Aggregate Earnings and Corporate Bond Markets

DOI: 10.1111/1475-679X.12030Journal of Accounting Research

Vol. 52 No. 1 March 2014Printed in U.S.A.

Aggregate Earnings and CorporateBond Markets

X A N T H I G K O U G K O U S I∗

Received 24 January 2012; accepted 10 September 2013

ABSTRACT

I examine the previously unexplored relation between aggregate earningschanges and corporate bond market returns. I find that aggregate earningschanges have a negative relation to investment-grade corporate bond mar-ket returns and a positive relation to high-yield corporate bond market re-turns. The aggregate earnings-returns relation is lower (i.e., less positive ormore negative) for bonds with higher credit ratings and longer maturities.Further, I show that the aggregate earnings-returns relation is driven by boththe expected and the news component of aggregate earnings changes. Theexpected component is negatively related to expected returns, and the newscomponent is positively related to cash flow news and changes in nominalinterest rates, and negatively related to changes in default premia. My resultscontribute to the understanding of the role of earnings in corporate bondmarkets as well as the macroeconomic role of accounting information.

∗Cornerstone Research.Accepted by Douglas Skinner. I thank an anonymous referee, Dion Bongaerts, Renhui

Fu, Gerard Mertens, Erik Peek, Peter Pope, Buhui Qiu, Bill Rees, Gil Sadka, Rui Shen,Theodore Sougiannis, Mathijs van Dijk, Manuel Vasconcelos, and David Veenman, and work-shop participants at Columbia Business School, Cornerstone Research, Erasmus School ofEconomics, ESSEC Business School, EAA 28th Doctoral Colloquium, EAA 35th AnnualCongress, HEC Paris, IESEG School of Management, Rotterdam School of Management,Universidad Carlos III de Madrid, University of Amsterdam, and Vrije Universiteit for help-ful comments. The views expressed in this article are solely those of the author, who isresponsible for the content, and do not necessarily represent the views of CornerstoneResearch.

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Copyright C©, University of Chicago on behalf of the Accounting Research Center, 2013

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1. Introduction

Aggregate earnings are a valuable source of information about the macroe-conomy as they reflect the value-generating ability of all the firms in theeconomy. Little is known, however, about the relation between aggregateearnings and asset prices and the exact nature of the information containedin aggregate earnings. I examine the relation between aggregate earningschanges and corporate bond market returns as well as the determinants ofthe aggregate earnings-returns relation for corporate bonds.

Kothari, Lewellen, and Warner [2006] document a negative relation be-tween quarterly aggregate earnings changes and stock market returns, andCready and Gurun [2010] confirm this negative relation at a daily fre-quency. Findings on the aggregate earnings-returns relation for stocks maynot generalize to corporate bonds. Corporate bonds have shorter matu-rities and predetermined and senior cash flows compared to stocks, andentail different risk premia (e.g., Fama and French [1993]). Consequently,the sensitivity of corporate bond prices to the information contained inaggregate earnings can be different from the sensitivity of stock prices.

The determinants of the negative aggregate earnings-returns relation forstocks are not well understood. Academic literature suggests that aggregateearnings changes are negatively related to stock market returns because ag-gregate earnings move with discount rates, but the exact nature of the rela-tion between aggregate earnings and discount rates remains an open ques-tion. One stream of literature argues that aggregate earnings move with dis-count rates because unexpected aggregate earnings changes are positivelyrelated to discount rate news (e.g., Kothari, Lewellen, and Warner [2006]),whereas another stream of literature suggests that aggregate earnings movewith discount rates because expected aggregate earnings changes are neg-atively related to expected returns (e.g., Sadka and Sadka [2009]).

The precise mechanism that links the unexpected component of ag-gregate earnings changes to discount rate news is also unclear. Shivaku-mar [2007] suggests that unexpected aggregate earnings changes are pos-itively related to discount rate news because they are positively associatedwith changes in inflation expectations, Patatoukas and Yan [2010] proposea positive relation between unexpected aggregate earnings changes andchanges in real interest rates, and Patatoukas [2013] provides evidence ofa positive relation between unexpected aggregate earnings changes andchanges in real interest rates, inflation expectations, and the equity riskpremium.

The use of corporate bond prices instead of stock prices can provideadditional insights into the relation between aggregate earnings and dis-count rates for two reasons. First, corporate bonds have predeterminedcash flows and finite maturities. As a result, I can accurately match the dura-tion of corporate bonds to the duration of risk-free assets, I can isolate thereturn component that is associated with changes in interest rates from the

AGGREGATE EARNINGS AND CORPORATE BOND MARKETS 77

return component that is associated with changes in risk premia, and I canmore precisely examine the relation between aggregate earnings changes,changes in interest rates, and changes in risk premia. Second, the determi-nants of stock and bond prices are different. Stock prices move primarilyin response to changes in equity risk premia, whereas bond prices moveprimarily in response to changes in inflation expectations (e.g., Campbelland Ammer [1993]). If aggregate earnings move with asset prices becausethey contain information about inflation expectations (e.g., Shivakumar[2007]), then using corporate bond prices to examine the relation betweenaggregate earnings and discount rates will produce more robust evidencethan using stock prices.

I use a sample of quarterly observations from January 1973 throughDecember 2010 to examine the previously unexplored relation betweenaggregate earnings changes and corporate bond market returns. I findthat aggregate earnings changes are negatively related to investment-grade corporate bond market returns, and positively related to high-yieldcorporate bond market returns. The aggregate earnings-returns relationfor corporate bonds is statistically as well as economically significant; atwo-standard-deviation positive shock to aggregate earnings changes cor-responds to a 1.66% decrease in the quarterly investment-grade corporatebond market returns, and a 1.23% increase in the quarterly high-yieldcorporate bond market returns. Further, I find that the aggregate earnings-returns relation is lower (i.e., less positive or more negative) for bondswith higher credit ratings and longer maturities. My results differ fromthe firm-level finding of a positive relation between earnings changes andcorporate bond returns, and from the finding that the earnings-returnsrelation for corporate bonds at the firm level depends only on corporatebond credit ratings (e.g., Datta and Dhillon [1993], Easton, Monahan, andVasvari [2009]).

Next, I shed light on the exact nature of the information contained in ag-gregate earnings. In line with Sadka and Sadka [2009], I find that expectedaggregate earnings changes are negatively related to expected returns. Fur-ther, I show that unexpected aggregate earnings changes are positively as-sociated with changes in nominal interest rates and cash flow news, andnegatively associated with changes in default premia. These latter findingsare consistent with Kothari, Lewellen, and Warner [2006], who suggest thataggregate earnings changes contain new information about discount rates.

My findings contribute to two streams of literature. First, I add to the un-derstanding of the role of earnings in corporate bond markets (e.g., Dattaand Dhillon [1993], Easton, Monahan, and Vasvari [2009]). My results sug-gest that firm-level findings on the relation between earnings changes andcorporate bond returns do not generalize to the aggregate level because theinformation contained in firm-specific earnings is different from the infor-mation contained in aggregate earnings. Firm-specific earnings changes arepositively related to firm-specific corporate bond returns because higherthan expected firm-specific earnings provide good news about future cash

78 X. GKOUGKOUSI

flows. Aggregate earnings, however, also contain information about thegrowth of the economy, and thus about discount rates. Hence, the rela-tion between earnings changes and corporate bond returns can be weak oreven negative at the aggregate level.

Second, my analysis contributes to a growing body of literature that ex-amines the macroeconomic role of accounting information (e.g., Kothari,Lewellen, and Warner [2006], Anilowski, Feng, and Skinner [2007]).1

Macroeconomic information impacts investors’ consumption and asset al-location decisions as well as governments’ policies. The macroeconomicrole of accounting information, however, is largely unexplored. I con-tribute to this stream of literature by reconciling prior findings on the rela-tion between aggregate earnings changes, expected returns, and discountrate news (e.g., Kothari, Lewellen, and Warner [2006], Sadka and Sadka[2009]). In particular, I show that aggregate earnings changes contain bothan expected and a news component, and both of these components drivethe earnings-returns relation at the aggregate level down. Further, my anal-ysis promotes our understanding of the exact link between unexpected ag-gregate earnings changes and discount rate news (e.g., Kothari, Lewellen,and Warner [2006]). My findings suggest that unexpected aggregate earn-ings changes are positively related to changes in nominal interest rates andnegatively related to changes in default premia. I do not address, however,whether aggregate earnings move with nominal interest rates because theyare associated with real interest rates, inflation expectations, or both. Fi-nally, I contribute to the understanding of the aggregate earnings-returnsrelation for stocks (e.g., Kothari, Lewellen, and Warner [2006]). My resultssuggest that the negative relation between aggregate earnings changes andstock market returns is partially driven by a negative relation between ex-pected aggregate earnings changes and expected returns, and a positiverelation between unexpected aggregate earnings changes and changes innominal interest rates. Nevertheless, my contribution to the understand-ing of the aggregate earnings-returns relation for stocks is incomplete asmy analysis does not provide evidence on the relation between aggregateearnings changes and changes in equity risk premia.

2. Hypotheses Development

To explain the relation between aggregate earnings changes and cor-porate bond market returns, I use the return decomposition proposed

1 Studies that examine the macroeconomic role of aggregate accounting numbers includeAnilowski, Feng, and Skinner [2007] and Bonsall, Bozanic, and Fischer [2013], who examinethe relation between aggregate earnings guidance and stock market returns; Hirshleifer, Hou,and Teoh [2009], who examine the relation of aggregate accruals and aggregate cash flowsto stock market returns; Kang, Liu, and Qi [2010], who study the association of aggregatediscretionary accruals and aggregate normal accruals with stock market returns; and Guo andJiang [2011], who examine the relation between aggregate accruals and the conditional equitypremium.


by Campbell [1991].2 Campbell decomposes market returns (Rt) into ex-pected returns (Et−1[Rt]), news about cash flows (NCF), and news aboutdiscount rates (NDR):

Rt = Et−1[Rt ] + NCF − NDR . (1)

Consequently, the relation between aggregate earnings changes (�Xt)and market returns depends on the relation between aggregate earningschanges and expected returns, cash flow news, and discount rate news(Hecht and Vuolteenaho [2006]):

Cov{Rt ,�Xt } = Cov{Et−1[Rt ],�Xt } + Cov{NCF ,�Xt }− Cov{NDR ,�Xt }. (2)

Aggregate earnings changes can be decomposed into expected aggregateearnings changes (Et−1[�Xt]) and unexpected aggregate earnings changes(U�Xt):

�Xt = Et−1[�Xt ] + U �Xt . (3)

The expected component of aggregate earnings changes is by definitionorthogonal to the news component of market returns, and the unexpectedcomponent of aggregate earnings changes has by definition no relationto the expected component of market returns. So, equation (2) can berewritten as:

Cov{Rt ,�Xt } = Cov{Et−1[Rt ], Et−1[�Xt ]} + Cov{NCF , U �Xt }− Cov{NDR , U �Xt }. (4)

Hence, the relation between market returns and aggregate earningschanges depends on the relation between expected returns and ex-pected aggregate earnings changes (Cov{Et−1[Rt], Et−1[�Xt]}), the rela-tion between cash flow news and unexpected aggregate earnings changes(Cov{NCF, U�Xt}), and the relation between discount rate news and unex-pected aggregate earnings changes (Cov{NDR, U�Xt}).

The cash flow effect of aggregate earnings is positive because higher thanexpected aggregate earnings provide good news about future cash flowsand thus cause higher prices and higher returns (i.e., Cov{NCF, U�Xt} > 0).3

The relation between aggregate earnings changes and discount rates, how-ever, remains an open question.

Some authors argue that aggregate earnings changes are negatively re-lated to discount rates. Ball, Sadka, and Sadka [2009] and Sadka and Sadka

2 Even though the return decomposition proposed by Campbell [1991] was developed forstock market returns, it can also be applied to the corporate bond markets (e.g., Campbelland Ammer [1993], Chen and Zhao [2009]).

3 The positive autocorrelations of aggregate earnings changes for several quarters supportthe proposition of a positive covariance between cash flow news and unexpected aggregateearnings changes.

80 X. GKOUGKOUSI

[2009] suggest that aggregate earnings are highly predictable. High ex-pected aggregate earnings are associated with high expected economicgrowth. Assuming countercyclical risk premia (e.g., Fama and French[1989]), high expected economic growth results in lower risk aversion orrisk, and lower risk premia. As a result, the negative contemporaneous ag-gregate earnings-returns relation documented by Kothari, Lewellen, andWarner [2006] is attributed to a negative relation between expected aggre-gate earnings changes and expected returns (i.e., Cov{Et−1[Rt], Et−1[�Xt]}< 0).4

Others suggest that aggregate earnings changes are positively related todiscount rates. Patatoukas and Yan [2010] argue that aggregate earnings in-corporate new information. Higher than expected aggregate earnings pro-vide good news about future cash flows. Investors who wish to smooth theirconsumption borrow against their future income, and thus bid up interestrates and raise discount rates. Higher discount rates result, in turn, in lowerprices and lower returns. This argument is also in line with monetary policyinterventions. Governments and central banks increase interest rates whenearnings are higher than expected to avoid an overheating of the economy.Shivakumar [2007] also proposes a positive relation between unexpectedaggregate earnings changes and discount rate news and, more specifically,a positive relation between unexpected aggregate earnings changes andchanges in inflation expectations. The line of reasoning is that good newsabout aggregate earnings signals improvement in economic conditions, risein aggregate demand, and ultimately higher inflation. Academic literaturealso suggests a positive relation between unexpected aggregate earningschanges and changes in risk premia (Patatoukas [2013]). The idea is thatrisk-averse investors wish to consume more during economic expansionsand thus demand higher rates in return for their investment (Cochrane[2006]). Overall, the latter stream of literature attributes the negative ag-gregate earnings-returns relation documented by Kothari, Lewellen, andWarner [2006] to a positive relation between unexpected aggregate earn-ings changes and discount rate news (i.e., Cov{NDR, U�Xt} > 0).

Regardless of whether the former or the latter stream of literature iscorrect, the fact remains that the cash flow effect of aggregate earningsis positive, and the discount rate effect of aggregate earnings is negative.As a result, the cash flow effect and the discount rate effect of aggregateearnings move asset prices in opposite directions. In the case of stocks,the discount rate effect dominates the cash flow effect, and the aggregateearnings-returns relation is negative. The relation between aggregate earn-ings changes and aggregate bond returns, however, is not clear a priori.

There are three reasons the aggregate earnings-returns relation canbe different for bonds as compared to stocks. First, payments to

4 This theory is also in line with the findings of Chen [1991], who documents a negativerelation between expected growth in gross national product and stock market returns.


bondholders are predetermined and take priority over payments to stock-holders. Thus, corporate bond prices are less sensitive to cash flow changesthan are stock prices. Second, corporate bonds have shorter durations thanstocks. Therefore, they are less sensitive to discount rate changes than arestocks. Third, stocks and bonds are subject to different risk premia. Bondprices move primarily in response to changes in term and default premia,and stock prices move primarily in response to changes in equity risk pre-mia (e.g., Campbell and Ammer [1993], Fama and French [1993]). Hence,stocks and corporate bonds are differentially sensitive to the cash flow ef-fect and the discount rate effect of aggregate earnings, and the relationbetween aggregate earnings changes and corporate bond market returns isan empirical question.

The aggregate earnings-returns relation can also vary with corporatebond characteristics and, more specifically, with corporate bond credit rat-ings and corporate bond maturities. At the firm level, Easton, Monahan,and Vasvari [2009] show a higher earnings-returns relation for low-ratedthan for high-rated bonds. The reason is that the payoff for low-rated bondsis similar to a call option that is close to out-of-the-money, whereas thepayoff for high-rated bonds is similar to a call option that is deep in-the-money. Prices of out-of-the-money options are more sensitive to changes inthe value of the underlying asset than prices of in-the-money options. Ac-cordingly, low-rated bonds are more sensitive to changes in earnings thanhigh-rated bonds. There is no evidence that corporate bond maturity hasan impact on the earnings-returns relation at the firm level.

In the case of the aggregate earnings-returns relation, the impact of cor-porate bond credit ratings is not obvious ex ante. On the one hand, it is pos-sible that the aggregate earnings-returns relation will be lower in the caseof bonds with higher credit ratings. The reason is that high-rated bondsare less sensitive to changes in cash flows than low-rated bonds (Easton,Monahan, and Vasvari [2009]). Hence, there should be less of a cash floweffect for high-rated bonds. At the same time, Shivakumar [2007] and Pata-toukas and Yan [2010] suggest that aggregate earnings relate positively tointerest rates. Interest rates have a negative relation to credit spreads, witha more pronounced negative relation for low-rated than high-rated bonds(Duffee [1998]). The negative relation between interest rates and creditspreads attenuates the discount rate effect of aggregate earnings and moreso for low-rated than for high-rated bonds. Hence, the discount rate effectof aggregate earnings should be stronger for high-rated bonds. The weakercash flow effect and the stronger discount rate effect of aggregate earningsfor high-rated than low-rated bonds can lead to a lower aggregate earnings-returns relation for bonds with higher credit ratings. On the other hand,it is possible that the aggregate earnings-returns relation will be higher forbonds with higher credit ratings because high-rated bonds are less sensitiveto discount rate changes than low-rated bonds (Fama and French [1989]).

With regard to the impact of bond maturity on the aggregate earnings-returns relation, long-term bonds are by construction more sensitive to

82 X. GKOUGKOUSI

changes in discount rates than short-term bonds. In addition, long-termbonds are less sensitive to changes in cash flows than short-term bondsbecause cash flow shocks are less persistent than discount rate shocks(Campbell [1991]). Therefore, I expect a lower aggregate earnings-returnsrelation for long-term than short-term bonds.

3. Data and Summary Statistics

My sample consists of all firms with data available in Compustat NorthAmerica Fundamentals Quarterly from January 1973 through December2010.5 I drop firms that are not listed on the NYSE, AMEX, or NASDAQ tomake my results more comparable to other studies. I exclude firms with fis-cal year-ends other than March, June, September, and December to betteralign quarterly aggregate earnings changes with quarterly aggregate bondreturns. I drop firms with earnings announcement dates more than threemonths after a quarter’s end to exclude the likelihood of stale earnings fig-ures. I also delete the top and bottom 0.5% of firms ranked by earningschanges each quarter to mitigate the influence of outliers.

Similar to Kothari, Lewellen, and Warner [2006], I measure aggregateearnings changes (�E/A) as the value-weighted average of firm-specificquarterly earnings changes.6 Firm-specific earnings changes are calculatedas earnings in the current quarter minus earnings four quarters ago scaledby lagged total assets. The scaling factor is lagged by four quarters. I usetotal assets as the scaling factor in the primary analysis because total assetsare always positive, unlike earnings or book value of equity, which can takenegative values. Nevertheless, the results are robust to using alternative de-flators. I measure earnings as income before extraordinary items. Thesesample selection criteria and data requirements yield a sample of 401,822firm-quarter observations.

To measure quarterly corporate bond market returns, I use the totalreturns of the value-weighted Bank of America Merrill Lynch U.S. Cor-porate Bond Indices downloaded from Bloomberg. Total return is thesum of the price return, the accrued interest return, and the couponreturn. I use 13 corporate bond indices with different maturities anddifferent credit ratings (R 1-3 AAA-AA, R 1-3 A-BBB, R 3-5 AAA-AA, R 3-5 A-BBB, R 5-10 AAA-AA, R 5-10 A-BBB, R 15+ AAA-AA, R 15+ A-BBB,R all invest grade, R all BB, R all B, R all CCC, and R all high yield).

5 I include all firms with data available in Compustat North America FundamentalsQuarterly—regardless of whether the firms have publicly traded bonds or not—to capturethe earnings-generating ability of all the firms in the economy.

6 The only difference in the definition of aggregate earnings changes is that Kothari,Lewellen, and Warner [2006] scale aggregate earnings changes by lagged earnings, book valueof equity, or price, whereas I scale aggregate earnings changes by lagged total assets in my mainanalysis.


The numbers in the indices represent the remaining maturities of thebonds that constitute the indices, and the letters represent the credit rat-ings. Each letter represents all subcategories within each credit rating cate-gory. For example, the index R 1-3 AAA-AA includes bonds with AAA, AA+,AA, and AA− credit ratings. Credit ratings are the average of the individ-ual bond ratings provided by Moody’s, Standard & Poor’s, and Fitch. Theindices of bonds with credit ratings BBB− and above are the investment-grade corporate bond indices, and those of bonds with credit ratings BB+and below are the high-yield corporate bond indices. “All” stands for “allavailable maturities.” For example, R all invest grade and R all high yieldare indices of bonds of all available maturities that are investment gradeand high yield, respectively. Bank of America Merrill Lynch provides fewerhigh-yield than investment-grade indices, and it provides indices with vary-ing maturities only in the case of the investment-grade bonds. Thus, I usenine investment-grade and only four high-yield indices for the analysis.

There are an average of 776 issues in the indices and each firm is in-cluded in each index, with three issues in the case of the investment-gradeindices and fewer than two issues in the case of the high-yield indices, on av-erage. Thus, the corporate bond indices used in the analysis are well diver-sified. The indices are rebalanced monthly, so the average remaining matu-rities and the average credit ratings remain fairly stable through time. Bondilliquidity is not an issue in the analysis because I use quarterly frequencydata. Further, the indices cover bonds with large issue size, and bond is-sue size is positively associated with bond liquidity (e.g., Hong and Warga[2000]). In particular, the investment-grade (high-yield) bonds in the in-dices have a minimum outstanding value equal to $250 ($100) million.

For estimation of the models, I use ordinary least squares and Newey-West heteroscedasticity- and autocorrelation-consistent standard errors. Iset the bandwidth of the Bartlett kernel to the integer value of 4 × ( T

100 )29 ,

where T is the number of observations used in the time-series regressions(Newey and West [1987, 1994]).

Table 1 reports summary statistics for the main variables used in theregressions. The mean quarterly returns of the corporate bond indicesrange from 1.60% to 2.28%. As expected, bonds with longer maturities andlower credit ratings earn, on average, higher returns, but the differencesare not statistically significant (Cornell and Green [1991]). In line withBlume, Keim, and Patel [1991], low-rated investment-grade bond returnshave lower standard deviations than high-rated investment-grade bond re-turns, but the differences are not statistically significant.

The first-order autocorrelations of the high-yield corporate bond indicesare higher than those of the investment-grade corporate bond indices, pre-sumably due to less frequent trading in the high-yield corporate bond mar-ket. The number of observations for the various corporate bond indicesranges from 56 to 152 because different indices have different inceptiondates. The mean quarterly change in the aggregate earnings is equal to0.15%, similar to Kothari, Lewellen, and Warner [2006]. The aggregate

84 X. GKOUGKOUSI

T A B L E 1Summary Statistics

Panel A: Univariate statisticsStandard

Mean Median Deviation Skewness Kurtosis Autocorrelation N

R 1-3 AAA-AA 1.90% 1.59% 1.96% 1.872 10.716 0.056 140R 1-3 A-BBB 1.99% 1.78% 1.86% 1.143 9.259 0.190 140R 3-5 AAA-AA 2.02% 1.84% 2.72% 1.190 8.645 −0.079 140R 3-5 A-BBB 2.07% 1.93% 2.69% 1.062 7.741 0.069 135R 5-10 AAA-AA 2.09% 1.78% 3.71% 0.616 5.308 −0.086 152R 5-10 A-BBB 2.14% 1.82% 3.63% 0.678 5.856 0.066 152R 15+ AAA-AA 2.23% 1.77% 5.45% 0.572 5.338 −0.128 152R 15+ A-BBB 2.28% 1.97% 5.17% 0.534 5.572 −0.024 152R all invest grade 2.09% 1.81% 4.10% 0.757 6.889 −0.004 152R all BB 1.90% 1.79% 4.19% −0.204 6.232 0.323 56R all B 1.60% 1.86% 5.54% −0.181 7.640 0.313 56R all CCC 2.24% 2.00% 9.98% 0.746 7.089 0.387 56R all high yield 2.26% 2.38% 4.90% 0.243 8.452 0.340 97�E/A 0.15% 0.21% 0.44% −1.916 10.804 0.697 152

Panel B: Correlation coefficientsR all R all

invest grade high yield �E/A

R all invest grade 1.00 0.46∗∗∗ −0.19∗

R all high yield 0.50∗∗∗ 1.00 0.01�E/A −0.20∗∗ 0.14 1.00

This table presents summary statistics of the main variables of the regression models. Panel Apresents univariate statistics and panel B presents Pearson (Spearman) correlation coefficients below(above) the diagonal. R 1-3 AAA-AA, R 1-3 A-BBB, R 3-5 AAA-AA, R 3-5 A-BBB, R 5-10 AAA-AA, R 5-10 A-BBB, R 15+ AAA-AA, R 15+ A-BBB, R all invest grade, R all BB, R all B, R all CCC, and R all high yield arequarterly total returns of the various corporate bond indices. Total return is the sum of the price return, theaccrued interest return, and the coupon return. The numbers in the names of the corporate bond indicesrepresent the remaining maturities of the bonds in the indices, and the letters represent the credit ratings.“All” stands for “all available maturities.” �E/A is the quarterly aggregate earnings changes measured asthe value-weighted average of firm-specific earnings changes. Firm-specific earnings change is the season-ally differenced income before extraordinary items scaled by lagged total assets. The sample extends fromJanuary 1973 through December 2010. ∗, ∗∗, and ∗∗∗ denote significance at the 1%, 5%, and 10% levels,respectively (two-tailed test).

earnings changes exhibit large time-series variation. Further, some variablesare persistent, but the augmented Dickey-Fuller test rejects the null hypoth-esis of a unit root for all the variables at the 1% level.

As panel B of table 1 shows, the quarterly returns of the investment-gradeand the high-yield indices are positively and significantly correlated at the1% level. Thus, there is a high degree of commonality in the returns of thevarious corporate bond indices. Aggregate earnings changes are negativelyand significantly related to the returns of the investment-grade corporatebond index, and not related to the returns of the high-yield corporate bondindex. These correlation coefficients contrast with the firm-level findingsof a positive relation between earnings changes and corporate bond re-turns (Easton, Monahan, and Vasvari [2009]). Further, the correlation co-efficients provide preliminary evidence that the aggregate earnings-returnsrelation depends on corporate bond characteristics.


4. Results

I examine the relation between aggregate earnings changes and corpo-rate bond market returns, as well as the impact of corporate bond charac-teristics on the aggregate earnings-returns relation in section 4.1. I examinethe drivers of the aggregate earnings-returns relation for corporate bondsin section 4.2.

4.1 CORPORATE BOND MARKET RETURNS AND AGGREGATE EARNINGSCHANGES

Table 2 presents the results of regressions of quarterly corporate bondmarket returns on contemporaneous aggregate earnings changes. As panelA shows, aggregate earnings changes are significantly and negatively relatedto the returns of investment-grade corporate bond indices; the regressioncoefficients of aggregate earnings changes range from −0.79 to −3.09 witht-statistics between −1.75 and −2.82. Further, as panel B shows, aggregateearnings changes are significantly positively related to returns of the high-yield corporate bond indices in two of four regressions; the slopes on ag-gregate earnings changes range from 0.38 to 3.29 with t-statistics between0.43 and 1.81. The adjusted R2 of the regressions range from −1.51% to5.50% and are presumably low because of the confounding impact of thecash flow effect and the discount rate effect of aggregate earnings. Theselow adjusted R2 are also similar to those of earlier studies (e.g., Kothari,Lewellen, and Warner [2006]).

The relation between aggregate earnings changes and corporate bondmarket returns is statistically as well as economically significant; a two-standard-deviation positive shock to the aggregate earnings changes cor-responds to a 0.69–2.70% reduction in the quarterly returns of theinvestment-grade corporate bond indices, and a 0.33–2.88% increase in thequarterly returns of the high-yield corporate bond indices. The results oftable 2, panels A and B suggest that aggregate earnings comove with cashflows and discount rates. The discount rate effect of aggregate earningsdominates the cash flow effect in the case of the investment-grade indices,and the earnings-returns relation is negative. But the cash flow effect dom-inates the discount rate effect in the case of the high-yield indices, and theearnings-returns relation is positive.7

7 I perform three additional tests. First, I examine whether the aggregate earnings-returnsrelation depends on the type of information incorporated into earnings. Easton, Monahan,and Vasvari [2009] show a lower firm-specific earnings-returns relation when earnings conveygood news. The reason is that bonds have limited upside potential, so the cash flow effectof earnings is less pronounced when earnings news is good. I also expect a lower aggregateearnings-returns relation when earnings news is good. In line with my expectations, I find alower relation in the case of investment-grade but not high-yield bonds. Second, I examinethe relation between aggregate earnings changes and corporate bond market returns overtime. Francis and Schipper [1999], Lev and Zarowin [1999], and Ryan and Zarowin [2003]

86 X. GKOUGKOUSI

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88 X. GKOUGKOUSI

In table 2, panels C and D, I examine the impact of corporate bondcharacteristics on the aggregate earnings-returns relation. To this end, Icompare the coefficients of the aggregate earnings changes from panelsA and B either for pairs of indices with the same maturity but differentcredit ratings or for pairs of indices with the same credit rating but dif-ferent maturities. Table 2, panel C presents the differences in the coeffi-cients of the aggregate earnings changes for the pairs with the same matu-rity but different credit ratings. All differences are negative, but only thedifference in the coefficients of the aggregate earnings changes for thelast pair of indices is statistically significant. The insignificant differences ofthe first five pairs are seen presumably because these pairs capture differ-ences in the coefficients of the aggregate earnings changes among eitherinvestment-grade or high-yield indices, whereas the last pair captures thedifference in the coefficients for the investment-grade versus the high-yieldindices.

Figure 1 plots regression coefficients of the aggregate earnings changesfrom panels A and B of table 2 for corporate bond indices with similarmaturities but different credit ratings. The regression coefficients declinemonotonically as the corporate bond credit ratings increase. The findingsof table 2, panel C and figure 1 provide support for the hypothesis that low-rated bonds are more sensitive to cash flow changes than high-rated bonds(Easton, Monahan, and Vasvari [2009]). Further, my findings are in linewith the proposition that the discount rate effect of aggregate earnings isweaker for low-rated bonds, because there is a more pronounced negativerelation between interest rates and credit spreads for bonds with low creditratings (Duffee [1998]).

Panel D of table 2 shows the differences in the coefficients of aggregateearnings changes for pairs of indices with the same credit rating but differ-ent maturities. The differences in the coefficients are, as expected, positiveand statistically different from zero at the 15% level or lower. Figure 2 plotsthe coefficients of the aggregate earnings changes from table 2, panel Afor corporate bond indices with the same credit ratings but different ma-turities. There is a lower aggregate earnings-returns relation for long-termthan short-term bonds. These findings are in line with the proposition that

document a decline in the contemporaneous firm-specific earnings-returns relation over time.My tests also provide some evidence of a weakening earnings-returns relation over time atthe aggregate level. Third, I decompose aggregate earnings changes into aggregate accru-als changes and aggregate cash flow changes, and examine their relation to aggregate bondreturns. I find that both aggregate accruals and aggregate cash flow changes are negatively re-lated to investment-grade corporate bond market returns. I also find that aggregate accrualschanges have no relation and aggregate cash flow changes have a positive relation to high-yield corporate bond market returns. My findings are contrary to the findings of Hirshleifer,Hou, and Teoh [2009], who show that aggregate accruals changes are negatively related andaggregate cash flow changes are positively related to stock market returns.


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90 X. GKOUGKOUSI

long-term bonds are more sensitive to discount rate changes than short-term bonds.8

To sum up, I find a negative aggregate earnings-returns relation forinvestment-grade bonds and a positive aggregate earnings-returns relationfor high-yield bonds. The aggregate earnings-returns relation is lower forhigh-rated and long-term bonds. These results differ from firm-level find-ings of a positive relation between earnings changes and corporate bondreturns, and from the finding that the earnings-returns relation for corpo-rate bonds at the firm level depends only on the corporate bond creditratings. My findings suggest that aggregate earnings move with cash flowsand discount rates.

4.2 AGGREGATE EARNINGS, CASH FLOWS, AND DISCOUNT RATES

Next, I examine the information content of aggregate earnings changesby conducting four sets of tests. First, I regress corporate bond market re-turns on aggregate earnings changes and lagged stock market returns tostudy the relation between aggregate earnings changes, expected returns,and discount rate news. In this and all subsequent analysis, I use laggedstock market returns to control for the expected component of aggregateearnings changes. If expected aggregate earnings changes are negativelyrelated to expected returns (e.g., Sadka and Sadka [2009]), then the con-temporaneous aggregate earnings-returns relation will increase after con-trolling for lagged equity market returns. That is, the regression coeffi-cients of aggregate earnings changes will become less negative or morepositive after controlling for the stock market returns. Further, if unex-pected aggregate earnings changes are positively related to discount ratenews (e.g., Kothari, Lewellen, and Warner [2006]), then the negative ag-gregate earnings-returns relation for investment-grade bonds will persisteven after controlling for the expected component of aggregate earningschanges.

Second, I regress one-quarter-forward corporate bond market returns onaggregate earnings changes and lagged equity market returns to examinethe relation between unexpected aggregate earnings changes and discountrate news. If unexpected aggregate earnings changes are positively relatedto discount rate news, then unexpected aggregate earnings changes shouldbe positive predictors of corporate bond market returns.

Third, I study the relation between unexpected aggregate earningschanges, changes in nominal interest rates, and changes in risk premia.

8 The indices in my analysis include callable bonds that are at least one year from the firstcall date. Even though call options are more common for long-term than short-term bonds(Kish and Livingston [1992]), it is not the differences in the optionality, but the differencesin the maturity of the corporate bond indices that drive the results of table 2, panel D. Thereason is that embedded call options make corporate bond prices less sensitive to discount ratechanges. Therefore, the higher percentage of callable bonds in the long-term indices shouldweaken the discount rate effect of aggregate earnings and should ultimately have a positiveimpact on the aggregate earnings-returns relation.


To this end, I regress excess corporate bond market returns on aggregateearnings changes and lagged equity market returns. If unexpected aggre-gate earnings changes are positively related to changes in nominal interestrates (e.g., Shivakumar [2007]), then the aggregate earnings-returns rela-tion will increase after controlling for nominal interest rates. In addition,if unexpected aggregate earnings changes are positively related to risk pre-mia (e.g., Patatoukas [2013]), then the negative aggregate earnings-returnsrelation for investment-grade bonds will persist even after controlling fornominal interest rates.

And fourth, I study the relation between unexpected aggregate earn-ings changes, cash flow news, and changes in default premia. To this end,I regress corporate bond market returns on aggregate earnings changes,changes in credit default swap (CDS) spreads, and lagged equity marketreturns. Changes in CDS spreads are negatively related to news about cashflows and positively related to changes in default premia. If aggregate earn-ings news are positively related to cash flow news and negatively related tochanges in default premia (e.g., Callen, Livnat, and Segal [2009]), thenthe aggregate earnings-returns relation will decrease after controlling forchanges in CDS spreads.

Table 3 presents the results of the regressions of corporate bond marketreturns on aggregate earnings changes and lagged stock market returns(R equity). Stock market returns are the quarterly returns, including distri-butions, of the equity market index downloaded from CRSP. I use laggedstock market instead of lagged corporate bond market returns to controlfor expected aggregate earnings changes because Downing, Underwood,and Xing [2009], among others, suggest that the stock market is informa-tionally more efficient than the corporate bond market. In this and all sub-sequent analysis, I use t-statistics to compare the slopes on aggregate earn-ings changes across models. Controlling for lagged stock market returnsraises the regression coefficients of aggregate earnings changes in table 3compared to table 2 in 12 of the 13 regressions, and these increases arestatistically significant at the 15% level or lower in 2 of the 13 regressions.Nevertheless, even after controlling for lagged equity market returns, theaggregate earnings changes remain negatively and significantly related toinvestment-grade corporate bond market returns and positively and signifi-cantly related to high-yield corporate bond market returns. For investment-grade bonds, the coefficients on aggregate earnings changes range from−0.67 to −2.96 with t-statistics between −1.44 and −3.09, and for high-yield bonds, the slopes on aggregate earnings changes vary considerablyfrom 2.15 to 9.31 with t-statistics between 1.46 and 4.05.

The finding that the aggregate earnings-returns relation increases af-ter controlling for expected aggregate earnings changes suggests that theaggregate earnings-returns relation is partially driven by a negative rela-tion between expected aggregate earnings changes and expected returns(Sadka and Sadka [2009]). The negative relation between expected ag-gregate earnings changes and expected returns, however, cannot fully

92 X. GKOUGKOUSI

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s,an

dth

ele

tter

sre

pres

entt

he

cred

itra

tin

gs.“

All”

stan

dsfo

r“a

llav

aila

ble

mat

urit

ies.

”�

E/A

isth

equ

arte

rly

aggr

egat

eea

rnin

gsch

ange

sm

easu

red

asth

eva

lue-

wei

ghte

dav

erag

eof

firm

-spe

cifi

cea

rnin

gsch

ange

s.Fi

rm-s

peci

fic

earn

ings

chan

geis

the

seas

onal

lydi

ffer

ence

din

com

ebe

fore

extr

aord

inar

yit

ems

scal

edby

lagg

edto

tala

sset

s.R

equi

tyis

the

quar

terl

yre

turn

ofth

eeq

uity

mar

ket

inde

x.T

he

sam

ple

exte

nds

from

Jan

uary

1973

thro

ugh

Dec

embe

r20

10.

Ius

eor

din

ary

leas

tsq

uare

sfo

rth

eca

lcul

atio

nof

the

regr

essi

onco

effi

cien

tsan

dth

eN

ewey

-Wes

th

eter

osce

dast

icit

y-an

dau

toco

rrel

atio

n-

con

sist

ent

stan

dard

erro

rsw

ith

four

lags

.t-s

tati

stic

sar

ein

pare

nth

eses

belo

wth

eco

effi

cien

tes

tim

ates

.F-s

tati

stic

ste

stth

en

ullh

ypot

hes

isth

atth

ere

gres

sion

coef

fici

ents

are

join

tly

equa

lto

zero

.∗∗∗ ,

∗∗,a

nd

∗de

not

esi

gnifi

can

ceat

the

1%,5

%,a

nd

10%

leve

ls,r

espe

ctiv

ely

(tw

o-ta

iled

test

).

94 X. GKOUGKOUSI

explain the contemporaneous negative aggregate earnings-returns rela-tion for investment-grade bonds. Aggregate earnings changes remain sig-nificantly negatively related to investment-grade corporate bond marketreturns even after controlling for the expected component of aggregateearnings changes. This latter finding suggests that the earnings-returns re-lation at the aggregate level is also driven by the news component of aggre-gate earnings changes and, more specifically, by a positive relation betweenunexpected aggregate earnings changes and discount rate news (Kothari,Lewellen, and Warner [2006]). My results reconcile the findings of Sadkaand Sadka [2009] and Kothari, Lewellen, and Warner [2006] as they sug-gest that both the expected and the news component of aggregate earningschanges drive the aggregate earnings-returns relation down.

Next, I focus on the information content of the unexpected componentof aggregate earnings changes. Table 4 presents the results of the regres-sions of one-quarter-forward corporate bond market returns on aggregateearnings changes and lagged equity market returns. The regression coeffi-cients of the aggregate earnings changes are not statistically distinguishablefrom zero in 12 of the 13 regressions, and the overall F-statistics are in-significant in all the 13 regressions. These findings cast doubt on the ideathat aggregate earnings convey new information and are consistent withthe theory put forward by Ball, Sadka, and Sadka [2009] and Sadka andSadka [2009] that aggregate earnings changes are highly predictable. Still,the results of table 4 should be interpreted with caution because realizedreturns are a poor proxy for expected returns; news about cash flows andnews about discount rates might not cancel out on average (Elton [1999]).

Table 5 presents the results of the regressions of quarterly excesscorporate bond market returns on aggregate earnings changes andlagged equity market returns. The excess bond market returns (ExcessR 1-3 AAA-AA, Excess R 1-3 A-BBB, Excess R 3-5 AAA-AA, Excess R 3-5 A-BBB, Excess R 5-10 AAA-AA, Excess R 5-10 A-BBB, Excess R 15+ AAA-AA, Ex-cess R 15+ A-BBB, Excess R all invest grade, Excess R all BB, Excess R all B,Excess R all CCC, and Excess R all high yield) are equal to the difference be-tween the total returns of the corporate bond indices and the total returnsof a key rate duration-matched basket of U.S. government securities. Theadvantage of using excess corporate bond returns to study the relation be-tween unexpected aggregate earnings, nominal interest rates, and risk pre-mia is that corporate bonds have finite maturities and predetermined pay-ments. As a result, I can accurately match the duration of the corporatebonds to the duration of the risk-free assets, and isolate the component ofcorporate bond market returns that is related to changes in interest ratesfrom the return component that is related to changes in risk premia.

As table 5 shows, aggregate earnings changes become positively and sig-nificantly related to the investment-grade corporate bond market returns infive of the nine regressions, and remain positively and significantly relatedto the high-yield corporate bond market returns in all four regressions.In the case of investment-grade bonds, the slopes on aggregate earnings


TA

BL

E4

Forw

ard

Cor

pora

teB

ond

Mar

ketR

etur

nsan

dA

ggre

gate

Earn

ings

Cha

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3-5

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(7.4

33)

(7.0

87)

(6.3

61)

(5.9

28)

(5.6

83)

(5.4

00)

(4.5

97)

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02)

�E/

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−0.5

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−0.6

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−0.7

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271

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(−1.

636)

(−1.

897)

(−1.

230)

(−1.

468)

(−0.

330)

(−1.

207)

(0.2

35)

(−0.

652)

(−1.

116)

Req

uity

t−1

−0.0

13−0

.015

−0.0

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(−0.

821)

(−0.

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(−1.

287)

(−1.

167)

(−1.

678)

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Req

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140

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(Con

tinue

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96 X. GKOUGKOUSI

TA

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tinue

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Req

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ple

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from

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1973

thro

ugh

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embe

r20

10.

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din

ary

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tsq

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atio

nof

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th

eter

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dast

icit

y-an

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con

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ent

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four

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ates

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stic

ste

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en

ullh

ypot

hes

isth

atth

ere

gres

sion

coef

fici

ents

are

join

tly

equa

lto

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.∗∗∗ ,

∗∗,a

nd

∗de

not

esi

gnifi

can

ceat

the

1%,5

%,a

nd

10%

leve

ls,r

espe

ctiv

ely

(tw

o-ta

iled

test

).


TA

BL

E5

Exce

ssC

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rate

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dM

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rega

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Req

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Req

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98 X. GKOUGKOUSI

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Req

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Req

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turn

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corp

orat

ebo

nd

indi

ces.

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ess

retu

rnis

the

diff

eren

cebe

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nth

eto

talr

etur

nof

the

corp

orat

ebo

nd

inde

xan

dth

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talr

etur

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ake

yra

tedu

rati

on-m

atch

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sket

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vern

men

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urit

ies.

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en

umbe

rsin

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nam

esof

the

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nd

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ces

repr

esen

tth

ere

mai

nin

gm

atur

itie

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bon

dsin

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indi

ces,

and

the

lett

ers

repr

esen

tth

ecr

edit

rati

ngs

.“A

ll”st

ands

for

“all

avai

labl

em

atur

itie

s.”�

E/A

isth

equ

arte

rly

aggr

egat

eea

rnin

gsch

ange

sm

easu

red

asth

eva

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wei

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erag

eof

firm

-spe

cifi

cea

rnin

gsch

ange

s.Fi

rm-s

peci

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ings

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geis

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seas

onal

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ffer

ence

din

com

ebe

fore

extr

aord

inar

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cale

dby

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edto

tala

sset

s.R

equi

tyis

the

quar

terl

yre

turn

ofth

eeq

uity

mar

keti

nde

x.T

he

sam

ple

exte

nds

from

Jan

uary

1997

thro

ugh

Dec

embe

r20

10.I

use

ordi

nar

yle

ast

squa

res

for

the

calc

ulat

ion

ofth

ere

gres

sion

coef

fici

ents

and

the

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ey-W

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het

eros

ceda

stic

ity-

and

auto

corr

elat

ion

-con

sist

ent

stan

dard

erro

rsw

ith

four

lags

.t-s

tati

stic

sar

ein

pare

nth

eses

belo

wth

eco

effi

cien

tes

tim

ates

.F-s

tati

stic

ste

stth

en

ull

hyp

oth

esis

that

the

regr

essi

onco

effi

cien

tsar

ejo

intl

yeq

ual

toze

ro.∗∗

∗ ,∗∗

,an

d∗

den

ote

sign

ifica

nce

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e1%

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changes range from 0.24 to 2.46 with t-statistics between 0.99 and 2.04, andin the case of high-yield bonds, the slopes on aggregate earnings changesrange from 4.07 to 10.53 with t-statistics between 1.97 and 3.88. Controllingfor nominal interest rates increases the regression coefficients of the aggre-gate earnings changes in all 13 regressions and the increases are statisticallysignificant at the 5% level or lower in 9 of the 13 regressions.9 In the case ofinvestment-grade bonds, the overall F-statistics are insignificant in all nineregressions, and in the case of high-yield bonds, they are significant at the5% level or lower in three of the four regressions. The number of avail-able observations decreases to 56 because Bank of America Merrill Lynchprovides data on excess returns only from January 1997 to the present.

The results of table 5 are in line with Kothari, Lewellen, and Warner[2006], who find a positive relation between aggregate earnings changesand changes in the one-year T-bill rate. My results provide support forthe idea that unexpected aggregate earnings changes are positively relatedto changes in nominal interest rates (e.g., Shivakumar [2007]), but theydo not address whether aggregate earnings move with nominal interestrates because they comove with real interest rates, inflation expectations,or both. Further, my results do not support the idea of a positive relationbetween unexpected aggregate earnings changes and changes in risk pre-mia (e.g., Patatoukas [2013]), as the relation between aggregate earningschanges and investment-grade corporate bond market returns becomespositive after controlling for nominal interest rates.

Table 6 presents the results of the regressions of corporate bond marketreturns on aggregate earnings changes, contemporaneous changes in thespreads of a CDS index, and lagged stock market returns. The changes inCDS spreads (�CDS) are quarterly changes in the spreads of the MarkitNorth America Investment Grade five-year CDS index downloaded fromBloomberg. I use the spreads of the five-year CDS index because the five-year issues are the most liquid issues. The number of available observationsdrops to 25 because the data for the CDS index spreads are available onlysince October 2004.

As table 6 shows, controlling for changes in CDS spreads reduces the sizeof the coefficients on aggregate earnings changes in 12 of the 13 regres-sions, and these declines are statistically different from zero in 4 of the 13regressions at the 10% level or lower.10 For investment-grade (high-yield)bonds, the coefficients on aggregate earnings changes range from −1.34

9 The increases in the coefficients of the aggregate earnings changes in table 5 comparedto table 3 are not due to the different sample periods used in the regressions. My conclusionis the same when I use the same sample period for the regressions with excess returns and theregressions with total returns as dependent variables.

10 The declines in the coefficients of the aggregate earnings changes in table 6 comparedto table 3 are not due to the different sample periods used in the regressions. My conclusionis similar when I use the same sample period for the regressions with and without changes inCDS spreads as control variables.

100 X. GKOUGKOUSI

TA

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lett

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ecr

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ngs

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ll”st

ands

for

“all

avai

labl

em

atur

itie

s.”

�E/

Ais

the

quar

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yag

greg

ate

earn

ings

chan

ges

mea

sure

das

the

valu

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eigh

ted

aver

age

offi

rm-s

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ings

chan

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ese

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ally

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inco

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befo

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arte

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the

spre

adof

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five

-yea

rC

DS

inde

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equi

tyis

the

quar

terl

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turn

ofth

eeq

uity

mar

ket

inde

x.T

he

sam

ple

exte

nds

from

Oct

ober

2004

thro

ugh

Dec

embe

r20

10.I

use

ordi

nar

yle

ast

squa

res

for

the

calc

ulat

ion

ofth

ere

gres

sion

coef

fici

ents

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the

coef

fici

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esti

mat

es.F

-sta

tist

ics

test

the

nul

lhyp

oth

esis

that

the

regr

essi

onco

effi

cien

tsar

ejo

intl

yeq

ualt

oze

ro.∗∗

∗ ,∗∗

,an

d∗

den

ote

sign

ifica

nce

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,5%

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d10

%le

vels

,res

pect

ivel

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wo-

taile

dte

st).

102 X. GKOUGKOUSI

to −12.19 (2.34 to 3.75) with t-statistics between −1.21 and −3.49 (0.78and 1.14). The explanatory power of the models increases when addingchanges in the CDS spreads as independent variable; the adjusted R2 rangefrom 1.22% to 43.20% for the investment-grade indices and from 47.43% to63.38% for the high-yield indices. The findings of table 6 provide evidenceof a positive relation between unexpected aggregate earnings changes andcash flow news, and a negative relation between unexpected aggregateearnings changes and changes in default premia.

My results are consistent with Kothari, Lewellen, and Warner [2006],who document a negative relation between aggregate earnings changesand changes in default spreads, and they are contrary to the assertion ofBali, Demirtas, and Tehranian [2008] that the cash flow component of firm-specific earnings is diversified away with aggregation. Further, my findingsare in line with Callen, Livnat, and Segal [2009], who document a negativerelation between firm-specific earnings changes and firm-specific changesin CDS spreads.

To summarize, the results in tables 3–6 suggest that aggregate earningschanges have an expected component and a news component. The ex-pected component of aggregate earnings changes is negatively related toexpected returns, and the news component is positively related to cashflow news and to changes in nominal interest rates, and negatively relatedto changes in default premia. My results partially explain the finding inKothari, Lewellen, and Warner [2006] of a negative relation between ag-gregate earnings changes and stock market returns. My tests, however, donot provide evidence on the relation between aggregate earnings changesand changes in equity risk premia, and so further analysis is necessary tofully understand the aggregate earnings-returns relation for stocks.

5. Untabulated Robustness Tests

My findings are robust to several alternative specifications for the aggre-gate earnings changes. First, the findings are similar when I scale the firm-specific earnings changes by lagged market value of equity, lagged absoluteearnings, lagged absolute book value of equity, and lagged enterprise valueinstead of lagged total assets. I define enterprise value as the sum of themarket value of equity plus the book value of debt.

Second, my results are robust to the measurement of earnings as oper-ating income after depreciation instead of earnings before extraordinaryitems. I use earnings before extraordinary items in the main analysis be-cause operating income after depreciation does not account for taxes, non-operating income, special items, and minority interests. Moreover, only apercentage of interest expense, which is included in operating income butis excluded from net income, accrues to corporate bondholders, whereasthe rest relates to repayments of bank loans, charges relating to leases,expenses related to the issuance of debt, factoring charges, and interestexpenses on deferred compensation.


Third, the findings are robust to defining aggregate earnings changes asthe equal-weighted instead of the value-weighted average of firm-specificearnings changes. I use value-weighted aggregate earnings changes in themain analysis because the corporate bond indices provided by Bank ofAmerica Merrill Lynch are also value weighted.

Fourth, the results are similar when I calculate firm-specific earningschanges as firm-specific errors in analysts’ forecasts instead of seasonally dif-ferenced firm-specific earnings. I define analyst forecast errors as reportedearnings minus the median of analyst earnings forecasts announced at theend of the previous quarter scaled by lagged total assets. I downloaded an-alyst forecasts from IBES.

Fifth, the results are robust to measuring aggregate earnings changesas the seasonal difference in the sum of quarterly firm-specific earnings.More specifically, aggregate earnings changes are defined as the sum offirm-specific earnings in the current quarter minus the sum of firm-specificearnings four quarters ago. I divide the sum of firm-specific earnings eachquarter by the number of available observations to control for the fact thatdata coverage changes through time. And sixth, my results are robust toshifting the corporate bond return window one and two months forward toinclude the earnings announcement date in the calculation of the quarterlyreturns.

My findings are also robust to measuring the aggregate bond returns us-ing the Barclays Capital U.S. Corporate Bond Indices downloaded fromDatastream. Further, the main inferences remain unchanged when I useannual instead of quarterly data, when I impose no sample selection cri-teria, when I match the firms of the earnings sample to the firms of thecorporate bond sample, and when I only include firms with publicly tradedbonds in my analysis. Following Faulkender and Petersen [2006], I definefirms with publicly traded bonds as firms with credit ratings.

Moreover, the relation between aggregate earnings changes and corpo-rate bond market returns is independent of the state of the economy. I mea-sure the state of the economy using the recession indicator downloadedfrom the Federal Reserve Bank of St. Louis Economic Data database.Finally, Jorgensen, Li, and Sadka [2011] show that aggregate earningschanges are unusually low in 2001 and unusually high in 2003 becauseof the implementation of SFAS 142. These unusual levels of aggregateearnings changes might conceivably distort the aggregate earnings-returnsrelation. Nevertheless, the results of my analysis are not sensitive to theexclusion of the period 2001–2003.

6. Conclusion

I study the relation between aggregate earnings changes and corporatebond market returns, and I find that aggregate earnings changes havea negative relation with investment-grade corporate bond market returnsand a positive relation with high-yield corporate bond market returns. The

104 X. GKOUGKOUSI

aggregate earnings-returns relation is lower for bonds with higher creditratings and longer maturities. I also examine the relation between aggre-gate earnings changes and the various components of corporate bond mar-ket returns. I find that expected aggregate earnings changes are negativelyrelated to expected returns, and unexpected aggregate earnings changesare positively related to cash flow news and to changes in nominal interestrates, and negatively related to changes in default premia.

My findings contribute to two streams of literature. First, I contributeto the literature that examines the relation between accounting earningsand asset prices from the corporate bondholders’ perspective (e.g., Easton,Monahan, and Vasvari [2009]). My findings suggest that aggregate earn-ings incorporate a different type of information from firm-specific earn-ings. Hence, firm-level findings on the relation between earnings and as-set prices are not generalizable to the aggregate level. Further, I add tothe literature that examines the relation between accounting informationand the macroeconomy (e.g., Kothari, Lewellen, and Warner [2006]) inthe following ways. First, I reconcile the findings in Kothari, Lewellen, andWarner [2006] and Sadka and Sadka [2009] by showing that the earnings-returns relation at the aggregate level is driven by both the expected andthe news component of aggregate earnings changes. Second, I shed lighton the relation between aggregate earnings news and discount rate news byshowing that nominal interest rates increase and default premia decreasewhen aggregate earnings are higher than expected. Finally, my findingshelp explain the negative aggregate earnings-returns relation for stocks(e.g., Kothari, Lewellen, and Warner [2006]). My results suggest that thenegative relation between aggregate earnings changes and stock market re-turns is partially driven by a negative relation between expected aggregateearnings changes and expected returns, and a positive relation betweenunexpected aggregate earnings changes and changes in nominal interestrates.

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Aggregate Earnings and Corporate Bond Markets

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