Empirical Investigations of Equity Market Anomalies in Corporate Bond and Firm Returns

Transcription

1 Empirical Investigations of Equity Market Anomalies in Corporate Bond and Firm Returns by Frederick M. Hood III Submitted in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy Supervised by Professor John B. Long Jr. William E. Simon Graduate School of Business Administration University of Rochester Rochester, NY 14627, USA 2009

2 ii Dedication I would like to dedicate this dissertation to my parents, Frederick M. Hood Jr. and Mary E. Hood.

3 iii Curriculum Vitae Frederick Hood III was born in Sylvania, Ohio on June 9 th, He attended Michigan State University from 1994 to 1998 and graduated with a Bachelor of Arts degree with high honor in He came to the University of Rochester in the Fall of 1998 and began graduate studies in finance. He received an Olin Fellowship in He pursued his research in finance under the direction of Professor John Long Jr. and received the Master of Science degree from the University of Rochester in While completing his thesis, he worked full time in the research group at Moody s KMV from 2004 to 2008 and is currently a Visiting Assistant Professor in the Finance Department at Virginia Polytechnic Institute and State University.

4 iv Acknowledgements I am blessed to have had the opportunity to learn from the outstanding faculty at the Simon School. I am extremely grateful to Professor John Long for his continuous encouragement, guidance and support. I am also very grateful to Professor Jerold Warner for serving as a committee member and for his guidance on my research. I would like to thank Professor Lu Zhang for serving as a member of my committee during the proposal process. I would like to thank Professor Jay Shanken for his research guidance and instruction for several years while at the Simon School. I would like to thank Merrill Lynch and Lehman Brothers for providing data. I would like to thank Douglas Dwyer and Jing Zhang for allowing me to use Moody s KMV data. I would also like to thank the following Professors who helped improve this thesis by providing comments or serving on the proposal committee: Carmela Quintos, Clifford Smith, Ross Watts, and Gerard Wedig.

5 v Abstract In this thesis I examine two questions related to corporate debt markets. First, how do corporate bond prices react to firm specific information? Second, are the size and book-to-market premiums found in the cross-section of equity returns due to capital structure risk or asset risk? I utilize a unique set of corporate bond returns from Merrill Lynch to provide answers to both questions. In Chapter 1 I describe the bond data and establish that weekly return distributions derived from the Merrill Lynch prices are similar to both pure transaction data and data from a different pricing service. In Chapter 2 I provide evidence that the Merrill Lynch prices reflect firm specific information and the firm specific information in bond prices contains more information about the mean of the firm s cash flows than the variance. I find a positive relationship between weekly bond and stock returns using time-series regressions after controlling for market returns. I also examine cumulative abnormal bond returns around earnings surprises and find that bonds react as predicted given the asymmetric nature of their payoff. Bonds with higher relative credit risk react more to earnings surprises. In addition, bonds react more to negative surprises than positive surprises. Stock prices incorporate the information in earnings surprises relatively faster than bonds do and the relative reaction of bonds to stocks around surprises is small. The demonstration of the bond prices adjusting to firm level information validates their use at the monthly level to build a bond return model.

6 vi In Chapter 3 I provide strong evidence that capital structure differences across firms account for a significant portion of the book-to-market and size premiums found in equity returns. I first establish that standard measures of equity beta from the Capital Asset Pricing Model do not fully reflect differences in financial leverage across firms as expected. I test the hypothesis that missing low grade debt claims from the market portfolio lead to systematic measurement errors in beta related to financial leverage. I do not find support for this hypothesis. In addition, I find that controlling for asset risk differences across firms does not change the conclusion regarding missing leverage risk from beta. I argue that multiple econometric issues do not allow one to directly control for leverage in equity return asset pricing tests. Therefore, I construct firm returns (weighted debt and equity) using an econometric model of debt returns. The model is estimated using the Merrill Lynch data. I am unable to reject the hypothesis that the premium on book-to-market in firm returns is zero. In addition, the premium on size decreases by more than one half. I also find that coefficients on industry adjusted size and book-to-market are substantially lower. Finally, I link my results to prior studies that argue that size and book-to-market premiums in equity returns are related to default risk since financial leverage is a major determinant of default risk. I examine the relationship between default risk as measured by Moody s KMV and equity and firm returns. I find that there is a positive premium on default risk in equity returns and that the premium is substantially lower in firm returns.

7 vii Table of Contents Introduction Chapter 1 Corporate Bond Data Introduction Merrill Lynch Sample Selection and Daily Summary Statistics Weekly Return Statistics Comparing Data Sources Chapter 2 Bond Price Reactions to Firm Specific News Introduction Abnormal Returns Earnings Announcement Returns Portfolios Earnings Response Coefficients Time-Series Relationship between Stock and Bond Returns Conclusion Chapter 3 Beta, Leverage, and the Cross-Section of Firm Returns Introduction Controlling for Leverage Beta and Leverage Firm Return Model Comparing the Cross-Section of Equity and Firm Returns Conclusion References 111

8 viii List of Tables Table 1.1 Table 1.2 Table 1.3 Table 1.4 Table 1.5 Table 1.6 Table 1.7 Table 2.1 Table 2.2 Table 2.3 Table 2.4 Table 2.5 Table 2.6 Table 2.7 Table 2.8 Table 2.9 Descriptive Statistics Ratings Distributions Summary Statistics for Individual Bonds Pooled Summary Statistics for Individual Bonds Averaged First Summary Statistics for Firm Level Pooled Summary Statistics for Firm Level Averaged First Weekly Returns: Comparing Merrill Lynch to Other Sources Weekly Time Series Regressions: By Bonds...79 Earnings Surprise Conditional Sorts: Bond Type and Provisions Earnings Surprise Unconditional Sorts: By Firm Earnings Surprise Conditional Sorts: Rating Earnings Surprise Conditional Sorts: Option Adjusted Spread Earnings Surprise Conditional Sorts: Duration 84 Earnings Response Coefficients: By Bond..85 Earnings Response Coefficients: Bonds by Firm Earnings Response Coefficients: Stocks..87 Table 2.10A Weekly Time Series Regressions: By Bonds...88 Table 2.10B Weekly Time Series Regressions: Additional Specifications - by Bonds Table 2.11 Table 2.12 Weekly Time Series Regressions: Bonds by Firm...90 Weekly Time Series Regressions: Stocks 92

9 ix Table 3.1 Table 3.2 Time-Series Correlation Between Indices Average Characteristics by Portfolio: Table 3.3 Average Cross-Sectional Correlations Between Key Variables: Table 3.4 Table 3.5A Table 3.5B Table 3.6 Fama-Macbeth Equity Return Cross-Sectional Regressions: Double Sorted by Industry and Broad Leverage: Beta and Stock Return Comparison Double Sorted by Industry and Narrow Leverage: Beta and Stock Return Comparison Double Sorted Portfolios: Asset Volatility and Leverage: Table 3.7 Time-Series Average of Monthly Cross-Sectional Distribution Statistics: Table 3.8 Table 3.9 Table 3.10 Table 3.11 Bond Return Model Regression Coefficients.103 Out of Sample Bond Return Model Tests: Single Variable Sorts - Stock and Firm Returns: Fama-MacBeth Regressions for Stock and Firm Returns: Table 3.12 Fama-MacBeth Regressions for Stock and Firm Returns with Beta: Table 3.13 Industry Relative Fama-MacBeth Regressions for Stock and Firm Returns:

10 x List of Figures Figure 1.1 Figure 3.1 Ratings Distribution Bond Return Model Errors

11 1 Introduction In this thesis I explore two important questions related to corporate debt markets. First, how do corporate bond prices react to firm specific information? Second, are the size and book-to-market premiums found in the cross-section of equity returns due to capital structure risk or asset risk? I utilize a unique set of corporate bond returns from Merrill Lynch to provide answers to both questions. The answers to both questions advance the literature by providing insight on how expected and realized returns of the firm as a whole impact equity and debt returns and their relationship to each other. The theoretical groundwork for how stock and bond returns are related to asset returns (firm returns) is well established 1. However, little is known about the cross-sectional properties of firm returns. Given the large amount of analysis of the cross-sectional properties of equity returns in the literature, analyzing firm returns is an important research topic for several reasons outlined below. In addition, little is known about how debt and equity claims jointly react to unexpected news about firm cash flows which change firm value. The lack of results in these areas is directly related to the availability of reliable prices of debt claims to do empirical studies. Bonds and notes are the most frequently traded corporate debt claims and prices are available from dealer transactions. The ability of my empirical analysis to answer the research questions depends on the reliability of the bond price data I use from Merrill Lynch. Some corporate bonds do not trade frequently and liquidity problems will impact prices, more so than common stock prices. In the first chapter of the thesis, I describe corporate bond markets and discuss different sources of data for bond prices. I provide sample properties of the Merrill Lynch returns for the bonds used in the empirical analysis to follow. I also provide evidence that the distribution of Merrill Lynch bond returns is similar in nature to other sources of data where transactions can be verified. 1 The work of Black and Scholes (1973) and Merton (1974) lay the groundwork for how to value a firm s debt claims relative to the return on the firm s assets (firm returns). Option pricing theory leads to a specific relationship between debt, equity, and firm returns (Smith (1976)).

12 2 Therefore, the conclusions reached in the empirical analysis should not be sensitive to this particular source of debt prices. In the second chapter of the thesis, I provide evidence that the Merrill Lynch bond prices are indeed reflecting firm specific information in a timely manner which further justifies their use in analyzing the cross-section of firm returns 2. Empirical studies examining how equity prices react to firm-specific news are an important branch of the finance literature. Event studies dating back to Ball and Brown (1968) and Pettit (1972) examine the manner in which equity values adjust to earnings and dividend announcements, respectively. However, there is little documentation on how corporate debt prices react to firm-specific news. Most studies examining bond reactions to corporate events are concerned with wealth transfer resulting from one-time events 3. A one-time event is often a choice by the firm, whereas every firm must report earnings periodically. A positive realization of earnings relative to expectations should lead to a direct increase in expected firm value, which in turn impacts both the debt and equity claims. Given the residual claim nature of equity, bond prices should be less sensitive to firm-specific news than equity. As the credit quality of corporate debt declines, it should behave more like equity. However, at the firm level, there is very little empirical validation of this basic claim in the literature. An additional factor that makes the response of corporate debt to firm-specific news more interesting is the trading environment for debt. The corporate debt market is largely an over-thecounter dealer market, whereas the majority of equity market trades occur on an exchange. There is a general concern that corporate debt markets are less transparent 2 One other paper to my knowledge, Schaefer and Strebulaev (2007), use Merrill Lynch corporate bond returns over a similar sample period to analyze the relationship between debt and equity returns at the monthly level. The acceptance of this paper at the Journal of Financial Economics further demonstrates the reliability of the data for use in academic research. Their study examines a fundamentally different research question related to hedge ratios. 3 For example: Cook, Easterwood, and Martin (1992), Dann (1981), Hite and Owers (1983), Asquith and Kim (1982), Warga and Welch (1993), Eberhart and Siddique (2002), Maxwell and Rao (2003), Maxwell and Stephens (2003), and Marais, Schipper, and Smith (1989).

13 3 and more costly to trade in than equity markets. Therefore, it is possible that bond markets are slower to react to firm-specific news than equity markets. In the second chapter of the thesis, I show that weekly contemporaneous stock and bond returns are positively correlated after controlling for stock and bond market returns. This correlation increases as credit quality declines. I also show that lagged equity returns predict current bond returns at the weekly level. This is consistent with previous literature and the idea that debt markets are slower to incorporate firmspecific news 4. I do not examine the underlying causes as to why the lead-lag relationship exists. Both sets of weekly returns are autocorrelated and the lead-lag relationship does not necessarily imply the bond market is inefficient. Trading costs of bonds are much higher than stocks and the marginal return to acting on new information is lower. In Chapter 2 I also examine the reaction of bonds to earnings announcements. As expected, I find an asymmetric response to positive and negative earnings surprises by bonds; bonds react only to downside news on average. I find that stocks fully incorporate information more quickly than bonds do. Bonds continue to react to negative earnings surprises up to 15 days after the earnings announcement. I also document that bonds with more credit risk and greater duration are impacted more by earnings surprises. While these results seem intuitive, this is the first study to document asymmetric responses of bonds prices to good and bad news, and variation in responses across predicted bond characteristics. Chapter 3 is the core of the thesis. Chapter 3 focuses on the cross-section of equity and firm returns. Firm returns are the weighted average of equity and debt returns. The existence of public and private debt in a firm s capital structure is the only difference between the two. By definition, the firm s stream of future cash flows 4 Kwan (1996) examines the contemporaneous and lagged relationship between equity returns and bond yields at the weekly level. Gebhardt, Hvidkjaer, and Swaminathan (2005b) examine crosssecurity momentum effects between stock and bond returns at the monthly level. Both studies find that lagged equity return information predicts future changes in bond prices. Hotchkiss and Ronen (2002) argue that stock prices do not incorporate firm specific news faster than bond prices, based on evidence from a small sample speculative grade bonds with high frequency transaction prices.

14 4 has a certain level of risk which is scaled up by financial leverage. I refer to financial risk as the difference between equity risk and asset risk. I argue that the debate over the cross-section of equity returns is directly related to financial and asset risk. Anomalous empirical results by Fama and French (1992) have led to a line of literature examining rational and irrational explanations to their results. The basic result in their paper is that market value of equity and book-tomarket equity explain the cross-section of equity returns better than measured equity beta. Fama and French (1993, 1996) argue that the premiums on size and book-tomarket are compensation for bearing systematic risk and are not due to investor behavioral biases. However, the exact type of systematic risk or the true economic force driving the premiums is still unknown. Fama and French argue that the premiums are due to distress related to the cash flows process of the firm. I argue that the size and book-to-market premiums are compensation for bearing financial risk, not asset risk. I first show that controlling for beta and leverage in equity return tests is not adequate. I then argue that testing cross-sectional premiums in firm returns is the best way to neutralize any mechanical relationship between capital structure and equity returns. I estimate several variants of the market beta and show that there is no crosssectional relationship between leverage and beta after controlling for industry. The assumption is that firms within industries have similar asset systematic risk, not necessarily total asset risk. Controlling for total asset risk estimated from a system of equations based on a Merton (1974) type structural model, I find a non-linear increasing relationship between beta and leverage 5. The lack of or weak relationship between beta and leverage is puzzling and is likely driven by estimation error since covariance with any portfolio should increase with leverage, all else constant. After attempting to reduce errors in beta mechanically related to leverage ratios, I am unable to find a strong positive relationship. I contribute to the literature by empirically testing the theoretical prediction by Ferguson and Shockley (2003) that 5 I use asset volatility based on an iterative solution from a two equation system. Vassalou and Xing (2004) calculate asset volatility in a similar manner.

15 5 measurement error in beta is mitigated when including multiple debt return indices in the beta estimation specification. I show that both beta on a broad market index including debt, and multi-beta specifications, do not reduce the role for size and book-to-market in the cross-section of equity returns. I examine a broad cross-section of firms over a thirty-six year period by using estimated debt returns. This is the first study comparable in breadth to Fama and French (1992) that examines the book-to-market and size premiums in firm returns. I am able to maintain a size bias free dataset by estimating a model for debt returns using the bond returns from Merrill Lynch. Using this method, I am unable to reject a zero cross-sectional book-to-market premium in firm returns and find that the size premium is substantially reduced. In addition, I control for asset risk (by industry) and decompose the premiums into differences in leverage across each industry. The industry relative results further support the hypothesis that size and book-to-market premiums are compensation for bearing systematic financial (leverage) risk. Another contribution I make to the literature is tying industry level empirical results that do not seem to fit an asset risk story to a financial risk story. The final contribution I make to the literature is related to how the crosssection of equity and firm returns are related to the default risk of the firm. Vassalou and Xing (2004) argue that the size effect in stock returns is closely related to default risk and that measures of default risk are priced in the cross-section of equity returns. Petkova (2006) finds that innovations in the default spread are related to the SMB loading in the Fama and French three factor model. This suggests default risk is related to the size premium. Their results are consistent with the risk of financial leverage story. However, since default risk contains information about a firm s leverage and asset volatility, it is important to test default risk itself 6. I find that default risk as measured by Expected Default Frequency from Moody s KMV is related to average equity returns in the cross-section. Information 6 A simplified way of thinking about distance-to-default in a structural model of default risk is one minus the firm s leverage ratio divided by its asset value volatility. Assuming that the value of the firm s assets is normally distributed, distance-to-default can be used to calculate the default probability of the firm, or the probability that its asset value falls below its defaultable liabilities (default point).

16 6 about firm s asset volatility in EDF leads to a premium in equity returns not captured by leverage alone or by size, book-to-market and beta. Controlling for default risk separately in a linear regression with the other variables may be misspecified as I argue leverage is. The premium on default risk does not exist in firm returns. This suggests the default risk premium in equity returns is related to differences in capital structure across firms. Even though the size premium in equity returns is reduced when controlling for EDF, some premium remains. This and the fact that a small size premium exists in firm returns, suggest that equity size is related to some other risk factor not captured in my analysis. Overall, the results in Chapter 3 show that financial risk plays a significant role in explaining asset pricing anomalies. This is a significant contribution to the literature and challenges asset pricing theory to incorporate capital structure choice and its associated risk.

17 7 Chapter 1 - Corporate Bond Data 1.1 Introduction Previous empirical studies of corporate bond prices have utilized several different sources of data. Before discussing the different sources of data, it is useful to discuss the trading environment for corporate bonds. The corporate bond market is segmented between an exchange based and an over-the-counter dealer market. Exchange based trades take place on the NYSE Automated Bond System and make up a small fraction of total market trades. Hong and Warga (2000) note that at least 90 percent of corporate bond trades take place between dealers. Transactions on the exchange market are largely odd lot trades under 100 bonds, while dealer transactions are much larger and are generally round lots (100 to 1000 bonds). Hong and Warga document that the historical average trade size is about 20 bonds on the exchange, while the average dealer trades are 50 times larger. Academic studies using market values of bonds should ideally reflect actual transactions in the marketplace. The issue of transactions and quotes is often a problem for assets not trading on a high frequency basis. A quote could be given either in the dealer market or the exchange where no transaction ultimately takes place. Without more detailed information, other than a quote, it is not possible to differentiate between transaction based or non-transaction based quotes. Hong and Warga (2000) argue that transaction prices from the exchanges are surprisingly close to dealer transactions prices. However, they find non-transaction prices from exchanges deviate substantially from dealer prices and suggest the prices are questionable for use in empirical studies. Gehr and Martell (1992) study quote spread differences among dealers and find they often do not intersect 7. Trading frequency of corporate bonds is an important empirical issue related to the problem of differences in transactions prices and quotes. Many corporate bonds 7 Michael Walsh details some of the different pricing methods used in the corporate bond market in, Bond Pricing: Method or Mayhem? Brown Bothers Harriman Insurance Asset Management Log, May Saunders, Shrinivasan, and Walter (2002) also detail the price formation process in the inter-dealer corporate OTC market.

18 8 do not trade frequently. Since empirical studies generally focus on consistent intervals, a researcher must decide what price to use for a bond without a recent trade or quote. There is a significant demand for pricing of bonds at regular intervals from institutional managers who must report values of assets (mark-to-market). This demand is met by a number of pricing services. Corporate bond pricing services typically rely on dealer quotes to produce daily evaluations. A pricing service business naturally arises out of an institution such as Merrill Lynch or Lehman Brothers, both major dealers in the secondary market. Other information providing services, such as Bloomberg, Interactive Data Corporation or Thompson Reuters, gather data from dealers to construct prices 8. Since there can be multiple quotes or no quotes on a given day, the service needs to decide how to handle the issue. If multiple quotes vary substantially, a decision must be made on how to construct the daily price. If no quote is given, they may provide modeled prices or just use the price from the prior day. When multiple quotes or transactions occur, services often take the closing price that is within certain bounds for a given day. A single trade that deviates substantially from the range of other observed trades in a given day is typically disregarded. One argument is that liquidity trading often causes these price outliers. Fund managers seek to mark-to-market with less noisy prices; therefore, the filtering of traded prices method is optimal for their purposes. If clients require a price to be based on the current day s market conditions and no transaction or quote to base a price on exists, the pricing service must model the price in some way. The pricing services now utilize proprietary issuer specific yield curves in their models for evaluations. This ensures firm-specific information is reflected daily in evaluations. However, in the past, prices of non-traded bonds were matrix based. Matrix prices are based on recently traded bonds with similar characteristics to the bond being priced. Warga and Welch (1993) examine leveraged 8 Mergers and acquisitions have been common among pricing services. Interactive Data Corporation purchased Merrill Lynch s pricing service in Reuters acquired Bridge/EJV in Thomson Financial, another data provider, acquired Reuters in 2007.

19 9 buyout transactions and argue that matrix prices do not reflect firm specific information in a timely manner. Moody s Bond Record or Standard and Poor s Bond Guide have been the source of prices for several empirical bond return studies 9. Moody s and S&P prices are purely based on exchange based quotes. Nunn, Hill and Schneeweis (1986) study differences in risk and return measurements using Moody s and Merrill Lynch prices. They argue Merrill prices are timelier and more likely to reflect current market conditions and information. Recently, several studies utilized the Lehman Brothers Fixed Income Database 10. The database contains bond pricing and characteristic information on a monthly basis. Over half of the pre 1980 s data is matrix based, but the majority of the later data is based on Lehman dealers prices 11. However, Lehman Brothers recently discontinued the sale of the database to academics. Other recent studies have examined bond yields and quotes from DataStream (Thompson Reuters) which are based on dealer quotes, similar to Merrill and Lehman (e.g. Chen, Lesmond, and Wei (2007)). Finally, other studies utilized recent data on insurance company bond trades, which must be filed with the National Association of Insurance Commissioners (e.g. Schultz (2001)). These data are contained in the Mergent Fixed Income Securities Database. A new source of transaction data from the National Association of Securities Dealers (NASD) Trade and Reporting Compliance Engine (TRACE) recently became available to academics. This system, implemented in July of 2002, requires all NASD members to report corporate bond transactions to TRACE. Not all corporate bonds were initially covered by TRACE; the coverage has increased since As of February 2005 about 99% of publicly traded bonds are covered. 9 Examples of studies using Moody s or S&P include: Kim and McConnel (1977), Crabbe (1991), and Cook and Easterwood (1994). 10 Examples of studies using the Lehman database are: Duffee (1999), Collin-Dufresne, Goldstein and Martin (2001), Eberhart and Siddique (2002), Elton, Gruber, Agrawal, and Mann (2001), Gebhardt, Hvidkjaer, and Swaminathan (2005a, 2005b), and Maxwell and Stephens (2003). 11 Hecht (2000) reports the percentage of matrix based prices in the Lehman database by year.

20 10 The TRACE system also contains corporate bond volume information. This data is virtually unstudied. It is interesting to see which bonds trade every day, in addition to which bonds trade more frequently for a given firm. This information helps reveal the extent of matrix pricing for daily price databases. In a testimony before the U.S. senate, a NASD representative stated that of the 23,000 publicly traded bonds, 20% of them traded at least once per day, with 5% trading more than five times a day 12. To my knowledge, Edwards, Harris, and Piwowar (2007) were the first to examine the TRACE data, perhaps due to the SEC access to the data. They examined the trading costs for corporate bonds based on several factors and argue that costs decrease after TRACE inclusion. Since then others used the data for a number of studies including: Bessimbinder, Maxwell, and Venkataraman (2006), Bessimbinder, Kahle Maxwell, and Xu (2008), and Goldstein, Hotchkiss and Sirri (2007). There are limitations to the use of the TRACE data in practice without supplementary data. The main drawback is there is no data on accrued interest, so the researcher must either use returns based on clean prices only or find data on coupon, payment date and frequency. To study the research questions outlined in the introduction, I utilize daily bond prices and accrued interest from Merrill Lynch s Investment Grade Index. The data was acquired directly from Merrill Lynch and is the same data used to compute daily index returns. As discussed, the Merrill data in the past has been shown to be reliable and based on issuer and bond specific information in a timely manner. The data is from January 1 st 1997 to June 31 st Schaefer and Strebulaev (2007) use similar data from Merrill Lynch covering a period from December 1996 to December The acceptance of this paper for publication at a top tier finance journal testifies to the reliability of the use of the data for academic purposes. 12 See the document, An Overview of the Regulation of the Bond Markets, which contains the testimony on June 17, 2004, of Doug Shulman, President - Markets, Services and Information of the NASD.

21 11 I use the TRACE and Reuters/EJV data as a second and third source to examine characteristics of returns relative to the Merrill data. There is no direct overlap between the TRACE data and the Merrill Lynch data sample; therefore it is not possible to do a direct comparison of Merrill prices to transaction prices. I compare return distributions at the weekly level for the Merrill, TRACE, and Reuters/EJV data based on ratings. This demonstrates that the Merrill data is similar in nature to pure transaction based type data and other information service prices. 1.2 Merrill Lynch Sample Selection and Daily Summary Statistics In addition to quote prices and accrued interest, the Merrill Lynch data provided for my research purposes includes information on yield measures, option adjusted spread (OAS), and effective duration 13. I use a basic sample selection process to create the core dataset used in the remainder of the analysis and then require additional filters on the data depending on the particular use of the data. The entire sample contains 8,080 unique bond CUSIP identifiers. The first restriction I make is that price, accrued interest, OAS and duration are populated. For the purposes of constructing the firm return model in Chapter 3, I first compute firm level debt returns using the issuer level CUSIP and merge with equity data from Center for Research in Securities Pricing (CRSP). The sample and comparison of the data to the broad cross-section is described in Chapter 3. For the analysis of how the bonds react to firm specific information, I broaden the definition of the firm to includes other financing offshoots that may issue debt but do not necessarily have the same issuer level CUSIP as the equity of the firm. Since the sample for the firm return model is described in Chapter 3, the remaining description of the sample selection in this section refers to the analysis in Chapter Option adjusted spread and effective yield account for imbedded options that decrease the bonds value given movements in interest rates. For example, if a bond is callable and interest rates drop, the bond may be called by the firm and the stream of cash flows to investors change. The call option is factored in the price of the bond and hence the static spread and yield, but not the option adjusted spread. Effective duration accounts for changes in bond prices from changes in interest rates. The difference from modified duration is that it accounts for changes in cash flows and price from option components mentioned above given an interest rate movement (Fabozzi (2006)).

22 12 Since bond ratings are an essential data item used in the empirical analysis, each bond must have a rating from S&P and Moody s to be included. The restriction to the rated universe typically will discard less liquid bonds of smaller firms. Since the data is used to calculate the investment grade index, this filter is implicitly applied before I received the data. I use ratings from Reuters, in addition to information on the type of bond issued, any provisions, its coupon rate, and payment frequency. I delete all convertible bonds from the sample. I include bonds with other imbedded options such as calls and puts since this gives a broader distribution of rated bonds on the lower end of the rating scale. I use a firm level mapping from an issue specific identifier to a firm level identifier derived by Moody s KMV. The firm level identifier is associated with an equity CUISP identifier from CRSP. I require that the equity level CUSIP mapped to the bonds is for common stock only. The match between CRSP common stock and the Merrill data significantly reduces the number of bonds in the sample. This is expected as there are many public bonds issued by firms without public equity. Utilities make up a large portion of these bonds. In addition to the restrictions above, I require bonds prices in the remaining sample change frequently. The fact that clean prices do not change every day suggests that this dataset does not contain prices which are evaluated from yield curves or other bonds since the clean price would move otherwise. For each bond I compute the percentage of time-series observations where the bond price does not move. This percent is based on the clean bond price as the price including accrued interest will automatically increase from the daily accrual of additional interest. A particular bond price was deleted from the sample if it does not move on a daily basis for more than 10% of its time-series observations. This restriction removes 123 bonds from the sample after the other filters are applied. This final sample contains 3,084 bonds with 535 unique equity identifiers. I compute holding period returns for a particular bond so a daily return will include the additional accrued interest plus the change in the clean price divided by

23 13 yesterday s price. When the coupon is paid, the accrued interest drops to zero, but the return will include the coupon payment. Given the nature of the positive return from the interest, the properties of daily bond returns will be positive on average when no underlying information has changed about the credit quality of the firm. Since I use abnormal returns or returns adjusted for movements in indices, this should not bias the results in favor of finding positive abnormal returns over an event. Table 1.1 Panel A contains summary information about the distribution of the number of bonds per firm. Panel B contains the percentage of each type of bond in the sample and Panel C contains the percent with embedded options. The distribution is highly skewed as most firms have a small number of bonds outstanding and few have a large amount. The median number of bonds per firm is 3. More than 25% of the firms in the sample have only 1 bond outstanding after the data filters. In Panel B the breakdown of bond type shows that 23% of the bonds in the sample are classified as debenture (no specific lien on an asset held by the firm). 39% of the debentures are classified as senior. The remaining bonds, which are technically notes, are mostly senior as well. The senior and unsecured senior notes make up about 70% of the sample. As for the embedded options, 31% have call provision, while 7% have put provisions. Table 1.2 contains the ratings distribution for S&P and Moody s and Figure 1.1 displays the ratings histogram. For this purpose I use the last available rating before the bond drops out of the database or the end of the sample occurs. There are a very small percent of bonds with a rating below investment grade. This is likely due to some small delay in the bond being removed from the index once it is downgraded. I examined if this is the case and confirmed bonds are removed once they are downgraded to speculative grade. I will not report statistics by rating group below BBB- or Baa3 for the remaining parts of the document given the small number of observations. The ratings distributions are bell shaped which is consistent with Moody s and S&P reports. 60% of the bonds are rated A-/A3 or above, which leaves the largest non-alpha-numeric rating as BBB/Baa.

24 14 Table 1.3 contains summary statistics for returns, duration and OAS after pooling the data by rating group. I report the statistics based on Moody s and S&P rating to ensure there is not a systematic difference between the relationship between returns, spreads, and ratings between the two sources. The mean level of daily bond returns is similar among the ratings groups, with the Baa/BBB group having the lowest mean. However, the median is actually higher for the Baa/BBB group, reflecting the skewed nature of the returns for these firms. Unreported results show this is not driven by the use of ratings at the end of the sample. The mean and median return pattern across the rating groups is very similar using the beginning of sample ratings. The risk of the lower rated bonds is apparent in the standard deviation measure and range of returns as well. The standard deviation and range of returns is monotonically increasing as ratings decrease. In addition, OAS is increasing as ratings decrease. The Baa/BBB bonds average an OAS of 170 for Moody s ratings and 167 for S&P (basis points). There is not a strong relationship between duration and rating. The Baa/BBB bonds have the longest duration, with an average of about one year longer than the other ratings classes. Schaefer and Strebulaev (2007) report a similar average duration measure for their sample. Table 1.4 contains summary statistics for returns, duration and spread after first averaging the time-series of each bond then taking statistics by rating group. This method displays any influence of bonds with more time-series observations in the sample relative to Table 1.3. I have added data on the distribution of time-series observations by rating class for each bond. The distributions are similar for the ratings classes with a minor difference between Moody s and S&P in this case. The average bond return pattern is similar to Table 1.3. The standard deviation of returns is now larger for the Aaa/AAA bonds since the sample size is only around 100 for that group now, relative to the pooled sample size of over 50,000 for Aaa/AAA in Table 1.3. The distribution of duration decreased across the board, which is consistent with the fact that the bonds with less time series observations have shorter maturities and hence lower durations. Finally, the pattern of OAS distribution is similar to Table 1.3

25 15 with the standard deviation in spreads increasing in the Aaa/AAA group for the same reason as returns, the sample size. Tables 1.5 and 1.6 contain statistics after averaging the bond data by 6-digit issuer CUSIP then merging with the associated stock returns. Table 1.5 contains summary statistics pooled by rating and statistics in Table 1.6 are computed based on rating groups after averaging each firm s time series data first. As with Tables 1.3 and 1.4, I also examined the relationship between the reported returns and spreads based on ratings at the beginning of the sample or when they enter the sample. The unreported results are quantitatively very similar, which is important to note given that ratings downgrades throughout the sample can influence returns of stocks and bonds. One issue to note is that the number of observations for the highest ratings groups drops substantially in Table 1.6 since we are looking at firms instead of bonds now. Therefore, when looking at the distribution statistics in Table 1.6 for Aaa/AAA and Aa/AA groups they must be interpreted with caution. The pattern in mean bond returns across the ratings groups is similar in Tables 1.5 and 1.6. Once again, the lower rated firms have lower average bond returns and higher OAS. The main point of interest in these two tables is related to the relationship between the stock return statistics and the bond return statistics. First, the stock returns are much larger than the bond returns on average. For the firms rated A or above, the average is between 6.3 and 7.2 basis points, where average bond returns are 3.0 to 3.2 basis points. Second, the range and standard deviation of stock returns is much larger than the bond returns, which is expected. Stock returns have a median of 0% for all ratings groups reflecting the fact that prices do not move for some stocks on some days in the sample. However, Table 1.6 shows the median of the means by time-series is similar to the averages. The standard deviation of stock returns in Table 1.5 is about five times the size of the bond return standard deviation by rating group and is increasing across the ratings groups as with the bond returns. Finally, the last interesting pattern is that the average stock return for the below A rating group is much lower than the other ratings groups. It appears that stocks with the Baa/BBB

26 16 ratings performed much worse than those rated A or above, especially relative to their volatility. This group makes up the largest amount of the data. 1.3 Weekly Return Statistics Comparing Data Sources As the discussion of bond data sources outlined, daily bond price evaluations may not necessarily reflect market transaction prices. Since I use daily evaluations from the Merrill Lynch Investment Grade Index to compute returns at weekly and monthly horizons for the remainder of the thesis document, it is important to compare the return characteristics to transaction based data and other price evaluations 14. If the return distributions are completely different, this suggests the major conclusions in the paper may be influenced by the data sourced used. To accomplish this comparison, I use TRACE and Reuters/EJV data from July 1 st, 2002 until December 31 st, The TRACE system initially required only a set of bonds starting in 2002 and this requirement was increased in March of I filter the TRACE database for a set of bonds that begin in 2002 and I do not use bonds that were added to the system (almost all corporates) in the last step-up in October I place further restrictions on the bonds in the sample. These restrictions are similar to the restrictions placed on the Merrill sample. First, I require at least 60 days where the bond is traded between the first and last appearance in the sample period. This decreases the number of bonds to about 5,000. Second, I require the number of days the bond does not trade to be less than or equal to 75% of the total number of days between the first and last appearance in the sample period. This reduces the number of total bonds to about 3,200. Third, I require that the identifier can be merged with CRSP equity data and Reuters/EJV bond data. The bond must also have a Moody s rating. As with the Merrill data, many of the bonds cannot be linked to a common stock price because the firm is either has no public equity or has not issued common 14 I use weekly returns to estimate time series regressions and monthly returns to estimate a bond return model for the firm. I do utilize daily bond returns to estimate abnormal daily bond returns, but these returns are aggregated at 5 to 15 day increments which is a minimum of one week. I report summary statistics on the daily bond returns in the prior tables for this reason.

27 17 equity. The remaining number of bonds is 1,777. The fundamental reason this sample contains fewer bonds than the Merrill sample is because of the requirement that the data be in TRACE in either 2002 or This restricts the sample relative to the Merrill data, which reflects most investment grade bonds in the market at the time. Returns are computed in the same manner as the Merrill data (dirty prices); therefore, I use accrued interest from EJV added to the TRACE price. Since TRACE is transaction based, a decision must be made on how to compute the daily prices. I use three different methods for this as a sensitivity check. For the first measure of daily price, I use trade volume weighted prices on a given day. For the second measure, I use the median price observed for a given day. For the third measure, I use the actual pseudo closing price; the last reported trade of the day. Bessimbinder et al. (2008) estimate a similar set of daily prices from TRACE in their analysis and compare these. For the EJV returns only one price per day exists. While the price closest to the EJV and Merrill prices is not clear, it stands to reason the last TRACE trade price would be closest to the others as long as the price was not of abnormal volume or a large price outlier relative to other trades in the day. The Merrill and TRACE-EJV periods do not overlap but are adjoining. Interestingly, the break occurs at the peak of a credit cycle. Spreads were low and then increased during the Merrill sample and spreads were high and decreased during the TRACE-EJV sample. Therefore, the average return should depend on the average risk during the period. However, since these are realized returns, the return variability should not necessarily be different. Unreported statistics show the mean and median yield is close to 1% higher in the Merrill sample. However, the average risk-free rate or equivalent term treasury yield was more than 1% higher during the Merrill sample. Therefore, the average spread and return is predicted to be lower during the later sample 15. Since there were two major market events impacting spreads in the Merrill sample (Long Term Capital collapse and 9/11 and the subsequent recession), I would expect higher variability in returns relative to the EJV data. 15 I am not able to directly compare OAS of the two samples since this data is only available for the Merrill sample.

28 18 Table 1.7 contains the distribution of weekly returns for all bonds in the two samples. The table also contains the distribution after pooling the bonds by rating. The Merrill sample is 2,784 bonds, the same sample size used for the time-series regressions. The distributions of the Merrill and EJV returns are very similar. The overall 99 th percentile maximum monthly return is 2.94% for the Merrill returns and 2.83% for the EJV returns. On the low end the returns are also similar. This also holds across the ratings groups. The only exception is the below Ba group where no comparable Merrill data exists. As expected, the low rated bonds have more volatile returns. The TRACE return distributions have longer tails. The distributions spread out marginally from volume weighted, to median, to closing. This makes sense because the median price is less likely to be an outlier or a small trade than the closing price. Across the ratings groups, the 99 th percentile for the Merrill returns falls closer to the 95 th percentile of the TRACE return distributions. The inter quartile range by rating group for the Merrill returns is higher than the EJV returns, but slightly lower than the TRACE ranges. The overall inter quartile range for the Merrill returns is 0.81% and is 0.97% for both the TRACE trade weighted and median returns. The TRACE prices are more volatile than the daily evaluations from both Merrill and EJV. This is fully expected as these services provide value by deleting outlier trades that do not seem to indicate the value of the bond relative to the other trades for a given day. This, in a sense, increases reliability of the data for empirical uses reflecting market values because a huge variation in price can exist between trades reported by TRACE in a given day. Trade size seems to be a very big determinant of the transacted price in the market. Perhaps the implementation of TRACE itself increased transparency and decreased the ability of dealers to charge large premiums for small trades 16. Edwards, Harris, and Piwowar (2007) argue this 16 There are often cases within the TRACE data where the dollar value of the trade is under $100,000. These trades are not likely to be institutional investors. Edwards, Harris, and Piwowar (2007) and Bessimbinder, Kahle Maxwell, and Xu (2008) look at weighted prices excluding these trades.