Issuer Quality and Corporate Bond Returns Robin Greenwood and Sam Hanson Harvard Business School QWAFAFEW Presentation: October 2013

Issuer Quality and Corporate Bond Returns

Robin Greenwood and Sam Hanson

Harvard Business School

QWAFAFEW Presentation: October 2013

The Credit CycleHow does the quantity/quality of credit evolve over time?

Research in corporate finance and macroeconomics has emphasized time-varying financing frictions

Recent research hints that time-varying returns due to shifting investor sentiment may also play a significant role:◦ Junk bond boom of the 1980s

◦ Credit boom of the 2000s

Jeremy Stein of the Federal Reserve has suggested that the Fed should actively monitor the composition of issuance

This paper: Historically, what is the relationship between quantity/quality of credit and future investor returns?

Introduction Empirical Strategy Issuer Quality Forecasting Results Interpretation Summary

Quantity and Quality

Existing market-timing literature uses financing quantities to forecast returns◦Firm-level stock returns: Loughran and Ritter (1995), Daniel and

Titman (2006), Fama and French (2008)

◦Market-wide or factor-level stock returns: Baker and Wurgler (2000), Greenwood and Hanson (2010)

Why focus on the credit quality of debt issuers?◦Firms borrow more when expected credit returns are lower

◦Broad changes in pricing of credit have a larger impact on the cost of debt for low quality firms (i.e., high default probability firms) “Credit Beta”

◦Low quality issuance responds more to shifts in pricing of credit

◦→ Movements in expected credit returns trace out variation in the average quality of debt issuers


OverviewConstruct time-series measures of corporate debt issuer quality.

Use quality measures to forecast corporate bond excess returns.

Main finding: When issuers are of low quality, future excess corporate bond returns are low, and often significantly negative

Incremental forecasting power over various controls and the total quantity of corporate debt financing

What drives time variation in expected returns?1. Countercyclical risk premia

2. Changes in the health of intermediary balance sheets

3. Excessive risk-taking due to agency problems: “reaching for yield”

4. Over-extrapolation by investors

Evidence of mispricing suggests #3 or 4 may be part of the story


Empirical Strategy

Firms of differing credit quality choose debt issuance

Credit spreads: reflect expected losses and expected excess returns, both of which vary over time◦ Shifts in expected excess returns can reflect changes in rational price of

risk, mispricing, or both

Firms: Issue more debt when expected returns are lower◦ But issuance is impacted by other factors (shifts in investment opportunities

or target leverage) → issuance is a noisy reflection of expected returns

Identifying assumption: Expected excess returns on low quality bonds are more exposed to broad changes in the pricing of credit◦ e.g., if E[AAA return] falls by 10 bps, E[HY return] falls by 100bps →Low quality issuance responds more to broad shifts in credit pricing


Quantity = sum of issuance of low and high credit quality firmso Impacted by common shocks to factors unrelated to expected returns

(shifts in investment opportunities or target leverage)

Quality = difference in issuance between low & high quality firmso Removes common shocks, better isolating movements in expected returns

Forecasting excess returns using quantity and quality:o Quality more informative than quantity if important common shocks

unrelated to expected returns impact debt issuance of all firms

Forecasting Returns w/ Quality, Quantity, and Spreads


Measuring Issuer Quality: ISSEDF

What is the default probability of firms with high vs. low debt issuance?

◦EDFi,t = Merton (1974) Expected Default Frequency, computed following Bharath and Shumway (2008)

◦Easiest to think of this as the difference in the “credit rating” between high and low debt issuers


, ,

, ,

, ,

i t i t

i t i t

EDF

t

i t i ti High d i Low d

High d Low d

t t

ISSEDF EDF

N N

ISSEDF is high when issuing firms are of poor credit quality



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SED

F

ISS EDF


◦ ISSEDF correlated with business cycle, but removing macro variation doesn’t change basic character of series.



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Recession ISS EDF


◦ ISSEDF correlated with business cycle, but removing macro variation doesn’t change basic character of series.

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SED

F

Recession ISS EDF ISS EDF (Orthogonalized to Macro variables)



Credit boom2004-2007

Credit boom1996-19981980s junk

bond boom

Junk bond bust1990-1991

Telecom bust2001-2002

Late-1960scredit boom

Penn Central1970

Measuring Issuer Quality: High Yield ShareIntroduction Empirical Strategy Issuer Quality Forecasting Results Interpretation Summary

-1

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194419461948195019521954195619581960196219641966196819701972197419761978198019821984198619881990199219941996199820002002200420062008

ISS

_ED

F

HY

S

HYS (NBER) HYS (Moody's Surveys) HYS (FISD) ISS_EDF

HYS (NBER)

HYS (FISD)

HYS (Moody's)

,

, ,

t

i tHighYield

i t i tHighYield InvGrade

HYSB

B B

Measuring Issuer Quality: High Yield ShareIntroduction Empirical Strategy Issuer Quality Forecasting Results Interpretation Summary

,

, ,

t

i tHighYield

i t i tHighYield InvGrade

HYSB

B B

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194419461948195019521954195619581960196219641966196819701972197419761978198019821984198619881990199219941996199820002002200420062008

ISS

_ED

F

HY

S

HYS (NBER) HYS (Moody's Surveys) HYS (FISD) ISS_EDF

HYS (NBER)

ISSEDF

HYS (FISD)

HYS (Moody's)

1962-1982: r(HYS,ISSEDF) = 0.47 1983-2008: r (HYS,ISSEDF) = 0.58

Measuring Issuer Quality: ISSEDF vs HYS

Advantages of HYS◦Simplicity

◦“Natural” to use bond issuance to forecast bond returns

Advantages ISSEDF

◦Combines all sources of debt financing → not impacted by secular shifts in the bond vs. loan mix → stationary series

◦ If bonds/loans are partial substitutes, measures based on total debt issuance (loans+bonds) may be more informative about bond returns.

◦Credit rating standards have evolved over time: agencies became more conservative in the late 1970s

◦Based on net debt issuance as opposed to gross issuance


Other dataCorporate bond returns by credit rating from Barclays (Lehman)

and Morningstar (Ibbotson)◦Cumulative k-year log excess returns:

◦Returns are in excess of Treasury bonds with comparable duration

Other controls: bill yield, term spread, macro controls, etc


HY HY Gt k t k t krx r r

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2-ye

ar E

xces

s H

Y R

etur

ns (

%)

Issu

er Q

uali

ty I

SSE

DF

ISS EDF 2-year Excess High Yield Returns (%)

Figure 3, Panel A:

• Economic magnitudes are significant:

◦ 1-s increase in ISSEDF (0.48 deciles) → cumulative excess returns fall by 7.30 %-points over the following 2 years

◦ Same results hold with HYS (Figure 3, Panel B)

Issuer quality forecasts excess corporate bond returnsIntroduction Empirical Strategy Issuer Quality Forecasting Results Interpretation Summary

2 2

2

[ 2.02] [ 5.29]3.62 15.24 26%HY

t t

EDFt

t trx ISS u R

1-yr: 2-yr: 3-yr:

Panel A: High Yield Excess Returns (rxHY)

b -9.534 -15.254 -17.301

[t] [-3.97] [-5.29] [-3.68]

R2 0.12 0.26 0.29

Panel B: BBB Excess Returns (rxBBB)

b -5.311 -6.945 -6.645

[t] [-3.96] [-4.87] [-3.00]

R2 0.13 0.18 0.13

Panel C: AAA Excess Returns (rxAAA)

b -2.278 -3.321 -3.372

[t] [-2.43] [-2.55] [-1.50]

R2 0.10 0.08 0.05

Issuer quality forecasts excess corporate bond returnsIntroduction Empirical Strategy Issuer Quality Forecasting Results Interpretation Summary

Table 2: Univariate forecasting regressions

t k t k

EDFtrx a b ISS u

• Increasing coefficients up to 3-years, levels off aftero Emphasize 2-year cumulative

returns from here on

• Stronger results for HY bonds. o Consistent with idea that ISSEDF

reflects pricing of credit risk

oResults hold even with number of interest rate and macro controls

→Parallel results for HYS

Quality and Quantity during Credit BoomsIntroduction Empirical Strategy Issuer Quality Forecasting Results Interpretation Summary

r(DDAgg/DAgg,ISSEDF) = 0.45

Measure aggregate credit growth using Compustat as DDt/Dt-1. Similar results using Flow of Funds data.

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ISS

ED

F

Cre

dit

Gro

wth

(%

)

Credit Growth (Compustat) ISS EDF

Credit Growth

ISSEDF

Table 4: Panel A

ISSEDF -15.254 -12.978 [-5.29] [-3.78]

DAgg/DAgg (Agg. debt growth) -5.212 -2.433 [-3.97] [-1.49]

D1/D1 (Low EDF debt growth) -3.474 -1.565 -4.917 [-2.04] [-0.86] [-3.16]

D5/D5 (High EDF debt growth) -7.091 -6.631 [-3.76] [-3.09]

D5/D5 - D1/D1 (High-Low) -5.420 -6.538 [-2.39] [-3.09]

R2 0.26 0.13 0.29 0.06 0.24 0.26 0.14 0.26

Quantity vs. QualityIntroduction Empirical Strategy Issuer Quality Forecasting Results Interpretation Summary

Credit growth of low quality firms is most

useful for forecasting returns

Quality beats Quantity in a horserace Differential debt

growth of low vs. high quality firms is a strong predictor

→Similar results for HYS

2 21 1, 2 2,HYt tt trx a b X b X u

What drives time-variation in expected credit returns?Introduction Empirical Strategy Issuer Quality Forecasting Results Interpretation Summary

1. Rational consumption-based (integrated-markets) explanations:i. Time-varying quantity of riskii.Time-varying rational price of risk

2. Frictional account:Changes in intermediary capital → changes in risk premia

3. Agency problems: Low interest rates → “Reaching for yield” → Mispricing

4. Investors make expectational errors:Extrapolation of recent outcomes → under/over-weight the probability of left-tail events

→ Mispricing

Changes in the Rational Price of RiskCounter-cyclical movements in price of risk as in representative

agent consumption-based models

If markets are integrated, time-varying risk premia that are reflected in credit markets should also show up in equity markets


Time-varying risk premia

A number of findings are consistent with these models:◦ ISSEDF is cyclical: High debt issuers have high EDFs in expansions But ISSEDF remains a strong forecaster after controlling for macro

variables

◦Results are strongest for lower-rated bonds which are more highly exposed to macroeconomic risk

Other findings cut against the integrated-markets view…◦ ISSEDF not useful for forecasting equity returns (Table 9)

◦ ISSEDF predicts high yield excess returns after controlling for contemporaneous realizations of MKTRF or Fama-French factors


Consumption-based models: expected excess returns always > 0◦HY underperform USTs in “bad times” → expected excess returns > 0.

However, predicted excess returns are often significantly negative

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utu

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etu

rn

-1 -.5 0 .5 1 1.5ISS_EDF

# Negative predicted returns: 28, # Significant: 14

Forecasting Reliably Negative Excess ReturnsIntroduction Empirical Strategy Issuer Quality Forecasting Results Interpretation Summary

Figure 5, Panel B:

Frictional Account: Intermediary capitalFluctuations in intermediary balance sheets affect risk premia

◦ Predict that issuer quality will be poor (i.e., ISSEDF will be high) when intermediary balance sheets are strong and risk bearing capacity is high

Look at several types of intermediaries:◦ Insurers: Largest holders of corporate bonds

◦ Broker-dealers: Provide liquidity in corporate bond market

◦ Banks: Provide a close substitute for bond financing

Measures of balance sheet strength:o Equity/Assets, Asset Growth, Bank Credit Losses


Frictional Account: Intermediary capital

We run two types of regressions:

◦Regression 1: What is the relationship between ISSEDF and proxies for intermediary balance sheet strength Zt?

Frictional models predict: b > 0

◦Regression 2: Do proxies for intermediary capital diminish the forecasting power of ISSEDF?

Frictional models predict: b2 < 0; magnitude of b1 should decline once we control for Zt


t

EDFt ta b Z eISS

2 21 2HY

t t

EDFt trx a b ISS b Z u

Equity Capital, or Assetgrowth

Insurer balance sheets

Some evidence of a link between balance sheets and ISSEDF

But controlling for intermediary balance sheet variables does not have meaningful impact on forecasting power of ISSEDF

Similar conclusions for other intermediary variables (Results here→)

Frictional stories also inconsistent with negative expected returns


Relationship with

ISSEDF

Forecasting 2-yr HY excess returns:

2

HY

trx

(1) (2) (3) (4) (5) (6) ISSEDF -13.102 -14.251

[-3.41] [-3.69]

E/AInsurer 0.156 0.133 -4.104 -2.058 -1.336 0.563

[3.58] [2.54] [-3.78] [-1.68] [-0.76] [0.33]

dA/AInsurer -0.008 -0.005 -0.382 -0.493 0.382 0.314

[-0.35] [-0.21] [-0.89] [-1.06] [0.70] [0.53]

Controls No Yes No No Yes Yes

R2 0.21 0.48 0.15 0.30 0.33 0.45

Agency-based Explanation: “Reaching for Yield”

Delegated institutional investors have incentives to reach for yield when interest rates are low or have fallen (Rajan 2005)◦2004-2007 credit market boom◦Klarman (1991): 1980s junk bond boom

Possible stories:◦ Intermediaries with fixed liabilities have incentives to engage in risk

shifting when nominal rates fall◦Costly for pensions to reduce return targets → reach for yield◦Fund managers compensated on basis of absolute nominal returns◦Stories may admit the possibility of negative expected returns

Our analysis:◦ Investigate impact of yields and changes in yields on ISSEDF

◦But recall our baseline results already control for interest rates


ISSEDF 1ISSEDF 2ISSEDF

,

G

S ty -0.047 [-1.63]

, ,( )G G

L t S ty y -0.277 [-4.91]

,1G

S ty -0.107 [-2.41]

, ,1( )G G

L t S ty y -0.335 [-5.99]

,2G

S ty -0.134 [-3.31]

, ,2 ( )G G

L t S ty y -0.410 [-7.63]

R2 0.44 0.36 0.47

Table 11: Impact of yields and changes in yields on ISSEDF

“Reaching for Yield”Introduction Empirical Strategy Issuer Quality Forecasting Results Interpretation Summary

→ Similar results for HYS

1-yr changes

Levels

2-yr changes

Investor-beliefs based explanation

Time-variation in expected returns may be due to mistaken investor beliefs about true creditworthiness of borrowers

Natural story: over-extrapolation◦Wide variety of evidence on investor extrapolation

◦ Investors use a “representativeness” heuristic

◦ Intermediaries use backwards looking risk management systems (e.g., Value-at-Risk) → built-in tendency towards over-extrapolation


Extrapolative BeliefsPotential account :

◦Economy switches between good times in which few firms default, and bad times in which a higher fraction of firms default

◦ Investors think economy either evolves via a more persistent process or less persistent process than truth (Barberis, Shleifer, Vishny 1998)

What happens?◦A string of low-default realizations → investors become over-optimistic that

good times will last → neglect down-side risks◦ If the high default state arrives → expectations are revised◦ If bad state persists → investors over-estimate default probabilitiesGenerates short-term return continuation, longer-term reversals

Add a corporate sector that levers up when debt is “cheap”Growing optimism → borrower quality erodesSpreads under-react to erosion in borrower quality in booms

→ both quality and credit spreads forecast returns


Extrapolative BeliefsConsistent with negative expected excess returns ✓ISSEDF should be high following a string of low realized defaults

or high returns on credit assets ✓


1-yr changes

Levels

2-yr changes

ISSEDF 1ISSEDF 2ISSEDF

1

HY

trx 0.014 [1.57]

tDEF -0.076

[-3.11]

1

HY

trx 0.031 [6.60]

1 tDEF -0.043

[-1.74]

3 1

HY

t trx 0.036 [6.91]

2 tDEF -0.065

[-3.77]

R2 0.34 0.49 0.59

→ Similar results for HYS

ConclusionsSummary:

◦ Issuer quality is low → future corporate bond excess returns are low

◦Evidence of mispricing: forecast significantly negative excess returns

◦2004-2007 credit boom is not without precedent – part of a recurring historical pattern, dating to at least the 1940s

Interpretation: ◦Difficult to fully explain by appealing to rationally time-varying risk

aversion or other rational drivers of counter-cyclical risk premia

◦Partially consistent with frictional and agency-based stories

◦Some evidence that over-extrapolation plays a role

Future work:◦Micro empirical work on excessive risk-taking? Or mistaken beliefs?

◦Understand the real consequences of credit market booms

◦Quality of sovereign debt issuers


Documents

Issuer Quality and Corporate Bond Returns Robin Greenwood and Sam Hanson Harvard Business School QWAFAFEW Presentation: October 2013