Diagnostic Expectations and Credit Cycles

• Author: NICOLA GENNAIOLI,ANDREI SHLEIFER,PEDRO BORDALO
• DOI: http://doi.org/10.1111/jofi.12586
• Date: 01 February 2018
• Published date: 01 February 2018

THE JOURNAL OF FINANCE •VOL. LXXIII, NO. 1 •FEBRUARY 2018

Diagnostic Expectations and Credit Cycles

PEDRO BORDALO, NICOLA GENNAIOLI, and ANDREI SHLEIFER∗

ABSTRACT

We present a model of credit cycles arising from diagnostic expectations—a belief for-

mation mechanism based on Kahneman and Tversky’s representativeness heuristic.

Diagnostic expectations overweight future outcomes that become more likely in light

of incoming data. The expectations formation rule is forward looking and depends on

the underlying stochastic process, and thus is immune to the Lucas critique. Diagnos-

tic expectations reconcile extrapolation and neglect of risk in a unified framework. In

our model, credit spreads are excessively volatile, overreact to news, and are subject

to predictable reversals. These dynamics can account for several features of credit

cycles and macroeconomic volatility.

THE FINANCIAL CRISIS OF 2008 TO 2009 REVIVED INTEREST among economists and

policy makers in the relationship between credit expansion and subsequent fi-

nancial and economic busts. According to an old argument (e.g., Minsky (1977)),

investor optimism brings about the expansion of credit and investment, and

leads to a crisis when such optimism abates. Stein (2014) echoes this view by

arguing that policy makers should be mindful of credit market frothiness and

consider countering it with policy. Inthis paper, we develop a behavioral model

of credit cycles with microfounded expectations that is consistent both with the

Minsky narrative and with a great deal of evidence.

Recent empirical research has documented a number of credit cycle facts.

Using a sample of 14 developed countries between 1870 and 2008, Schularick

and Taylor (2012) demonstrate that rapid credit expansions forecast declines in

real activity.Jorda, Schularick, and Taylor (2013) further find that more credit-

intensive expansions are followed by deeper recessions. Mian, Sufi, and Verner

(2017) show that growth in household debt predicts economic slowdowns, Baron

and Xiong (2017) show for a sample of 20 developed countries that bank credit

expansion predicts increased crash risk in both bank stocks and equity markets

∗Pedro Bordalo is at Sa¨

ıd Business School, University of Oxford. Nicola Gennaioli is at Univer-

sita Bocconi and IGIER. Andrei Shleifer is at Harvard University. Gennaioli thanks the European

Research Council and Shleifer thanks the Pershing Square Venture Fund for Research on the

Foundations of Human Behavior for financial support of this research. The authors are also grate-

ful to Nicholas Barberis, Bruce Carlin, Lars Hansen, Sam Hanson, Arvind Krishnamurthy,Gordon

Liao, Yueran Ma, Matteo Maggiori, Sendhil Mullainathan, Andreas Schaab, Josh Schwartzstein,

Jesse Shapiro, Alp Simsek, Jeremy Stein, Amir Sufi, David Thesmar, Chari Varadarajan, Wei

Xiong, Luigi Zingales, and two anonymous referees for helpful comments. The authors have no

conflicts of interest to declare.

DOI: 10.1111/jofi.12586

199

200 The Journal of Finance R

more broadly, and Fahlenbrach, Prilmeier, and Stulz (2016) find, in a cross-

section of U.S. banks, that rapid loan growth predicts poor loan performance

and low bank returns in the future.

Similar findings emerge from an examination of credit conditions. Green-

wood and Hanson (2013) show that the credit quality of corporate debt issuers

deteriorates during credit booms, and that a high share of risky loans fore-

casts low, and even negative, corporate bond returns. Gilchrist and Zakrajˇ

sek

(2012) and Krishnamurthy and Muir (2015) show that credit tightening cor-

rectly anticipates recessions. Lopez-Salido, Stein, and Zakrajsek (2017) find

that low credit spreads predict both a rise in credit spreads and low economic

growth afterwards. They stress predictable mean-reversion in credit market

conditions.1In Section I, we offer preliminary evidence that survey forecasts

of credit spreads are excessively optimistic when these spreads are low, and

that both errors and revisions in forecasts are predictable. Overall, the exist-

ing evidence is hard to square with rational expectations, indicating a need for

a behavioral approach to modeling credit cycles.

We propose a behavioral model that both accounts for this evidence and

describes in a dynamic setup how credit markets overheat. We begin with a

psychologically founded model of beliefs and their evolution in light of new

data.2This model was developed to account for judgment biases that are well

documented in the lab, such as the conjunction and disjunction fallacies and

base rate neglect, and is therefore portable in the sense of Rabin (2013). The

model relies on Gennaioli and Shleifer’s (2010) formalization of Kahneman

and Tversky’s (1972) representativeness heuristic. According to Kahneman

and Tversky, a certain attribute is judged to be excessively common in a pop-

ulation when that attribute is diagnostic for the population, meaning that it

occurs more frequently in the given population than in a relevant reference

population (Tversky and Kahneman (1983)). For example, after seeing a pa-

tient test positive on a medical test, doctors tend to overestimate the likelihood

that the patient has the disease because sick people are more frequent in the

population of positive tests relative to the population of negative tests, even

when they are few in absolute terms (Casscells, Schoenberger, and Graboys

(1978)).

This idea can be naturally applied to modeling expectations in a macroeco-

nomic context. Similar to the medical test example, agents overweight those fu-

ture states whose likelihood increases the most in light of current news relative

to what they know already. Thus, just as doctors overestimate the probability

of sickness after a positive test result, agents overestimate the probability of a

1See also Bernanke (1990), Friedman and Kuttner (1992), and Stock and Watson (2003), among

others.

2Many models of beliefs in finance are motivated by psychological evidence, but often use

specifications specialized to financial markets (e.g., Muth (1961), Barberis, Shleifer, and Vishny

(1998), Rabin and Vayanos (2010), Fuster, Laibson, and Mendel (2010), Hirshleifer, Li, and Yu

(2015), Greenwood and Hanson (2015), Barberis et al. (2015)). Fuster, Laibson, and Mendel (2010)

review lab and field evidence on deviations from rational expectations.