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Corporate Bond Yield Spreads in Recent Decades: Trends, Changes, and Stock

Market Linkages

Douglas J. Lamdin Department of Economics

University of Maryland, Baltimore County 1000 Hilltop Circle

Baltimore, MD 21250 Phone: 410-455-2160 Fax: 410-455-1054

E-mail: lamdin@umbc.edu

JEL Classifications: G0, G1, E4

September 2003 Abstract Corporate bond interest rates are a subject of concern to investment analysts, corporate financial managers, and scholars. In this article the yield spreads between corporate bonds and government bonds, and between differing quality corporate bonds is examined during recent decades. Also, the relationship between yield spreads and stock market movements is examined. Yield spreads vary considerably over this period, and have varying trends. Causality tests show that stock market movements precede changes in yield spreads.

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Corporate Bond Yield Spreads in Recent Decades: Trends, Changes, and Stock Market Linkages

1. Introduction

Corporate bond interest rates and associated yield spreads are core topics in

financial economics. What determines the level of, and changes in these variables is

important to a variety of financial analysts. An examination of these variables and

relationships between them, as well as possible linkages between yield spreads and stock

market behavior is the focus of this article. In particular, changes in yield spreads

between high and medium quality corporate bonds and Treasury bonds are examined, as

are changes in the spread between the differing quality corporate bonds. How these may

relate to aggregate stock market behavior is considered. At issue is the extent to which

changes in yield spreads and stock valuation may be linked in a “causal” or predictive

way.

The findings of this study should matter to scholars and practitioners. The former

seek to better understand the workings of financial markets and how they might have

changed in recent years. Financial market activity also has an impact on the real

economy as interest rates affect investment at the firm level, and at the macroeconomic

level.1 Practitioners such as security analysts and portfolio managers, investment

bankers, and corporate financial managers, will also find these results of interest as they

seek to improve their decision-making processes. For example, asset allocation

decisions, and the timing of bond issues would be of concern to them.

The structure of the article is as follows. The next section discusses how and why

the variables of interest have changed over recent decades. Data on these variables are

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examined to detect trends and changes from 1970 to the present. Thus, the data

examined are timely, but also cover a sufficient period to discern long-term trends. As

will be shown, there has been much variation in these variables. The third section

examines changes in the equity risk premium (ERP) during this period. The ERP is the

additional return equity investors expect to earn above the risk-free return on government

bonds. Recent research suggests that the ERP has declined, and thus it might be related

to bond yield spread behavior. The next section describes econometric causality tests.

These are then used to examine the possible relationship between movements in yield

spreads and stock market movements. A final section concludes and discusses the

significance and implications of the results.

2. A Preliminary Examination of the Data

Variations in interest rates are a given in financial market history. Changes in

macroeconomic factors (e.g., inflation, the business cycle) cause these fluctuations.

Investor perceptions of risk, of course, are a primary variable that affects both interest

rates and stock market valuations. Interest rates tend to move together due to common

influences, such as inflation. However, the difference between corporate and government

bond rates, and between corporate bonds of different quality is not constant. This

difference is the yield spread (or default spread, or quality spread). In general, when

economic uncertainty is higher (e.g., during recessions) these spreads widen.2

Yield spreads on different risk-classes of bonds will reflect the perceived relative

risk of these assets other things, primarily maturity, equal. Just as the difference between

the expected return on corporate bonds and government bonds will reflect primarily the

default risk of corporate bonds, so will differences in yields across corporate bonds. For

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example, Moody’s rates corporate bonds (e.g., Aaa, Baa, and so on), and the higher the

risk the higher the expected return necessary to compensate investors.

The concern here is with the time-series behavior of bond rates and yield spreads.

Monthly data from February 1970 to May 2003 were collected for the current promised

yield on Aaa and Baa corporate bonds, and for 10-year Treasury bonds.3 During this

period the monthly promised yield on the Treasury bonds (T) averaged 7.93 percent with

a standard deviation of 2.38 percent. For Aaa bonds these values were 8.87 percent and

2.08 percent. For Baa bonds these values were 9.98 percent and 2.40 percent. The Aaa -

Treasury (Aaa - T) yield spread averaged 0.95 percent during this period, with a standard

deviation of 0.48. The (Baa - T) yield spread averaged 2.05 percent with a standard

deviation of 0.58. The (Baa - Aaa) yield spread averaged 1.10 percent with a standard

deviation of 0.43. These statistics provide a sense of the size and degree of dispersion of

these variables, but do not tell us how the rates or spreads move together, nor does it tell

us of any trends in them.

How are these interest rates and yield spreads related? One way to determine this

is to examine the correlations among the contemporaneous values of the variables. For

example, if the Aaa and Baa bond rates move in lockstep, the correlation coefficient will

be +1.0. The various correlation coefficients are shown in Table 1. The Table shows that

the levels of the three interest rates move closely together, with all of the correlations

close to one. It might be concluded that because of this the yield spreads are similarly

correlated. But this is not exactly the case. The correlation between the (Aaa - T) spread

and the (Baa - T) spread is a strong 0.683. Similarly, the (Baa - T) and (Baa - Aaa)

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spreads tend to move together, with a 0.597 correlation coefficient. Conversely, the (Aaa

- T) and (Baa - Aaa) correlation coefficient is -0.179.

An interesting pattern to note is that when the level of the bond rates changes, the

yield spreads move in different directions. The (Aaa - T) spread moves in the opposite

direction as the level of all of the interest rates with the correlation coefficients ranging

from -0.528 to -0.699. The (Baa - Aaa) spread moves in the same direction as the level

of the interest rates with the correlation coefficients ranging from 0.690 to 0.779. The

(Baa - T) spread is basically independent of the level of the interest rates with the

correlation coefficients ranging from -0.097 to 0.147.

______________________________________________________________________________________

Table 1: Correlations of Bond Rates and Yield Spreads (Monthly data: 2/70-5/03)

Aaa Baa Aaa - T Baa - T Baa - Aaa

Treasury 0.986 0.970 -0.699 -0.097 0.673

Aaa 0.991 -0.572 0.046 0.690

Baa -0.528 0.147 0.779

Aaa - T 0.683 -0.179

Baa - T 0.597

______________________________________________________________________________________

The rates themselves are not of primary interest here, and we have seen that they

move together closely. Of greater interest are the changes in the yield spreads. In Table

2, the correlations between contemporaneous changes in the levels of the interest rates

and the changes in the yield spreads are examined. Also added is a measure of the

change in stock market value--the monthly total rate of return on the S&P 500 Index.4

The changes in the levels of the interest rates move together with correlation coefficients

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above 0.8. The yield spread changes, however, show a different pattern. Changes in the

Treasury rate are negatively related to all of the yield spreads as well as the stock market.

The change in the stock market is negatively related to changes in the Aaa and Baa rates

as well. The relationship between the stock market and the yield spreads is positive,

though weak.

______________________________________________________________________________________

Table 2: Correlations of the Changes in Bond Rates, Yield Spreads, and the Stock Market (Monthly data:

2/70-5/03)

∆Aaa ∆Baa ∆(Aaa – T) ∆(Baa – T) ∆(Baa – Aaa) S&P Return

∆Treasury 0.909 0.816 -0.652 -0.708 -0.372 -0.225

∆Aaa 0.893 -0.276 -0.459 -0.419 -0.240

∆Baa -0.257 -0.169 0.035 -0.235

∆(Aaa – T) 0.800 0.085 0.090

∆(Baa – T) 0.666 0.101

∆(Baa – Aaa) 0.056

______________________________________________________________________________________

To examine any trend in the spreads, regression equations were estimated with the

spreads as the dependent variable and a time trend variable as the independent variable.

The results are summarized below with the t-ratios for the regression coefficients in

parentheses.

(Aaa - T) = 0.485 + 2.315*E-3*TIME R2 = 0.315

(13.5) D-W = 0.134

(Baa - T) = 1.850 + 9.912*E-4*TIME R2 = 0.038 (4.0) D-W = 0.116

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(Baa - Aaa) = 1.365 – 1.323*E-3*TIME R2 = 0.124 (-7.5) D-W = 0.085

There appears to be a trend in each yield spread. The (Aaa - T) and (Baa - T)

spreads have increased over time. For example, the results imply that each 60 months the

(Aaa - T) spread has risen by 60*0.002351 = 0.14 percent. This suggests that investors

have increasingly required relatively higher returns on corporate bonds in general. This

is consistent with Woolridge’s (1995) claim that bonds have become more risky relative

to stocks.5 The (Aaa - T) regression has by far the best fit with an R-squared value of

0.315. The R-squared values for the (Baa - T) and (Baa - Aaa) spreads are only 0.038

and 0.124. The negative coefficient on time in the (Baa - Aaa) model suggests that the

“quality spread” has declined. The implication is that there has been a decline in the

relative risk of the lower-quality Baa bonds. The obvious explanation is that investors,

for a given level of relative risk, do not require as much additional compensation as in

previous years. Another explanation is that the lower-quality bonds are increasingly of

higher relative quality although they have the same rating by Moody’s. That is, ratings

have become more stringent over time. The study of Blume et al. (1998), which uses

Standard and Poor’s ratings, suggests that this has occurred.

The time trend variable does not fully explain the movement in the spreads given

that the R-squared values are well below one. The Durbin-Watson statistics show quite a

bit of positive serial correlation, which is not surprising given the cyclical nature of the

yield spreads. Because the purpose at hand is simply to detect any trend, serial

correlation corrections were not done.

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To see the extent to which this trend in yield spreads has been a more recent

phenomenon, or may be sensitive to particular time periods, a dummy variable was added

to each model. The dummy is set equal to one for all observations in 2001 through 2003.

This corresponds to the period in which the stock market decline was well underway, and

also subsumes the post “9/11” terrorist attack period. This is summarized below. In each

case the coefficient on the dummy variable (D01-03) was positive and statistically

significant. Moreover, the size is economically significant. For example, the coefficient

for the (Baa - T) model implies a higher spread of 1.1 in 2001-2003. For the (Aaa - T)

and (Baa - Aaa) models these are 0.795 and 0.324 percent. After treating 2001-2003

separately, the (Aaa - T) spread still has a positive and significant trend. The (Baa - T)

spread no longer has a significant trend. The significant negative trend in the (Baa - Aaa)

spread still exists. Again, the three yield spreads do not all follow an identical pattern

from 1970-2003.

(Aaa - T) = 0.588 + 1.513*E-3*TIME + 0.795*D01-03 R2 = 0.465 (8.9) (10.5) D-W = 0.184

(Baa - T) = 1.995 - 1.370*E-4*TIME + 1.118*D01-03 R2 = 0.236 (-0.5) (10.1) D-W = 0.158

(Baa - Aaa) = 1.407 - 1.650*E-3*TIME + 0.324*D01-03 R2 = 0.154 (-8.5) (3.7) D-W = 0.090

As mentioned earlier, yield spreads tend to move with the state of the economy.

To see whether the results reported thus far are affected by this, the monthly

unemployment rate was added to the models. This is shown below. The coefficient on

the unemployment rate is positive in the case of the (Baa - T) and (Baa - Aaa) spreads as

would be expected. Moreover, the increment to the R-squared from the addition of the

unemployment rate is substantial in these cases. A less robust economy increases these

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yield spreads. Conversely, the (Aaa - T) spread is negatively related to the

unemployment rate, although the effect is small and the increment to the R-squared is

small as well. In all cases the 2001-2003 dummy remains positive and of similar size.

The time trend is significantly positive for the (Aaa - T) and (Baa - T) spreads. It remains

significantly negative for the (Baa - Aaa) spread.

(Aaa - T) = 0.745 + 1.422*E-3*TIME + 0.792*D01-03 – 2.199E-2*UR R2 = 0.468 (8.0) (10.5) (-1.7) D-W = 0.186

(Baa - T) = 0.631 + 6.579*E-4*TIME + 1.145*D01-03 + 0.192*UR R2 = 0.423 (2.9) (11.9) (11.4) D-W = 0.202

(Baa - Aaa) = -0.113 - 7.639*E-4*TIME + 0.353*D01-03 + 0.241*UR R2 = 0.578 (-5.3) (5.8) (19.9) D-W = 0.174

3. The Equity Risk Premium: What is it? Has it changed?

The changing bond yield spreads evident above suggests that there have been

changes in the factors that would affect perceived risk. It could reflect changes from the

supply side such as the level and volatility of corporate cash flow to meet interest

obligations. It could also reflect demand side factors such as diversification possibilities

of investors, or their degree of risk aversion. These same factors could also have an

impact on the risk of equity investments. Thus, changing yield spreads could also be

related to an ongoing topic of interest: changes in the equity risk premium.

The ERP is the return equity investors expect to earn above the rate of return on

government bonds. Recent research has lead to two primary conclusions: (1) realized

historic returns on the stock market likely overestimate the historic and current ERP, and

(2) “implied,” ERP estimates are below realized rates, and also may have fallen over

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time. A decline in the ERP appears to be at odds with corporate yield spreads, which,

with the exception of the (Baa - Aaa) spread, do not appear to have fallen with time.

Measurement of the equity risk premium (ERP) using realized returns on the

stock market may seriously overestimate the ERP. Basic models of share valuation imply

that stock values equal the present value of expected future cash flows to shareholders.

As the expected growth rate in future cash flows rises or falls, so too will share values. If

the required return on equity rises or falls, share values will move in the opposite

direction. Thus, if “surprises,” on average, are positive regarding growth, and/or the

required rate of return is falling, stock prices will be higher than expected, as will realized

returns. Realized returns will therefore exceed the required return of investors. If this

has been the case, as Fama and French (2002) and others claim, ERP estimates based on

realized returns will not provide accurate estimates. This suggests using a method to

extract the required return of investors and the ERP that does not use historic realized

returns.6 For example, Brealy and Myers (2003) use an estimate based on historic

realized returns of eight percent, while Ross et al. (2002) suggest about nine percent.

Implied ERP estimates appearing in the literature have tended to be about half of these

values. For example, ranges of estimates include Claus and Thomas (2001) estimate 3

percent to 4 percent, Fama and French estimate 2.5 percent to 4.3 percent, and Lamdin

(2002) who suggests 3.3 percent to 4.7 percent.

The ERP measured in a forward-looking manner uses a model of stock value

(returns) based on the present value of future cash flows to investors. In a constant

growth model this implies that the expected dividend yield plus the expected capital gains

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provides an estimate of the required return of investors. Subtracting the current

government bond rate provides an “implied” ERP estimate.

A detailed examination and application of such an approach to estimate the ERP

is beyond the scope here. However, a related and simple way to estimate the trend

(though not the level) of the forward-looking implied ERP can be illustrated. Levy

(1998) shows that the earnings-price ratio can be used as an estimate of the required

return of investors under some assumptions. The most of important assumption is that

the firms under consideration are experiencing “normal” growth. This means that the

long-run growth of their profits, and thus cash paid to shareholders, is in line with the

growth of the economy. For a diversified value-weighted portfolio (e.g., the S&P 500

index), this is not an unreasonable assumption. To the extent that some firms in the index

are experiencing, or are expected to experience above-normal growth, the E/P ratio will

underestimate the true required return. If this underestimate is roughly constant over

time, the trend in the E/P ratio minus the government bond rate should reflect the trend in

the ERP.

Annual data on the E/P ratio for the S&P 500, and the 10-year Treasury bond for

1970-2001 were examined.7 The results of the regression of the annual E/P ratio on a

time trend, and the E/P ratio minus the 10-year Treasury bond rate on a time trend

variable are shown below.

E/P = 10.692 - 0.2048*TIME R2 = 0.388

(-4.4) D-W = 0.442

(E/P - T) = 1.304 - 0.124*TIME R2 = 0.313 (-3.7) D-W = 0.674

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The negative coefficient on the time trend variable in both cases implies a decline in the

ERP. For example, (E/P - T) is falling by 0.124*5 = 0.62 percent per year, a non-trivial

decline. The decline in the ERP is similar to the decline in the (Baa - Aaa) yield spread,

but opposite of the positive trend in the (Aaa - T) and (Baa - T) spreads. This presents a

conundrum. The declining ERP suggests a decline in the risk of investment in the equity

of the aggregate corporate sector. It is not illogical to expect that this decline in equity

risk would also cause a decline in (Aaa - T) and (Baa - T) spreads as the bonds are less

risky as well. One must be somewhat circumspect, however, in drawing conclusions

from time-series data such as these. If stock market valuations are in some sense

“irrational,” then ERP estimates such as these are not reliable. On a more positive note,

as will be shown, there is some consistency regarding movements in yield spreads and

the stock market valuations than is apparent so far.

A more complicated question to be addressed is the extent to which there is any

causal or predictive relationship between yield spread changes and changes in stock

market valuations. Basic stock market valuation models imply that value is positively

related to cash flows to investors (e.g., dividends), and negatively related to riskiness of

the cash flows (required returns of investors). Increases (decreases) in perceived risk

should widen (narrow) yield spreads measured against government bonds, and also widen

(narrow) yield spreads of differing quality of bonds if lower-quality bonds are more

sensitive to macroeconomic changes in expected cash flows. Increases (decreases) in

expected future cash flow should decrease (widen) yield spreads measured against

government bonds, and also decrease (widen) yield spreads of differing quality of bonds

if lower-quality bonds are more affected by economy wide changes in perceived risk.

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The empirical question is whether this is borne out in the data. In particular, the question

to be addressed is whether changes in yield spreads precede changes in stock market

valuation, or changes in stock market valuation precede changes in bond risk premia, or

neither, or both?8

4. Causality Tests

Causality tests became popular in the empirical economics literature in the 1970s

and 1980s, and continue to be used to examine time-series data. Their implementation

will be briefly reviewed.9 The basic approach is as follows. Variable A is regressed on

its lagged values to see the extent to which lagged values can explain current values. If

an additional variable (B), is believed to cause A, lagged values of B are added to the

model to measure whether they add significant explanatory power. If so, B is said to

cause A. Similarly, one uses B as the dependent variable. B is regressed on its lagged

values, and then on its lagged values and lagged values of A to test for the significance of

adding lagged A values. In the case of two variables such as this, one can find: (1) A

causes B; (2) B causes A; (3) A causes B and B causes A; and (4) A does not cause B and

B does not cause A. Case (3) is likely to be spuriously caused by a third variable that is

influencing both X and Y.

Causality tests described above are used to examine the hypothesis that

movements in bond yield spreads and movements in the stock market are related. For

econometric reasons it is appropriate to use changes in the yield spreads, and changes in

stock market valuation. Unlike the levels of these variables, the changes are less likely to

have a trend and therefore are a “stationary” series. Using non-stationary variables can

lead to spurious correlations between the variables. So, the monthly changes in each of

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the three yield spreads, and the monthly S&P 500 return are the variables used in the

analysis that follows.

The first set of models has the S&P return as the dependent variable. Separate

models use lagged values of each yield spread change as independent variables. The

choice of lag length is somewhat subjective. Three months of lags are used for each

variable in all models. This should allow sufficient time for the market to reflect any

changes given that interest rates and stock prices are presumed to quickly impound new

information. Also, as a practical matter, this keeps the results reported to a manageable

amount. Table 3 summarizes the results. The R-squared values for the models are shown,

as is the F-test of the significance of adding the lagged values of each yield spread

change. The lagged values of the S&P return have little power in explaining its current

value. The R-squared is essentially 0. Addition of the lagged values of yield spread

changes increases the R-squared by little. The F-test confirms this. One cannot reject the

hypothesis that yield spread changes do not cause changes in the S&P 500 at the one

percent level of significance. (The critical F-value for one percent significance for these

tests is 3.78.)

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________________________________________________________________________

Table 3: Test of Whether Changes in Yield Spreads Cause Changes in the S&P 500 Index (Monthly data: 2/70-5/03)

Independent variables R-squared F-statistic

Dependent variable: Monthly S&P 500 return

Lagged S&P return 0.001 Lagged S&P return and Lagged ∆(Aaa - T) 0.015 1.8 Lagged S&P return and Lagged ∆(Baa - T) 0.020 3.5 Lagged S&P return and Lagged ∆(Baa - Aaa) 0.017 2.1 ________________________________________________________________________

Now, each of the yield spread changes is the dependent variable, with lagged S&P

500 returns added as independent variables. Table 4 reports these results. Lagged values

of each yield spread have a small amount of explanatory power with R-squared values

ranging from 0.040 to 0.097. What is more important is the finding in each case that

lagged values of the return on the S&P 500 add a significant amount of explanatory

power. The hypothesis that lagged S&P returns do not add explanatory power to the

models can be rejected at the one percent level in each case. That is, lagged values of the

S&P 500 return precede (“cause”) changes in the yield spreads. In each case, the

coefficients on the lagged S&P return values are negative. That is, an increase (decline)

in the S&P 500 leads to a narrowing (widening) of the yield spreads. This makes sense

because a reduction in perceived risk, or an increase in the expected future cash flow of

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the corporate sector, would cause stock prices to rise. These should also act to reduce

yield spreads. Note however that should the (Aaa - T) and (Baa - T) spreads change by a

similar amount, the (Baa - Aaa) spread would be unchanged.

________________________________________________________________________

Table 4: Test of Whether Changes in the S&P 500 Index Cause Changes in Yield Spreads (Monthly data: 2/70-5/03) Independent variables R-squared F-statistic

Dependent variable: Monthly ∆(Aaa - T)

Lagged ∆(Aaa - T) 0.040 Lagged ∆(Aaa - T) and Lagged S&P 500 return 0.096 7.6

Dependent variable: Monthly ∆(Baa - T) Lagged ∆(Baa - T) 0.097 Lagged ∆(Baa - T) and Lagged S&P 500 return 0.173 10.9

Dependent variable: Monthly ∆(Baa - Aaa) Independent variables: Lagged ∆(Baa - Aaa) 0.080 Lagged ∆(Baa - Aaa) and 0.126 6.5 Lagged S&P 500 return

In the interest of space, all of the full equations are not show. However, it is

useful to examine at least of one of these in detail. Below the model with ∆(Baa - T) as

the dependent variable is shown. This had the highest R-squared value, but is

representative of the other models. In addition to the joint significance of the lagged S&P

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variables, note that the individual t-ratios on the lagged S&P coefficients are either

significant, or marginally so at standard levels.

Dependent variable: ∆(Baa – T) Independent Variable Coefficient t-ratio Constant 2.270*E-02 ∆(Baa – T) -1 0.240 4.79 ∆(Baa – T) -2 -0.230 -4.52 ∆(Baa – T) -3 5.154E-02 1.06 S&P Return -1 -0.976 -4.88 S&P Return -2 -0.401 -1.94 S&P Return -3 -0.585 -2.83

In spite of the significance of the addition of the lagged S&P 500 returns, the R-

squared values are low (ranging from 0.096 to 0.173). Well below one-third of the

variation in the yield spread changes is accounted for by the models. This can be

explained in two related ways. First, stock market movements and interest rate

movements, particularly over short periods, are difficult to predict. Thus, the R-squared

values reflect this. Also, because the dependent and independent variables are changes in

the variable, not levels of the variables, this will cause low R-squared values. This is

similar to what one would find by estimation of the basic macroeconomics time series

consumption function. Aggregate consumption regressed on aggregate national income

has an R-squared close to 1. Using the same data, but estimating the regression using the

changes in the variables rather than their levels yields a much lower R-squared value.

The results shown here complement some in related articles by Kwan (1996) and

Collin-Dufresne et al. (2001). Kwan (1996) examined weekly firm-level bond yields (not

spreads) changes and stock returns from 1986-1990. Lagged stock returns were found to

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explain current bond yield changes, but lagged bond yield changes were not found to

explain current stock returns. Collin-Dufresne et al. (2001) examined monthly data from

1988 through 1997 on firm-level changes in bond yield spreads. They found them to be

negatively related to contemporaneous changes in treasury yields and the S&P index

returns. The one period lag of the S&P return was also negatively related to the change

in yield spreads in a significant way.

6. Conclusions and Prospective Issues

These results have a number of implications. The trend in corporate bond yield

spreads suggests that over the past three decades they exhibit differing trends. The (Aaa -

T) and (Baa - T) spreads have risen with time. This implies higher promised returns to

investors, and thus a higher cost of borrowing to corporations. The (Baa - Aaa) spread,

however, does not show the same trend, suggesting a decline in the relative risk of these

differing quality ratings. In the period 2001-2003 all yield spreads increased.

The equity risk premium seems to have decreased during this time, which is

counter to the movement in the (Aaa - T) and (Baa - T) yield spreads. This is not easily

reconciled, and warrants future examination. Looking at shorter-term data (monthly)

with causality tests, rather than long-term trends, a rise or fall in the stock market tends to

precede (predict) a change in yield spreads in the opposite direction. This should be of

interest to bond investors, investment bankers, and corporate financial managers. Bond

investors may be able to profit from being able to anticipate bond price (yield)

movements from stock market movements. Corporate financial managers and investment

bankers may also be able to better time bond issues from stock market movements.

Causality tests do not show that changes in yield spreads precede stock price movements.

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Equity investors who seek to better time the market using information on movements in

yields spreads should not be able to do so.

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Blume, Marshall E., Felix Lim, and A. Craig MacKinlay. 1998. The Declining Credit Quality of U.S. Corporate Debt: Myth or Reality. Journal of Finance 53, pp. 1389-1413. Brealy, Richard A., and Stewart C, Myers.2003. Principles of Coroprate Finance, 7th ed., New York: McGraw-Hill. Chen Nai-Fu, Richard Roll, and Stephen A. Ross. 1986. Economics Forces and the Stock Market. Journal of Business 59, pp. 383-403. Claus, James, and Jacob Thomas. 2001. Equity Premia as Low as Three Percent? Evidence from Analysts’ Earnings Forecasts for Domestic and International Stock Markets. Journal of Finance 56, pp. 1629-1666. Collin-Dufresne, Pierre, Robert S. Goldstein, and J. Spencer Martin. 2001. The Determinants of Credit Spread Changes. Journal of Finance 56, pp. 2177-2207. Cornell, Bradford. 1999. The Equity Risk Premium. New York: John Wiley and Sons. Dignan, James H. 2003. Nondefault Components of Investment-Grade Bond Spreads. Financial Analysts Journal 59, pp. 93-102. Elton, Edwin J., Martin J. Gruber, Deepak Agrawal, and Christopher Mann. 2001. Explaining the Rate Spreads on Corporate Bonds. Journal of Finance 56, pp. 247-277. Fama, Eugene F., and Kenneth R. French 2002. The Equity Premium. Journal of Finance 57, pp. 637-659. Francis, Jack C., and Roger Ibbotson, 2002. Investments: A Global Perspective. Upper Saddle River, NJ: Prentice Hall. Gujurati, Damodar N. 1995. Basic Econometrics, 3rd ed., New York: McGraw Hill. Hubbard, R. Glenn. 2003. Economic Outlook and Economic Policy in the United States. Business Economics 38, pp. 12-19. Jagannathan, Ravi, Ellen R. McGrattan, and Anna Scherbina. 2000. The Declining U.S. Equity Premium. Federal Reserve Bank of Minneapolis Quarterly Review 24, pp. 3-19. Kwan, Simon H. 1996. Firm-Specific Information and the Correlation Between Individual Stocks and Bonds. Journal of Financial Economics 40, pp. 63-80.

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Lamdin, Douglas J. 2002. New Estimates of the Equity Risk Premium and Why We Need Them. Business Economics 37, pp. 54-60. Levy, Haim. 1998. Principles of Corporate Finance. Cincinnati: International Thomson Publishing Company. Malkiel, Burton G. 1979. The Capital Formation Problem in the United States. Journal of Finance 34, pp. 291-306. Malkiel, Burton G., and Yexiao Xu. 1997. Risk and Return Revisited. Journal of Portfolio Management 23, pp. 9-14. Mishkin, Frederic S., and Stanley G. Eakins 2003. Financial Markets and Institutions, 4th ed., Boston: Addison Wesley. Ross, Stephen A., Randolph W. Westerfield, and Jeffrey F. Jaffe. 2002. Corporate Finance, 6th ed., Boston: Irwin-McGraw-Hill. Siegel, Jeremy J. 1999. The Shrinking Equity Premium. Journal of Portfolio Management 26, pp. 10-16. Van Horne, James C. 2001. Financial Market Rates and Flows, 6th ed. Upper Saddle River, NJ: Prentice Hall. Woolridge, J. Randall. 1995. Do Stock Prices Reflect Fundamental Values? Journal of Applied Corporate Finance 8, pp. 64-69.

1Hubbard (2003) provides a current view of related issues. Although written much earlier, issues raised by Malkiel (1979) remain timely. 2This is discussed in, for example, Van Horne (2001), Francis and Ibottson (2002), and Mishkin and Eakins (2003). 3 The data were obtained from www.federalreserve.gov. 4 The S&P Index data were taken from the www.research.stlouisfed.org. 5 The Malkiel and Yu (1997) show that aggregate stock market volatility has not risen over time, but that of individual stocks has risen. 6 See, for example, Siegel (1999), Cornell (1999), Jagannathan et al. (2000), Claus and Thomas (2001), Fama and French (2002), and Lamdin (2002). 7 The data were taken from The Economic Report of the President, 2003, Tables B-73 and B-95. 8 That bond yield spreads may be related to stock returns is not new. Chen et al. (1986), in an early empirical examination of the Arbitrage Pricing Theory, include a yield spread measure as one of the risk factors. Their concern was with explaining cross-sectional return differences, not time-series causality. 9 Further descriptions are available in most econometrics texts, for example, Gujurati (1995).