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

2. Introduction Empirical Strategy Issuer Quality Forecasting Results Interpretation Summary
The Credit Cycle
How 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?
Junk bond boom of the 1980s (Kaplan and Stein 1993)
Credit boom of the 2000s (Coval, Jurek and Stafford 2010; Ivashina and Sun 2010, Axelson, Jenkinson, Stromberg, and Weisbach 2010)

3. 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

4. Introduction Empirical Strategy Issuer Quality Forecasting Results Interpretation Summary
Overview
Construct 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?
Countercyclical risk premia
Changes in the health of intermediary balance sheets
Excessive risk-taking due to agency problems: “reaching for yield”
Over-extrapolation by investors
Evidence of mispricing suggests #3 or 4 may be part of the story

5. Introduction Empirical Strategy Issuer Quality Forecasting Results Interpretation Summary
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

6. Forecasting Returns w/ Quality, Quantity, and Spreads
Introduction Empirical Strategy Issuer Quality Forecasting Results Interpretation Summary
Forecasting Returns w/ Quality, Quantity, and Spreads
Quantity = sum of issuance of low and high credit quality firms
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 firms
Removes common shocks, better isolating movements in expected returns
Forecasting excess returns using quantity and quality:
Quality more informative than quantity if important common shocks unrelated to expected returns impact debt issuance of all firms

7. Measuring Issuer Quality: ISSEDF
Introduction Empirical Strategy Issuer Quality Forecasting Results Interpretation Summary
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

8. Measuring Issuer Quality: ISSEDF
Introduction Empirical Strategy Issuer Quality Forecasting Results Interpretation Summary
Measuring Issuer Quality: ISSEDF
ISSEDF is high when issuing firms are of poor credit quality

9. Measuring Issuer Quality: ISSEDF
Introduction Empirical Strategy Issuer Quality Forecasting Results Interpretation Summary
Measuring Issuer Quality: ISSEDF
ISSEDF is high when issuing firms are of poor credit quality
ISSEDF correlated with business cycle, but removing macro variation doesn’t change basic character of series.

10. Measuring Issuer Quality: ISSEDF
Introduction Empirical Strategy Issuer Quality Forecasting Results Interpretation Summary
Measuring Issuer Quality: ISSEDF
ISSEDF is high when issuing firms are of poor credit quality
ISSEDF correlated with business cycle, but removing macro variation doesn’t change basic character of series.
Credit boom
1980s junk bond boom
Junk bond bust
Telecom bust
Late-1960s credit boom
Penn Central 1970

11. Measuring Issuer Quality: High Yield Share
Introduction Empirical Strategy Issuer Quality Forecasting Results Interpretation Summary
Measuring Issuer Quality: High Yield Share

12. Measuring Issuer Quality: High Yield Share
Introduction Empirical Strategy Issuer Quality Forecasting Results Interpretation Summary
Measuring Issuer Quality: High Yield Share
: r(HYS,ISSEDF) = : r (HYS,ISSEDF) = 0.58

13. Measuring Issuer Quality: ISSEDF vs HYS
Introduction Empirical Strategy Issuer Quality Forecasting Results Interpretation Summary
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
Blume, Lim, and MacKinlay (1998) and Baghai, Servaes, and Tamayo (2010) argue that the agencies have become more conservative in assigning ratings since the late 1970s.

14. Introduction Empirical Strategy Issuer Quality Forecasting Results Interpretation Summary
Other data
Corporate 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

15. Issuer quality forecasts excess corporate bond returns
Introduction Empirical Strategy Issuer Quality Forecasting Results Interpretation Summary
Issuer quality forecasts excess corporate bond 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)

16. Issuer quality forecasts excess corporate bond returns
Introduction Empirical Strategy Issuer Quality Forecasting Results Interpretation Summary
Issuer quality forecasts excess corporate bond returns
Table 2: Univariate forecasting regressions
Increasing coefficients up to 3- years, levels off after
Emphasize 2-year cumulative returns from here on
Stronger results for HY bonds.
Consistent with idea that ISSEDF reflects pricing of credit risk
Results hold even with number of interest rate and macro controls
→Parallel results for HYS

17. Quality and Quantity during Credit Booms
Introduction Empirical Strategy Issuer Quality Forecasting Results Interpretation Summary
Quality and Quantity during Credit Booms
Measure aggregate credit growth using Compustat as DDt/Dt-1. Similar results using Flow of Funds data.
A numbers of theories suggests that ISSEDF will be high (i.e., quality is low) when aggregate credit growth is high
In principle, both quantity and quality may be useful for forecasting future returns
Quality should outperform in a horserace if uninformative common shocks affect the issuance of both low and high quality firms
r(DDAgg/DAgg,ISSEDF) = 0.45

18. Quantity vs. Quality Table 4: Panel A
Introduction Empirical Strategy Issuer Quality Forecasting Results Interpretation Summary
Quantity vs. Quality
Table 4: Panel A
Quality beats Quantity in a horserace
Credit growth of low quality firms is most
useful for forecasting returns
Differential debt
growth of low vs. high quality firms is a strong predictor
→Similar results for HYS

19. What drives time-variation in expected credit returns?
Introduction Empirical Strategy Issuer Quality Forecasting Results Interpretation Summary
What drives time-variation in expected credit returns?
Rational consumption-based (integrated-markets) explanations:
Time-varying quantity of risk
Time-varying rational price of risk
Frictional account: Changes in intermediary capital → changes in risk premia
Agency problems: Low interest rates → “Reaching for yield” → Mispricing
Investors make expectational errors:
Extrapolation of recent outcomes → under/over-weight the probability of left-tail events → Mispricing

20. Changes in the Rational Price of Risk
Introduction Empirical Strategy Issuer Quality Forecasting Results Interpretation Summary
Changes in the Rational Price of Risk
Counter-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

21. Time-varying risk premia
Introduction Empirical Strategy Issuer Quality Forecasting Results Interpretation Summary
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

22. Forecasting Reliably Negative Excess Returns
Introduction Empirical Strategy Issuer Quality Forecasting Results Interpretation Summary
Forecasting Reliably Negative Excess Returns
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
Figure 5,
Panel B:
Tests avoids joint hypothesis problem; have been used by Fama and Schwert (1977), Fama and French (1988), Kothari and Shanken (1997), Baker and Wurgler (2000).
Conclusion: Overall, we argue that our results are difficult to reconcile with integrated-markets models in which the rationally-determined price of risk fluctuates in a countercyclical fashion.

23. Frictional Account: Intermediary capital
Introduction Empirical Strategy Issuer Quality Forecasting Results Interpretation Summary
Frictional Account: Intermediary capital
Fluctuations 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:
Equity/Assets, Asset Growth, Bank Credit Losses

24. Frictional Account: Intermediary capital
Introduction Empirical Strategy Issuer Quality Forecasting Results Interpretation Summary
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

25. Insurer balance sheets
Introduction Empirical Strategy Issuer Quality Forecasting Results Interpretation Summary
Insurer balance sheets
Equity
Capital, or Asset
growth
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
Negative results don’t imply that intermediary capital isn’t ever part of the story.
Might matter at certain points, but quality may integrate different factors over time.

26. Agency-based Explanation: “Reaching for Yield”
Introduction Empirical Strategy Issuer Quality Forecasting Results Interpretation Summary
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)
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

27. Introduction Empirical Strategy Issuer Quality Forecasting Results Interpretation Summary
“Reaching for Yield”
Table 11: Impact of yields and changes in yields on ISSEDF
1-yr changes
Levels
2-yr changes
Results are consistent with “reaching for yield” hypothesis, but probably far from definitive.
→ Similar results for HYS

28. Investor-beliefs based explanation
Introduction Empirical Strategy Issuer Quality Forecasting Results Interpretation Summary
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

29. Extrapolative Beliefs
Introduction Empirical Strategy Issuer Quality Forecasting Results Interpretation Summary
Extrapolative Beliefs
Potential 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 probabilities
Generates short-term return continuation, longer-term reversals
Add a corporate sector that levers up when debt is “cheap”
Growing optimism → borrower quality erodes
Spreads under-react to erosion in borrower quality in booms → both quality and credit spreads forecast returns

30. Extrapolative Beliefs
Introduction Empirical Strategy Issuer Quality Forecasting Results Interpretation Summary
Extrapolative Beliefs
Consistent 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
→ Similar results for HYS

31. Conclusions Summary: Interpretation: Future work:
Introduction Empirical Strategy Issuer Quality Forecasting Results Interpretation Summary
Conclusions
Summary:
Issuer quality is low → future corporate bond excess returns are low
Evidence of mispricing: forecast significantly negative excess returns
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