The impact of cost of equity on seasoned equity offerings

THE IMPACT OF COST OF EQUITY ON SEASONED EQUITY OFFERINGS ZHANG WEIQI (B.Comp. (Hons.), National University of Singapore) A THESIS SUBMITTED FOR THE DEGREE OF DOCTOR OF PHILOSOPHY DEPARTMENT OF FINANCE NATIONAL UNIVERSITY OF SINGAPORE 2012 ACKNOWLEDGEMENTS I would like to express my deepest and sincere gratitude to my supervisor, Professor Duan Jin-Chuan, for the continuous guidance and encouragements during the past few years of my Ph.D. study. He introduced me to the area of finance research, and his enthusiasm, inspiration and tremendous support has always been my guiding light in research, especially when I encounter obstacles. I benefit from him far beyond this thesis. I am very grateful to my thesis committee members, Professor Anand Srinivasan and Dr. Emir Hrnjić. This thesis would not have been possible without their help. The constructive comments and insightful feedback from them inspired my thinking and greatly improved this thesis. It gives me a great pleasure to acknowledge Professor Ravi Jagannathan, for his guidance during my visit to Kellogg School of Management. The invaluable research exposure I obtained from Kellogg would not be possible without his kind support. I am indebted to many of my colleagues from National University of Singapore Business School and Kellogg School of Management. We had both insightful discussions in school and joyful moments outside of school. The discussions often helped me re-focus my efforts, and the companionships supported me throughout the time in research. I would like to thank the finance department office and Ph.D. program office for their generous support, especially Callie Toh, T I Fang, Kristy Swee, Lim Cheow Loo, and Hamidah Bte Rabu. Their help greatly eased my research process. Last but not least, I owe my deepest gratitude to my parents and my fiancé. For all these years, my faith in research is inseparable from their understanding, encouragement, and unconditional support. This thesis is dedicated to them. i TABLE OF CONTENTS Acknowledgement i Summary . iii List of Tables iv List of Figures . v Chapters 1. Introduction . 2. Literature Review . 3. Data and Methodology 12 3.1 Seasoned equity offerings sample 12 3.2 The forward-looking risk premium 13 4. Seasoned Equity Offering and Cost of Equity 16 4.1 Aggregate SEO issuance and cost of equity . 16 4.2 Firm’s likelihood of issuance and cost of equity 20 4.3 SEO proceeds and the cost of equity 26 4.4 SEO announcement effect and cost of equity . 27 4.5 The long run post-SEO effect and cost of equity 31 4.6 Robustness 33 5. Why Do Firms Issue When Cost of Equity Is High? . 35 5.1 The distress likelihood and SEO issuance likelihood . 35 5.2 The distress likelihood and SEO announcement 38 5.3 Post-SEO change of debt 39 6. Conclusion 42 Bibliography . 44 Appendix . 48 ii SUMMARY This thesis studies the impact of forward-looking cost of equity on firms’ Seasoned Equity Offerings decisions, the announcement effect and long run post-SEO returns. As the net present value of investment projects is negatively related to the timevarying cost of equity, the decision to raise capital for investment opportunities is more likely when the cost of equity is low. Using a new measure of forward-looking risk premium, I document that the market-wide SEO issuances, firm-level SEO likelihood and the proceeds from SEO are all greater when the forward-looking cost of equity is low. Small firms’ issuance decisions are particularly sensitive to the fluctuation of forward-looking cost of equity, suggesting that the impact from cost of equity is greater for firms with tighter financial constraints. Moreover, firms that carry out SEOs at higher forward-looking costs of equity have more negative announcement returns, which are followed by lower long run post-SEO returns. I propose a distress based explanation for the observed negative abnormal announcement and long run returns. I also document empirical findings that are consistent with the distress based explanation. Specifically, firms with higher default probabilities and negative net income are more likely to issue SEOs at higher costs of equity. Firms with higher default probabilities also receive more negative announcement returns when the announcement of a SEO is made at a higher cost of equity. Furthermore, firms issuing SEOs at higher costs of equity engage in more debt reduction one year after the SEO issuances. iii LIST OF TABLE Table Summary Statistics for Seasoned Equity Offerings 52 Table SEO Intensity and Market Cost of Equity 53 Table Logistic Regression of SEO Issuance . 54 Table SEO Proceeds and Cost of Equity 57 Table Abnormal Returns of Seasoned Equity Offering Announcements . 58 Table Regression Estimates for Announcement Period Stock Returns 59 Table Abnormal Return of Portfolio Formed by years Post-issuance Return . 60 Table SEO Issuance Choice and Distress Likelihood 61 Table SEO Announcement Returns and Distress Likelihood . 63 Table 10 Post-SEO Change of Debt 64 iv LIST OF FIGURES Figure Number of SEOs . 65 Figure The Forward-looking Market Risk Premium 66 v CHAPTER 1. INTRODUCTION There is a large body of literature on the determinants of seasoned equity offerings (SEO) by publicly traded firms. One common reason for a firm to issue SEOs is to raise capital for capital expenditures and investment projects (Masulis and Korwar 1986; Eckbo, Masulis, and Norli 2007). Another prominent reason advocated by Graham and Harvey (2001) and others is that managers time the market to take advantage of over-valuation of their publicly traded securities. Evidence for this reason is provided in literature: the clustering of equity issues together (Bayless and Chaplinsky 1996), the negative market reaction at SEO announcement time (Asquith and Mullins 1986; Masulis and Korwar 1986), and the long run post-SEO underperformance (Loughran and Ritter 1995; Spiess and Affleck-Graves 1995). In addition, other papers such as Pastor and Veronesi (2005) and Li, Livdan, and Zhang (2009) provide rational reasons for clustering of equity issuances in terms of time varying expected returns. Regardless of whether the reason is investment or market timing, prior literature suggests expected cost of equity plays an important role in seasoned equity offering activities. However, expected return is quite difficult to estimate. In asset pricing, the most common way to estimate expected return is to use historical average of realized returns. This historical approach is backward-looking. The decisions to issue SEOs for future investments should be affected by the forward-looking cost of equity capital, not the historical cost. One approach to derive a forward-looking cost of equity is to use analyst forecast data and fit into an earning or dividend discount model to obtain an implied cost of equity (e.g. Gebhardt, Lee, and Swaminathan 2001; Gordon and Gordon 1997). However, the estimated cost of equity using this approach is sensitive to the model used and the predictive power of analyst forecast data. Further, to the extent that analysts have biases (Easton and Sommer 2007), this approach may lead to large errors in the forward-looking cost of equity capital. These errors may be compounded by the fact that analyst coverage correlates with firms’ issuance decisions (Chang, Dasgupta, and Hilary 2006). This paper uses an alternative forward-looking measure for the cost of equity, based on the work of Duan and Zhang (2011). Given that this measure relies solely on market data, it does not suffer from biases as the implied cost of equity measures based on analyst forecasts. Specifically, the methodology developed in Duan and Zhang (2011) derives a closed form formula for the forward-looking market risk premium under the assumption of a particular form of stochastic discount factor. The forward-looking market risk premium is expressed as a function of investors’ risk aversion and forward-looking return volatility, skewness, and kurtosis. Using the above, one can also compute a firm specific forward-looking risk premium that is simply the product of the market forward-looking risk premium and firm beta. First, this paper examines the impact of market forward-looking risk premium (henceforth, MFLRP) on aggregate fraction of SEO issuances, defined as the number of SEOs in a given month divided by the number of traded firms at the end of the previous month (in thousands). Using data from 1970 to 2009, the fraction of SEO issuances is strongly negatively related with the month-end forward-looking market risk premium. An increase in the MFLRP by 1% reduces the SEO issuance fraction by about 1%. These results include controls for well-known variables that may influence equity offering decisions, such as market timing and other market-specific variables. The results are consistent with traditional theories that imply a lower expected cost of equity increase the number of seasoned equity offerings. Next, I conduct a similar test at the firm level. Using a panel data sample, I examine if the likelihood of a firm issuing an SEO in a given month is related to its firm-specific forward-looking risk premium (henceforth, FFLRP), which is defined as the product of beta and the MFLRP. Consistent with the market results, the likelihood of firm issuing SEO is higher when the firms’ forward-looking risk premium is low. In addition, firms raise a larger amount of capital from SEOs when their forward-looking risk premium is low. The sensitivity of firms’ SEO issuances to their forward-looking risk premium also varies with firm characteristics. Firms with smaller size are even more likely to issue SEOs when their forward-looking risk premium is low. The results suggest that the issuance decisions for small firms with tighter financial constraints are more sensitive to the variations in the cost of equity. Furthermore, I examine the implications of the cost of equity on the SEO announcement returns and the long run post-SEO returns. Prior studies document negative SEO announcement returns (Asquith and Mullins 1986, Marsulis, and Korwar 1986) and long run post-SEO underperformance (Spiess and Affleck-Graves 1995, Loughran and Ritter 1995). In this study, I explore how these returns relate to firms’ forward-looking cost of equity during SEOs. Firms announcing their seasoned equity offerings at a higher cost of equity should receive a more negative market reaction, consistent with pecking order theory models of capital structure and costly external financing. I find that this is indeed the case. The difference in two days abnormal announcement return for firms issuing at top 30% of FFLRP and bottom 30% of FFLRP is -0.71% and statistically significant. This finding is also consistent with Jung et al. (1996), who documents that firms without valuable growth opportunities experience a more negative stock price reaction to equity issues than firms with better investment opportunities. Next, I perform a calendar-time regression test for the long run post-SEO abnormal returns. I find that the long run abnormal post-SEO negative returns are more pronounced to firms issuing at high cost of equity. No abnormal long run returns are identified for firms issuing at low cost of equity. While market timing theory interprets the long run post-issuance underperformance as a correction from the initial market over-valuation (Ritter 1991; Loughran and Ritter, 1995, 1997; Spiess and Affleck-Graves, 1995; Baker and Wurgler 2002), my results are inconsistent with market timing theory. In particular, market timing theory implies that a more pronounced post-issuance underperformance should prevail when the firms time the market, which is usually associated with a higher stock price and lower cost of equity. I further investigate the reason why firms issue SEOs when their cost of equity is high. Inspired by DeAngelo, DeAngelo, and Stulz (2010)’s findings that an important motive for firms’ issuance decision is to “meet a near-term cash need”, I propose a distress based explanation. Firms usually have unclear investment objectives when their cost of equity is high, so their motives for offering equities are likely to be driven by urgent cash needs such as debt repayment. As such, firms that issue SEOs APPENDIX B B.1 The estimation of probability of default The default probability is estimated from the structural approach of Vassalou and Xing (2004). Specifically, the distance to default is estimated as: ln , √ Using the normal distribution implied by Merton’s model, the probability of default is given by: ln , √ Where , is the firm’s asset value at time t, with drift and volatility . . denotes the book value of debt at time t. Following the conventions in the literature, the forward-looking period T is set to one year, and book debt is computed as short term plus half long-term book debt. T-bill rate is used as risk free rate. The initial asset value for each trading day is computed as market value of equity plus book debt. An iterative procedure that is similar to Vassalou and Xing (2004) is used to calculate each month-end. The drift and back out for each firm at is calculated from the mean of change in ln . . Default probabilities are obtained for each firm-month. 50 Table 1: Summary Statistics for Seasoned Equity Offerings This table presents the summary statistics of the SEO sample from 1970 to 2009. The SEO data are from SDC and only include the firms with some primary shares offered. Only firms listed on NYSE, AMEX, and NASDAQ are included. Utility and financial firms are excluded from the sample. The # of SEOs presents the number of offerings for each decade and the whole sample. Total proceeds are the total value offered for these SEOs and represented in millions of dollars. The numbers of listed firms are obtained from CRSP database. Period  1970 ‐ 1979  1980 ‐ 1989  1990 ‐ 1999  2000 ‐ 2009  1970 ‐ 2009  Mean # of monthly SEOs 5.17 15.89 24.72 17.07 15.70 Mean proceeds (millions) 136.04 514.91 1951.15 2616.16 1326.09 Mean # of listed firms 4279 6105 7917 6972 6358 51 Table 2: SEO Intensity and Market Cost of Equity This table presents the relationship between SEO intensity and the market forward-looking risk premium from 1970 to 2009. The monthly SEO intensity is measured by the number of monthly SEO issues deflated by the total number of firms (in thousands) in the prior month. The MFLRPt-1(τ) measures the one month forward-looking market risk premium at the end of the prior month (t-1). See section 2.2 for details on the computation of this measure. GDPGrowtht and are the quarterly percentage change in GDP obtained from BEA. IPGrowtht is the percentage change in industrial production obtained from Federal Reserve System. P/E is the price to earnings ratio for S&P500 index, using 12 month moving earnings per share. M/B is the market-to-book ratio for S&P500 index obtained from Compustat. Sentiment index is constructed from University of Michigan index following Lemmon and Portniaguina (2006). Rt-1 is the past market return from S&P500 index. The dispersion of abnormal returns around earnings announcements at month t (EarnDispersion) equals the standard deviation of announcement abnormal returns across all firms in the past three months. Analyst dispersion in month t is the standard deviation of analyst earning forecasts for each company in the past three months, across companies that are in the last quarter of their fiscal year and have analyst forecasts listed on IBES. T-statistics are computed from robust standard errors. ***, **, * denote statistical significance at the 1%, 5%, and 10% levels, respectively. SEO Fraction  MFLRPt-1(τ)  ‐0.733 *** ‐0.659 *** ‐1.008 *** ‐0.885  ***  ‐0.670 ** ‐0.351 * ‐0.614 ** (‐4.65)  (‐4.00)  (‐3.65)  (‐4.62)  (‐2.55)  (‐1.82)  (‐2.20)  GDPGrowtht-1  0.0257  0.0554  **  ‐0.0143  0.0358  ‐0.00426  ‐0.00744  (0.87)  (2.13)  (‐0.40)  (1.18)  (‐0.12)  (‐0.16)  0.0422  0.0340  0.0474  0.0543  0.0647  0.0456  IPGrowtht-1  (1.20)  (1.05)  (1.14)  (1.41)  (1.57)  (1.18)  (P/E)t‐1  0.0517  ***  0.0377  ***  0.0509  ***  (8.49)  (4.99)  (7.55)  (M/B)t‐1  0.0647  0.0414  (0.70)  (0.44)  Rt‐1  6.637  ***  8.784  ***  4.038  **  (3.03)  (3.81)  (1.99)  Sentimentt‐1  ‐0.0111  ‐0.00559  ‐0.0152  (‐0.86)  (‐0.39)  (‐1.56)  0.151  Δ EarnDispersiont‐13 to t‐1  (0.23)  Δ AnalystDispersiont‐13 to t‐1  ‐14.17  ***  (‐3.13)  Const.  2.544  ***  2.453  ***  1.419  ***  2.693  ***  1.839  ***  2.553  ***  1.531  ***  (27.52)     (18.84)     (8.52)     (9.15)     (9.09)     (8.80)     (5.94)     Adj. R‐sq  1.10%  1.10%  15.20%  1.50%  12.70%  4.60%  24.20%  Nobs  480  480  479  383  381  381  308  52 Table 3: Logistic Regression of SEO Issuance This table presents the logistic regression results of monthly SEO issuance from 1970 to 2009. The dependent variable is firm-month SEO issuance. It is equal to if a specific firm-month issues SEO, and equals to otherwise. MFLRP is the one-month forward-looking market risk premium at the end of month t-1. See section 2.2 for details on the computation of this measure. FFLRP is the one-month firm forward-looking risk premium that is equal to the product of market forward-looking risk premium and firms beta. Size is defined as the natural logarithm of the firm's market value at the end of prior month. Log(M/B) is the natural logarithm of the most recent market-to-book ratio. Beta is individual firms' beta estimated from prior years returns. Firm age is equal to the number of years that the firms are listed in CRSP. Cash, research and development expenditure (RD), operating income before depreciation (OIBD) are obtained from the most recent quarterly reported and deflated by total assets. RD is set to zero if R&D expenditure is missing. RDD is a dummy variable that equals one if R&D is missing. Capex is the capital expenditures in the fiscal year prior to SEO. Rt-3,t-1 is the stock return for the prior three months. IPGrowth is the monthly growth rate of industrial production. All regressions include industry fix effect. Industry classification is based on FamaFrench 48 industries. Crisis period are defined as the month with extreme value of forward-looking risk premium. The z values under the coefficient are computed from the robust standard errors. ***, **, * denote statistical significance at the 1%, 5%, and 10% levels, respectively. 53 Panel A: main effects  SEO Issuance (=1) All Sample   (1)  FFLRPt-1(τ)  log(M/B)  Beta  Cash  Age  OIBD  Capex  RD  RDD  Rt‐4,t‐1  IPGrowtht‐1  Industry fixed effect  Log likelihood  Nobs  Nobs SEO  Pseudo R‐sq  Exclude Crisis Period  (3)    (4)    ‐0.168  ***  (‐3.56)  MFLRPt‐1(τ)  Size  (2) 0.191  (33.87)  0.717  (50.93)  0.131  (11.61)  ‐0.942  (‐12.99)  ‐0.0256  (‐18.46)  0.0613  (0.47)  1.294  (13.83)  1.890  (9.86)  0.243  (7.77)  0.352  (18.86)  0.0102  (1.65)     Yes  ‐40,503  2,059,484  6,526  8.14%  ***  ***  ***  ***  ***  ***  ***  ***  ***  *        ‐0.128 **  (‐2.17) ‐0.428 (‐5.63) 0.193 (34.14) 0.716 (50.76) 0.111 (10.50) ‐0.941 (‐12.96) ‐0.0257 (‐18.46) 0.0384 (0.30) 1.294 (13.74) 1.896 (9.77) 0.238 (7.62) 0.355 (18.84) 0.00962 (1.55)   Yes ‐40,488 2,059,484 6,526 8.17% *** *** *** *** *** *** *** *** *** ***   0.19 (33.68) 0.72 (51.07) 0.125 (10.60) ‐0.949 (‐13.03) ‐0.0258 (‐18.45) 0.0933 (0.71) 1.286 (13.67) 1.899 (9.90) 0.249 (7.94) 0.351 (18.81) 0.0122 (1.94)   Yes ‐40,246 2,033,642 6,495 8.13% ***  ***  ***  ***  ***  ***  ***  ***  ***  *       ‐0.537  (‐4.55)  0.192  (34.00)  0.719  (50.83)  0.11  (10.36)  ‐0.943  (‐12.96)  ‐0.0258  (‐18.48)  0.0667  (0.51)  1.287  (13.57)  1.908  (9.76)  0.241  (7.69)  0.355  (18.82)  0.011  (1.75)     Yes  *** *** *** *** *** *** *** *** *** *** *      ‐40,235  2,033,642  6,495  8.16%  54 Panel B: interaction effects  SEO Issuance (=1) All Sample (5)    FFLRPt-1(τ)  ‐0.924  ***  (‐4.75)  FFLRPt‐1(τ) × Size  0.0583  ***  (3.68)  FFLRPt‐1(τ) × log(M/B)  Size  log(M/B)  Beta  Cash  Age  OIBD  Capex  RD  RDD  Rt‐4,t‐1  IPGrowtht‐1  Industry fixed effect  Log likelihood  Nobs  Nobs SEO  Pseudo R‐sq  0.183  (29.44)  0.717  (50.92)  0.137  (12.31)  ‐0.944  (‐13.00)  ‐0.0256  (‐18.39)  0.0647  (0.50)  1.293  (13.80)  1.897  (9.78)  0.243  (7.77)  0.353  (18.91)  0.0105  (1.69)     Yes  ‐40,498  2,059,484  6,526  8.15%  ***  ***  ***  ***  ***  ***  ***  ***  ***  *      (6) Exclude Crisis Period  (7)   (8)    ‐0.138 **  (‐2.20) ‐0.876 ***  (‐3.75)   ‐0.0506  (‐0.58)  0.0587 ***  (3.40) ‐0.0250 (‐0.58) 0.191 (33.83) 0.721 (46.76) 0.131 (11.61) ‐0.942 (‐12.98) ‐0.0256 (‐18.45) 0.0633 (0.48) 1.294 (13.83) 1.891 (9.85) 0.243 (7.78) 0.352 (18.85) 0.0102 (1.65)   Yes ‐40,497 2,059,484 6,526 8.15% *** *** *** *** *** *** *** *** *** *    0.182 (29.27) 0.719 (51.06) 0.131 (10.71) ‐0.951 (‐13.05) ‐0.0256 (‐18.34) 0.0948 (0.72) 1.285 (13.65) 1.906 (9.83) 0.249 (7.95) 0.352 (18.85) 0.0123 (1.96)   Yes ‐40,244 2,033,642 6,495 8.14% ***  ***  ***  ***  ***  ***  ***  ***  ***  **       ‐0.0592  (‐1.08)  0.19  (33.64)  0.729  (46.18)  0.126  (10.71)  ‐0.948  (‐13.02)  ‐0.0257  (‐18.43)  0.0977  (0.74)  1.286  (13.67)  1.901  (9.87)  0.249  (7.95)  0.351  (18.76)  0.0123  (1.95)     Yes  *** *** *** *** *** *** *** *** *** *      ‐40,246  2,033,642  6,495  8.13%  55 Table 4: SEO Proceeds and Cost of Equity This table presents the cross section regression of SEO proceeds on forward-looking cost of equity and control variables. The sample period is from 1970 to 2009. The dependent variable is the proceeds of SEOs divided by the firms' total assets. MFLRP is the one-month forwardlooking market risk premium prior to issuing month. See section 2.2 for details on the computation of this measure. FFLRP is the one-month firm forward-looking risk premium that is equal to the product of market forward-looking risk premium and firms beta. The construction of other variables follows Baker and Wurgler (2002). Accounting data are obtained from the nearest quarterly financial statement prior to SEO. The market-to-book ratio (M/B) is equal to assets minus book equity plus market equity and divided by assets. Fixed assets intensity (PPE/A) is defined as net property, plants and equipment divided by total assets. Profitability (EBITDA/A) is defined as operating income before depreciation, divided by assets. Firm size is defined as the log of net sales. T-statistics are computed from robust standard errors. ***, **, * denote statistical significance at the 1%, 5%, and 10% levels, respectively.  SEO Proceeds/ApreSEO FFLRPpreSEO(τ)  ‐0.0824 *** (‐2.84) MFLRPpreSEO(τ)  (M/B)preSEO  (PPE/A)preSEO  (EBITDA/A)preSEO  log(S)preSEO  Const.     Industry fixed effect  Adj. R‐sq  Nobs  0.0953 *** (10.91) 0.0954 *** (10.91) ‐0.134  ***  (‐3.18)  0.0954  ***  (10.94)  0.0000490 (0.00) ‐1.306 *** (‐3.86) ‐0.0725 *** (‐11.12) 0.368 *** (5.91) ‐0.000124 (‐0.00) ‐1.337 *** (‐3.95) ‐0.0711 *** (‐10.79) 0.375 *** (6.00) ‐0.00449  (‐0.11)  ‐1.339  ***  (‐3.94)  ‐0.0707  ***  (‐10.92)  0.377  ***  (6.02)    Yes   54.00% 5,075     Yes   54.10% 5,075       Yes     54.10%  5,075  56 Table 5: Abnormal Returns of Seasoned Equity Offering Announcements This table presents the mean cumulative abnormal return for the announcement of SEO. Abnormal returns for individual firms are measured using Carhart (1997) four factor model. All issuances are separated into low (70%) firm forward-looking risk premium. T-Values for CAR are computed using crude dependence adjustment method and are presented in the parentheses. ***, **, * denote statistical significance at the 1%, 5%, and 10% levels, respectively. Days  Low FFLRP  (‐1, +1)  (0, +1)  ‐2.14%  ***  (‐11.57)  ‐2.07%  ***  (‐13.71)  Mean Cumulative Abnormal Return Median FFLRP High FFLRP ‐2.09% *** (‐12.48) ‐2.07% *** (‐15.17) ‐2.71% *** (‐11.64) ‐2.78% *** (‐14.64)    Diff(High ‐ Low)  ‐0.57%  ** (‐2.01)  ‐0.71%  *** (‐2.92)  57 Table 6: Regression Estimates for Announcement Period Stock Returns This table presents regression of SEO announcement period cumulative abnormal return on explanatory variables. Abnormal returns for individual firms are measured using Carhart (1997) four factor model. FFLRP is the firms' one-month forward-looking risk premium at the monthend prior to the SEO announcement month. ∆SHR is proportional change in outstanding shares of common stock, measured as the logarithm of number of shares issued over outstanding shares. ∆LEV is the change in debt equity ratio due to the offering, where debt is measured as the book value and equity is measured as the market value of common stock. CON is shareholder concentration, which is measured as the logarithm of total market value of stocks, divided by total number of shareholders. RUNUP is cumulative stock returns over the threemonth period prior to the announcement month. ∆Bret is the three-month bond return calculated from 10 years bond index prior to the announcement month. IPGrowth is the growth rate of industrial production for the three months prior to the announcement month. T-statistics are computed from robust standard errors. ***, **, * denote statistical significance at the 1%, 5%, and 10% levels, respectively. CAR(‐1,+1) FFLRPt‐1(τ)  ∆SHR  ∆LEV  CON  RUNUP  ∆Bret  Ipgrowth  Const.     Adj. R‐sq  Nobs  ‐1.500 ** (‐2.29) 0.00845 (0.06) ‐0.221 (‐0.96) 0.0221 (0.37) ‐0.0541 (‐0.16) ‐1.688 (‐0.64) 2.711 (0.52) ‐2.325 *** (‐3.04)   0.60% 4,069     CAR(0,+1)  ‐1.208  **  (‐1.96)  ‐0.0279  (‐0.22)  ‐0.162  (‐0.80)  0.0313  (0.61)  0.0283  (0.09)  0.655  (0.30)  1.916  (0.42)  ‐2.534  ***  (‐3.98)        0.50%  4,069  58 Table 7: Abnormal Return of Portfolio Formed By years Post-issuance Return This table presents the post-issuance long run performance of portfolios separate by the firm forward-looking risk premium at SEO issuance. See section 2.2 for details on the computation of the forward-looking risk premium. All issuances are separated into low (70%) firm forward-looking risk premium. The portfolios are constructed by value weighting post-issuances return for the SEO companies if the return in a month is within five years of their issuance. The portfolio returns are regressed on Fama-French three factors and Carhart four factors. ***, **, * denote statistical significance at the 1%, 5%,and 10% levels, respectively. Abnormal Ret  Rm‐Rf  smb  hml  Low FFLRPt‐1(τ) 0.032  (0.19)  0.912  ***  (22.96)  0.156  ***  (2.75)  ‐0.227  ***  (‐3.79)  umd  Adjusted R‐sq  Nobs        64.29%  416    0.134 (0.77) 0.890 (22.28) 0.166 (2.97) ‐0.265 (‐4.37) ‐0.112 (‐3.00) 64.97% 416 *** *** *** ***   Median FFLRPt‐1(τ)  ‐0.211 * (‐1.73) 1.109 *** (39.39) 0.210 *** (5.24) ‐0.257 *** (‐6.06)     84.04% 417   ‐0.186 (‐1.5) 1.104 (38.58) 0.212 (5.29) ‐0.266 (‐6.13) ‐0.027 (‐1.00) 84.04% 417 *** *** ***   High FFLRPt‐1(τ) ‐0.321 ** (‐2.12) 1.266 *** (36.25) 0.426 *** (8.57) ‐0.194 *** (‐3.70)     82.24% 416   ‐0.352 ** (‐2.28) 1.272 *** (35.85) 0.423 *** (8.49) ‐0.183 *** (‐3.40) 0.034 (1.02)   82.24% 416 59 Table 8: SEO Issuance Choice and Distress Likelihood Panel A presents the mean one-year probability of default and percentage of negative net income for SEO firms. Probability of default (PD) is calculated from Vassalou and Xing (2004) model and measured at announcement month. Net income (NI) is obtained from the financial statement prior to SEO issuance month. All issuances are separated into low (70%) firm forward-looking risk premium. T-values are reported in the parentheses. Panel B presents the interaction of cost of equity with probability of default and negative net income in the logistic regression of SEO likelihood. The dependent variable is firm-month SEO issuance. It is equal to if a specific firm-month issues SEO, and equals to otherwise. MFLRP is the one-month forward-looking market risk premium at the end of month t-1. See section 2.2 for details on the computation of this measure. FFLRP is the one-month firm forward-looking risk premium that is equal to the product of market forward-looking risk premium and firms beta. PD is the probability of default calculated from Vassalou and Xing (2004) model. NegNI is a dummy variable set to if the firms have a negative income prior to SEO. Size is defined as the natural logarithm of the firm's market value at the end of prior month. Log(M/B) is the natural logarithm of the most recent market-to-book ratio. Beta is individual firms' beta estimated from prior years returns. Firm age is equal to the number of years that the firm is listed in CRSP. Cash, research and development expenditure (RD), operating income before depreciation (OIBD) are obtained from the most recent quarterly reported and deflated by total assets. RD is set to zero if R&D expenditure is missing. RDD is a dummy variable that equals one if R&D is missing. Capex is the capital expenditures in the fiscal year prior to SEO. Rt-4,t-1 is the stock return for the prior three months. IPGrowth is the monthly growth rate of industrial production. All regressions include industry fix effect. Industry classification is based on Fama-French 48 industries. Crisis period are defined as the month with extreme value of forward-looking risk premium. The z values in the brackets are computed from the robust standard errors. ***, **, * denote statistical significance at the 1%, 5%,and 10% levels, respectively. Panel A: Probability of default and firms with negative net income     Low FFLRP  Median FFLRP High FFLRP Diff(High ‐ Low)  Mean PD  3.64  4.25 12.44 8.80  ***      T‐value  (9.89)  % Negative NI  29.80%  27.30% 44.60% 14.80%  ***      T‐value  (9.23)  60 Panel B: Cross sectional Interactions:  FFLRPt-1(τ)  FFLRPt‐1(τ) × PD  PD  FFLRPt‐1(τ) × NegNI  NegNI  Size  log(M/B)  Beta  Cash  Age  OIBD  Capex  RD  RDD  Rt‐4,t‐1  IPGrowtht‐1  Industry fixed effect  Log likelihood  Nobs  Nobs SEO  Pseudo R‐sq  SEO Issuance (=1) All Sample Exclude Crisis Period  (1)    (2)   (3)    (4) ‐0.296  ***  0.765 ***  ‐0.0338 ‐0.202 (‐5.09)  (‐2.92) (‐0.33) (‐1.57) 0.00699  ***  0.0107 ***  (9.59)  (5.19) ‐0.0192  ***  ‐0.0212 ***  (‐16.04)  (‐16.38) 0.225 **  0.232 (2.20) (1.59) ‐0.155 ***  ‐0.161 (‐3.67) (‐3.61) 0.150  ***  0.180 ***  0.144 ***  0.178 (21.87)  (26.08) (21.12) (25.72) 0.663  ***  0.719 ***  0.665 ***  0.723 (40.10)  (44.42) (40.07) (44.57) 0.146  ***  0.127 ***  0.114 ***  0.118 (11.53)  (9.93) (7.51) (7.83) ‐0.897  ***  ‐0.700 ***  ‐0.914 ***  ‐0.703 (‐9.70)  (‐7.73) (‐9.83) (‐7.73) ‐0.0245  ***  ‐0.0246 ***  ‐0.0246 ***  ‐0.0247 (‐16.73)  (‐17.08) (‐16.68) (‐17.05) ‐0.0776  0.273 ‐0.0412 0.311 (‐0.51)  (1.58) (‐0.27) (1.78) 1.499  ***  1.456 ***  1.491 ***  1.443 (11.09)  (11.01) (10.97) (10.86) 2.044  ***  2.109 ***  2.064 ***  2.123 (11.35)  (11.23) (11.41) (11.23) 0.194  ***  0.230 ***  0.202 ***  0.237 (5.66)  (6.68) (5.86) (6.85) 0.356  ***  0.351 ***  0.356 ***  0.351 (16.71)  (16.29) (16.61) (16.22) 0.0143  **  0.0180 ***  0.0175 **  0.0208 (2.07)  (2.60) (2.48) (2.95)              Yes    Yes   Yes   Yes ‐32,714  1,637,927  5,321  8.62%  ‐32,908 1,636,987 5,316 8.00% ‐32,443 1,606,799 5,286 8.65% ‐32,650 1,611,158 5,281 7.98% 61   ***  ***  ***  ***  ***  ***  *  ***  ***  ***  ***  ***    Table 9: SEO Announcement Returns and Distress Likelihood This table presents the announcement period cumulative abnormal return, including the interaction terms of cost of equity with probability of default and negative net income. Abnormal returns for individual firms are measured using Carhart (1997) four factor model. FFLRP is the firms' one-month forward-looking risk premium at the month-end prior to the announcement month. Probability of default (PD) is calculated from Vassalou and Xing (2004) model and measured prior to announcement month. NegNI is a dummy variable set to if the firms have a negative income prior to SEO. ∆SHR is proportional change in outstanding shares of common stock, measured as logarithm of number of shares issued over outstanding shares. ∆LEV is the change in debt equity ratio due to the offering, where debt is measured as the book value and equity is measured as the market value of common stock. CON is shareholder concentration, which is measured as the logarithm of total market value of stocks, divided by the total number of shareholders. RUNUP is cumulative stock return over the three-month period prior to the offering month. ∆Bret is the three-month bond return calculated from 10 years bond index prior to the offering month. IPGrowth is the growth rate of industrial production for the three months prior to the offering month. T-statistics are computed from robust standard errors. ***, **, * denote statistical significance at the 1%, 5%, and 10% levels, respectively. FFLRPt‐1(τ)  FFLRP t‐1(τ) × PD  PD  CAR(‐1,+1) ‐0.684 * (‐1.66) ‐0.0246 *** (‐2.82) 0.0173 (1.40) FFLRP t‐1(τ) × NegNI  NegNI  ∆SHR  ∆LEV  CON  RUNUP  ∆Bret  Ipgrowth  Const.  Adj. R‐sq  Nobs  0.0731 (0.46) ‐0.183 (‐0.73) 0.0638 (1.00) ‐0.119 (‐0.36) ‐2.692 (‐0.93) 6.902 (1.22) ‐2.847 *** (‐3.50)     1.30% 3,366   ‐2.178  (‐2.52)  **  1.445  (1.52)  0.429  (1.58)  ‐0.0187  (‐0.13)  ‐0.192  (‐0.83)  0.0255  (0.42)  ‐0.131  (‐0.39)  ‐1.289  (‐0.49)  3.863  (0.73)  ‐2.538  ***  (‐3.36)        0.90%  4,058  62 Table 10: Post-SEO Change of Debt This table presents the regression of changes in book debts for SEO firms in the year after the SEO issuance on the forward-looking cost of equity and control variables. The change in book debt is measured as the change in book debt in the year after SEO divided by the total assets right after SEO issuance. MFLRP and FFLRP are the market and firms' forward-looking risk premium, measured prior to the SEO month. Other explanatory variables are measured at the quarter-end immediately following SEO issuance. The market-to-book ratio (M/B) is assets minus book equity plus market equity all divided by assets. Fixed assets intensity (PPE/A) is defined as net property, plants and equipment divided by assets. Profitability (EBITDA/A) is defined as operating income before depreciation, divided by assets. Firm size is defined as the log of net sales. ***, **, * denote statistical significance at the 1%, 5%, and 10% levels, respectively. ((D)t+1 ‐ (D)t)/At ‐0.0601 *** (‐3.26) FFLRPpreSEO(τ)  MFLRPpreSEO(τ) (M/B)t  (PPE/A)t  (EBITDA/A)t  log(S)t  Dt/A  Const.  Industry fixed effect  Adj. R‐sq  Nobs  0.0121 * (1.74) ‐0.0510 (‐0.96) 0.137 (0.76) ‐0.00740 (‐1.40) ‐0.0450 (‐1.07) 0.204 *** (3.45)   Yes   7.30% 4290 0.0124 * (1.78) ‐0.0495 (‐0.93) 0.104 (0.58) ‐0.00626 (‐1.18) ‐0.0450 (‐1.07) 0.207 *** (3.52)   Yes   7.30% 4290 ‐0.111  ***  (‐4.80)  0.0126  *  (1.80)  ‐0.0527  (‐0.99)  0.101  (0.57)  ‐0.00592  (‐1.11)  ‐0.0440  (‐1.04)  0.210  ***    (3.56)    Yes     7.40%  4290  63 Figure 1: Number of SEOs This figure plots the number of monthly SEOs from 1970 to 2009. The SEO sample is obtained from SDC database. The sample only includes SEOs with some primary offerings, and listed in NYSE, AMEX, and NASDAQ. Financial and utility companies are excluded.   Monthly number of SEOs   80 70 60 50 40 30 20 10 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 64 Figure 2: The Forward-looking Market Risk Premium This figure plots the monthly forward-looking market risk premium from 1970 to 2009. The method to compute the forward-looking market risk premium is based on Duan and Zhang (2011). The forward-looking market risk premium is computed at each month-end with the forward-looking period of one month.     MFLRP 4.5 3.5 2.5 1.5 0.5 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 65 [...]... This chapter briefly reviews the literature on Seasoned Equity Offerings and the measures of cost of equity capital Selected reviews are conducted based on the relevance of the literature to the thesis 2.1 Seasoned equity offerings Although equity offering is a visible and important activity, its motive varies and the literature suggests different reasons for it A common reason is to raise capital for... measure for the market’s cost of equity, the following regression examines the time series relationship between fraction of SEO issuance and the cost of equity at monthly frequency 1 SEO decisions are likely to be affected by the forward-looking cost of equity for the past few months Qualitatively the same results are documented using past three-month average forward-looking cost of equity 16 (2) The dependent... need is the most important reason for SEO issuances In particular, they document that most issuers would run out of money without the SEO proceeds, even after adjusting for their capital expenditure In summary, the literature has yet to reach a consensus for the primary reason of seasoned equity offerings Nevertheless, cost of equity undoubtedly plays an important role in the seasoned equity offerings. .. 4 SEASONED EQUITY OFFERING AND COST OF EQUITY This section presents the empirical results for the impact of cost of equity on SEO issuances, its announcement effect and post-SEO returns In the following subsections, I investigate the time series relationship between the aggregate SEO issuances and forward-looking cost of equity, followed by a cross sectional study of firm’s issuance likelihood and their... facilitates the study of cost of equity on SEO to a greater extent, including the impact on announcement effect 5 Second, this study proposes a distress based explanation to reconcile the empirical findings of different stock market behavior around SEO at different cost of equity Nevertheless, this study does not preclude other explanations beyond the distress based hypothesis The remainder of the thesis... security offering announcements 4.5 The long run post-SEO effect and cost of equity The previous section documents the evidence that investors react more negatively to the SEO announcements when the cost of equity is higher One question inherited from the previous section is, if the stronger negative reaction to SEO announcement at higher cost of equity is an effect of firms’ non-productive use of proceeds,... possible correlation of the forward-looking cost of equity with market timing indicators (even though measures of market timing are explicitly controlled for in all regression specifications) The principal contribution of this study lies in using a direct measure of forwardlooking cost of equity, bridging the gap between studies in SEO and cost of equity The monthly availability of forward-looking... adverse-selection costs explanation Other variables have little impact on the SEO fraction 4.2 Firm’s likelihood of issuance and cost of equity To examine the cross sectional relationship between firms’ SEO decisions and their respective cost of equity, I use firm-level forward-looking risk premium (FFLRP) which is constructed as the product of the market forward-looking risk premium and firm’s beta (the loading... and their cost of equity1 The cross sectional studies also explore whether the issuance decision for firms with different characteristics are of different sensitivity to their costs of equity Then I continue to examine how the SEO announcement effect and long run post-SEO returns differ for firms that conduct SEO at different costs of equity 4.1 Aggregate SEO issuance and cost of equity Using the forward-looking... projects at these times Therefore, information asymmetry magnifies the sensitivity of small firms’ issuance decisions to the cost of equity There is no significant result for the interaction term of FFLRP t-1(τ) and log(M/B) 4.3 SEO proceeds and the cost of equity Previous sections document that SEO issuance likelihood is affected by the market and firms’ forward-looking cost of equity In this section, I . summary, the literature has yet to reach a consensus for the primary reason of seasoned equity offerings. Nevertheless, cost of equity undoubtedly plays an important role in the seasoned equity offerings. . THE IMPACT OF COST OF EQUITY ON SEASONED EQUITY OFFERINGS ZHANG WEIQI (B.Comp. (Hons.), National University of Singapore) A THESIS SUBMITTED FOR THE DEGREE OF DOCTOR OF. Statistics for Seasoned Equity Offerings SEO Intensity and Market Cost of Equity Logistic Regression of SEO Issuance SEO Proceeds and Cost of Equity Abnormal Returns of Seasoned Equity Offering