Anchoring on Credit Spreads

• Author: EDWARD D. VAN WESEP,CHRISTOPHER A. PARSONS,JOSEPH ENGELBERG,CASEY DOUGAL
• Published date: 01 June 2015
• DOI: http://doi.org/10.1111/jofi.12248
• Date: 01 June 2015

THE JOURNAL OF FINANCE •VOL. LXX, NO. 3 •JUNE 2015

Anchoring on Credit Spreads

CASEY DOUGAL, JOSEPH ENGELBERG, CHRISTOPHER A. PARSONS,

and EDWARD D. VAN WESEP∗

ABSTRACT

This paper documents that the path of credit spreads since a firm’s last loan influ-

ences the level at which it can currently borrow. If spreads have moved in the firm’s

favor (i.e., declined), it is charged a higher interest rate than is justified by current

fundamentals, whereas if spreads have moved to the firm’s detriment, it is charged a

lower rate. We evaluate several possible explanations for this finding, and conclude

that anchoring to past deal terms is most plausible.

VIRTUALLY ALL BORROWERS PAY a spread above the risk-free rate, a premium that

compensates lenders for expected losses in default, illiquidity, and other con-

siderations. Calculating the appropriate spread requires answering a number

of questions. How likely is the firm to default, and over what horizon? If it does

default, how big are the losses? Is default more likely to occur in bad economic

times, or is it largely dependent on firm-specific factors? How easy will it be

to sell the firm’s bonds or bank notes, and will this be more difficult during

certain economic states?

The common element to these questions is their perspective: they are all

forward-looking. Retrospective information is thought to be relevant only to

the extent that it improves the lender’s estimate of forward-looking variables,

data that are purely historical, in the sense that they do not provide informa-

tion about the firm’s creditworthiness, should not affect spreads. This paper

provides evidence, however, that noninformative historical signals do in fact

influence borrowing costs, suggesting that observed credit spreads likely depart

from a fully rational benchmark spread.

What do we mean by noninformative historical signals? Suppose that two

neighbors living across the street from one another both want to refinance their

∗Dougal is at the LeBow College of Business at Drexel University, Engelberg and Parsons are

at the Rady School of Management at the University of California, San Diego, and Van Wesep

is at the Vanderbilt University Owen Graduate School of Management. We thank Aydo˘

gan Altı,

Malcolm Baker, Adolfo DeMotta, Jan Ericsson, Dirk Hackbarth, Jay Hartzell, David Hirshleifer,

Victoria Ivashina (discussant), Anil Shivdasani, Sheridan Titman, and Siew Hong Teoh. We also

thank seminar participants at UNC, McGill, DePaul, University of Michigan, UC Irvine, SFS

Cavalcade, University of Washington Florida State University, 2012 AFA Meeting, University of

Oregon, Brigham YoungUniversity, Tilburg University,Erasmus University, Duisenberg School of

Finance, Indiana University, and the 2011 NBER Corporate and Behavioral Programs in Chicago

for helpful feedback.

DOI: 10.1111/jofi.12248

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1040 The Journal of Finance R

home loans, and that prevailing rates on 30-year mortgages are currently 6% on

average. Neighbor 1 originated his mortgage five years ago, when average rates

were 8%, and neighbor 2 originated her mortgage 10 years ago, when average

rates were 4%. It would be surprising if this differential timing in prior loan

originations translated to a difference in borrowing costs on new loans today:

we would not expect neighbor 1 to pay a higher rate today than neighbor 2,

given that the only (observable) distinction is when each happened to have last

borrowed. Yet this is precisely what we find in the syndicated loan market. On

average, when a firm borrows money from a bank, the level of aggregate credit

spreads when the firm last borrowed correlates with the spread it receives on a

new loan. The pattern is consistently one of stickiness, whereby firms that last

borrowed when spreads were high pay a premium, and firms that last borrowed

when spreads were low receive a discount.

To give a specific example, suppose that a BBB-rated firm took out a five-year

term loan in 2000, and then another in 2003. Suppose also that the average

BBB credit spread rose by 50 basis points (bps) over these three years. We find

that, relative to other BBB borrowers in 2003, the firm will receive a discount

of approximately 6 bps, negating 12% of the change in aggregate spreads.

To demonstrate how bizarre this finding is, in the preceding example we

excluded all firm-specific information. However, prior aggregate spreads must

ultimately affect current borrowing costs through some firm-specific variable.

The most likely candidate is the firm’s actual spread at the time it last borrowed.

When we extend the analysis to consider the specific spread at which the firm

last borrowed, the results strengthen. Returning to the example, instead of

taking the difference between average BBB term loan indices in 2000 and

2003, we take the difference between average BBB term loan spreads in 2003

and the actual spread the firm paid in 2000. Here, a firm’s current borrowing

spread is dragged about 20% of the way toward the spread at which it last

borrowed.

Our preferred explanation for this finding is anchoring, whereby borrowers

and/or lenders are influenced by past deal terms and, perhaps unintentionally,

allow seemingly stale signals to enter negotiations. The spread that borrowers

pay on bank debt is typically similar to prevailing spreads paid by similar bor-

rowers in the recent past, but there is always uncertainty as to the precise risks

that the lender faces for any given borrower. Further, information asymmetry

can yield surplus from a particular lender-borrower match. Both facts suggest

a range of feasible interest rates for any given borrower, implying that its ulti-

mate borrowing cost may be subject to a variety of psychological influences, of

which anchoring is one.

Four types of evidence support an anchoring interpretation, two positive and

two negative. First, there appears to be an overt fixation on a deal’s “headline

terms” in particular, the past spread, in current spread negotiations. Conse-

quently, when we compare past and current spreads, we observe a dispropor-

tionate number of loans made at exactly the same rate, even when market

conditions have changed drastically. This is visually apparent in Panel A of

Figure 1, which plots the percentage change in spread for firms borrowing both

Anchoring on Credit Spreads 1041

Panel A: Change in spreads before/during the crisis

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Percent change in spread

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Panel B: Average spreads over time by debt rating

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Average spread (Log scale)

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Figure 1. Spreads before and during the financial crisis of 2008. Panel A plots the his-

togram of spread changes for every firm in our sample that took out a line of credit from a banking

syndicate exactly once during the interval 2005 to 2007, and then once again in 2008. Panel B plots

the average spread above LIBOR for long-term lines of credit during 2005 to 2008. We only include

firms that maintained the same credit rating between borrowing events. The histogram shows the

percentage change in spreads between the first, precrisis loan and the second, postcrisis loan.