.05) (0.10) (0.07) (0.06)

Adjusted R2 0.89 0.89 0.68 0.68 0.75 0.75 0.87 0.87

83 Table 11--Continued (1) (2) (3) All Issuers Investment Recapitalization General Corporate Purposes a b 1a 1b 2a 2b 3a 3b

Panel B: Pre-Regulation FD (1997-1999)

0.24 0.71 -0.19 0.01 0.64 0.85 1.10 1.50* Alpha (0.62) (0.66) (0.76) (0.84) (0.75) (0.83) (0.72) (0.78)

1.13*** 1.10*** 1.25*** 1.23*** 1.36*** 1.34*** 1.11*** 1.08*** MKT (0.15) (0.15) (0.19) (0.20) (0.19) (0.20) (0.18) (0.18)

0.74*** 0.80*** 1.05*** 1.07*** 0.95*** 0.97*** 0.55*** 0.59*** SMB (0.14) (0.14) (0.18) (0.18) (0.18) (0.18) (0.16) (0.17)

-0.65*** -0.81*** -0.26 -0.35 0.20 0.10 -1.03*** -1.16*** HML (0.20) (0.22) (0.27) (0.31) (0.26) (0.31) (0.23) (0.26)

-0.25* -0.11 -0.12 -0.21 MOM - - - - (0.14) (0.19) (0.18) (0.16)

Adjusted R2 0.87 0.88 0.83 0.82 0.77 0.76 0.86 0.86

84 Table 11--Continued (1) (2) (3) All Issuers Investment Recapitalization General Corporate Purposes a b 1a 1b 2a 2b 3a 3b

Panel C: Post-Regulation FD (2001-2007)

-0.58 -0.66* 0.44 0.35 -1.53*** -1.67*** -0.57 -0.58 Alpha (0.36) (0.36) (0.87) (0.87) (0.51) (0.50) (0.45) (0.45)

1.22*** 1.32*** 1.30*** 1.42*** 1.27*** 1.44*** 1.29*** 1.31*** MKT (0.08) (0.10) (0.20) (0.24) (0.12) (0.14) (0.10) (0.12)

0.95*** 0.95*** 0.67** 0.69** 0.88*** 0.86*** 1.03*** 1.03*** SMB (0.14) (0.14) (0.33) (0.33) (0.19) (0.19) (0.17) (0.17)

-0.14 -0.19 -0.85** -0.91** 0.61*** 0.51** -0.25 -0.25 HML (0.13) (0.13) (0.36) (0.36) (0.22) (0.22) (0.16) (0.17)

0.17* 0.20 0.31** 0.02 MOM - - - - (0.09) (0.22) (0.13) (0.11)

Adjusted R2 0.81 0.81 0.44 0.44 0.65 0.67 0.76 0.76

85 Table 11--Continued

Panel D: Tests of Difference of Alphas Three-Factor Model Four-Factor Model

Full Sample Period Investment – Recapitalization alpha 0.82 0.99 t-statistic 1.51 1.79* (p-value) (0.1344) (0.0753) Pre-Reg FD (1997-1999) Investment – Recapitalization alpha -0.87 -0.89 t-statistic -0.82 -0.75 (p-value) (0.4185) (0.4553) Post-Reg FD (2001-2007) Investment – Recapitalization alpha 1.23 1.25 t-statistic 1.34 1.34 (p-value) (0.1836) (0.1838)

86 Table 12. Regressions for Change in IO: Investment and Recapitalization (Pre- vs. Post-Reg FD) This table presents results from a regression of the dependent variable (change in institutional ownership from quarters -1 to +1) and the independent variables: Post (dummy for post-Reg FD issuances), INV (dummy for the investment classification), and Post*Inv (interaction term for investment and post-Reg FD). Panel A reports results for Model 1 which includes just the INV indicator variable. Panels B (model 2) and C (Model 3), include the Post and Post*INV variables, respectively. The t-statistic reported is adjusted using the Satterthwaite method . ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.

Independent Coefficient Standard T-statistic p-value Variable Estimate Error

Panel A: Dependent Variable: Change in IO (Model 1)

Intercept 0.14 0.01 14.52 <0.0001 INV -0.02 0.01 -1.08 0.28

Panel B: Dependent Variable: Change in IO (Model 2)

Intercept 0.12 0.01 10.57 <0.0001 INV -0.01 0.01 -0.89 0.37 Post 0.03 0.01 2.18** 0.03

Panel C: Dependent Variable: Change in IO (Model 3)

Intercept 0.12 0.01 9.28 <0.0001 INV -0.01 0.02 -0.58 0.56 Post 0.03 0.02 1.70* 0.09 Post*INV -0.004 0.03 -0.13 0.89

87 Table 13. Calendar-Time Portfolio Regressions by Institutional Ownership and the Intended Use of Proceeds of SEOs This table presents monthly estimates from regressing calendar-time portfolio (equally-weighted) monthly returns on the Fama-French (1993) three factors (MKT, SMB, HML) and the four-factor model that also includes Carhart’s (1997) momentum factor (MOM). The four factors are obtained from Ken French’s website. Panel A reports the intercept (alphas) coefficient estimates using a one-year period beginning the month after issuance for the three-factor model. Panel B reports alphas for the four-factor model. Both panels present alphas for the three intended use of proceeds classifications: (1) investment, (2) recapitalization, and (3) general corporate purposes. The sample is also segmented by time periods (pre-Reg FD, 1997-1999, and post-Reg FD, 2001-2007) and by the following classifications of changes in institutional ownership around the offering.: (1) firms belonging to the highest quintile of change in IO around the SEO (quarters -1 to +1), (2) firms belonging to the middle three quintiles of change in IO, and (3) firms belonging to the lowest quintile of change in IO. Standard errors are in parentheses. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.

(1) (2) (3) Investment Recapitalization General Corporate Puposes Changes in Pre-Reg FD Post- Reg FD Pre-Reg FD Post- Reg FD Pre-Reg FD Post- Reg FD Institutional Ownership

Panel A: Three-Factor Model Intercept Coefficients (1) High Change in IO 1.09 -0.50 3.60** -0.43 4.11*** -1.94** (1.49) (1.16) (1.76) (0.81) (1.49) (0.88) (2) Moderate Change in IO -0.51 0.96 -0.17 -1.06* 0.45 0.02 (0.83) (1.07) (0.80) (0.58) (0.71) (0.54) (3) Low Change in IO -0.62 0.13 0.17 -2.89** -1.19 -2.18 (2.18) (1.06) (1.38) (1.33) (1.76) (1.21) Panel B: Four-Factor Model Intercept Coefficients (1) High Change in IO 1.18 -0.64 2.76 -0.45 3.19* -2.07** (1.64) (1.17) (1.92) (0.82) (1.60) (0.89) (2) Moderate Change in IO -0.57 0.82 0.26 -1.25** 0.97 -0.03 (0.91) (1.08) (0.87) (0.55) (0.76) (0.54) (3) Low Change in IO 1.50 0.29 0.31 -3.05** -0.12 -1.88 (2.15) (1.10) (1.41) (1.33) (1.87) (1.21)

88

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BIOGRAPHICAL SKETCH

David E. Bray was born in Fort Myers, Florida and he lived there until beginning his studies at the University of Miami in Coral Gables, Florida. Initially, he thought he would be a Hurricane his entire life; however, life has its own path. Upon graduating with a Bachelor’s (2002) and a Master’s (2003) degree from the Florida State University in Tallahassee, Florida, both with a Finance concentration, he embarked on a corporate career. In addition, his soul transitioned from being a Hurricane to a Seminole (GO NOLES!!). A brief stint in “Cubicle-Land” was all that was needed to encourage David to return to FSU and begin work on his Doctorate degree with a specialization in Finance. After a longer- than-average tenure in the doctoral program at FSU, David is beginning his academic position as an Assistant Professor of Finance at Kennesaw State University in the Fall 2010 semester. The University is located in the metropolitan area of Atlanta, Georgia.

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