Personal Financial Distress, Limited Attention

• Published date: 01 March 2022
• Author: HADIYE ASLAN
• Date: 01 March 2022
• DOI: http://doi.org/10.1111/1475-679X.12409

DOI: 10.1111/1475-679X.12409

Journal of Accounting Research

Vol. 60 No. 1 March 2022

Printed in U.S.A.

Personal Financial Distress, Limited

Attention

HADIYE ASLAN∗

Received 19 June 2019; accepted 31 August 2021

ABSTRACT

By linking sell-side equity analysts to their deed records and LinkedIn pro-

files, I show that analysts with higher exposure to negative wealth shocks issue

more pessimistic and less accurate forecasts. The effects are stronger when

analysts have higher leverage in their homes and face career concerns. I also

find that stocks recommended by exposed analysts underperform those of

nonexposed counterparts, by an amount that is significant and economically

large in magnitude. The results remain robust to unobserved skill differences,

the potential endogeneity of housing prices, the self-selection of analysts into

neighborhoods with certain traits, and placebo tests where housing wealth

shocks are randomized across analysts. Collectively, this study provides new

∗J. Mack Robinson College of Business, Georgia State University

Accepted by Luzi Hail. I thank an anonymous referee and the associate editor for helpful

comments. For helpful comments and discussions, I also thank seminar participants at Babson

College, Federal Reserve Board of Governors, U.S. Securities and Exchange Commission, Uni-

versity of Alabama, University of Illinois at Chicago, University of Texas-San Antonio, CEIBS

Finance and Accounting Symposium, and Southeast Summer Accounting Research Confer-

ence. An online appendix to this paper can be downloaded at http://research.chicagobooth.

edu/arc/journal-of-accounting- research/online-supplements

97

© 2021 The Chookaszian Accounting Research Center at the University of Chicago Booth School of

Business

98 h. aslan

evidence on if and how personal wealth shocks impact analysts’ work produc-

tivity and forecast behavior.

JEL codes: D10, D91, G41, M41

Keywords: financial distress; limited attention; sell-side analysts

1. Introduction

An analyst’s job involves synthesizing large amounts of hard and soft infor-

mation from conference calls, company filings, and news resources, all of

which impose extraordinary demands on the analyst’s time and attention

to detail. However, like any other decision maker, analysts are subject to

limited attention constraints, and it is conceivable that distracting events—

such as adverse personal wealth shocks—can affect their work productivity.

The investigation of wealth-related distraction, however, poses both con-

ceptual and empirical challenges. Conceptually, if personal wealth shocks

are temporary and eventually reverse, prima facie, they should not affect

analysts’ behavior. Empirically, there is the challenge of identifying the in-

dependent role of personal wealth shocks on other outcomes. The sudden

and unexpected nature of the 2007–2008 financial crisis renders a unique

opportunity to study the effects of wealth shocks and the impact of limited

attention on analysts’ work productivity.12 Using the 2007–2008 period as a

laboratory and by linking analysts’ deed records to their background infor-

mation, I find that sell-side analysts with higher exposure to adverse wealth

shocks issue more pessimistic and less accurate forecasts.

Generally, the effects of adverse personal wealth shocks on analysts’ work-

related productivity is ambiguous. I posit that a rapid and sudden drop in

personal wealth—because of housing market distress—is largely an unex-

pected life event that triggers anxiety (Lashley et al. [2009], McInerney,

Mellor, and Nicholas [2013]) and distracts analysts from their work-related

1The expansion of mortgage credit in the boom period was not confined to only poor

households. Foote, Loewenstein, and Willen [2016] show that, during the housing market

crash, the portion of total delinquency value accounted for by low-income zip codes declined.

Liebowitz [2009] reports that “the foreclosure rate for prime loans grew by 488% compared

to a growth rate of 200% for subprime foreclosures.”

2Although prices reversed, data show that home price declines were long-lasting during

the 2007 crash. The first phase of price declines lasted more than two years, and the reversal

between 2009 and 2010 was only partial. After a decade, nationwide housing prices were still

short by 1% in nominal terms and 20% in real terms from the bubble peak in July 2006. In

addition, we (as econometricians) can only identify the severity of home value declines and

households’ exposure to wealth shocks ex post by examining past market data. With market

signals being largely noisy—especially during the 2007–2008 crash—households could have

exhibited incomplete learning and suboptimal responses to financial shocks. Indeed, survey

data from the Rand American Life Panel—which analyzes household expectations on house

prices—find that, in 2009, about 63% and 51% of homeowners still did not expect an increase

in home values in the next 12 months and five years (2009–2014), respectively.

personal financial distress, limited attention 99

activities.3Because attention is a scarce resource, analysts could limit the

amount of time and effort they allocate to their work (Hirshleifer and Teoh

[2003]), resulting in more inaccurate forecasts. However, it is also plausible

that analysts react to a sudden loss of wealth by working harder and mak-

ing more accurate and optimistic forecasts as they attempt to ensure em-

ployment security (Hong, Kubik, and Solomon [2000], Hong and Kubik

[2003]) and win back personal wealth that was lost.4Overall, the question

is an important one because idiosyncratic events in analysts’ personal lives

can lead to biased forecast outcomes and potentially affect resource alloca-

tion.

To test these hypotheses, I estimate a series of regression models that

relate analysts’ relative forecast errors to changes in their housing wealth

during the 2007–2008 crisis. The detailed data on analysts’ living situation

allow me to exploit the large variation in house price changes—which

appear to be random across analysts—within the same geographic areas

across different zip codes. Using this setting, I can test whether changes

in analysts’ personal wealth—triggered by an unexpected shock to house

value—influence their forecast behavior and investment recommenda-

tions. The ability to observe repeated forecast outcomes and the return

performance of recommended investment portfolios related to a particular

analyst over time allows me to exploit within-analyst variations.

Several empirical challenges remain to isolate pure treatment effects.

Ideally, one would like to compare the performance of analysts exposed to

negative wealth shocks with that of their similar nonexposed counterparts.

However, brokerage houses are likely to differ in their procedures and poli-

cies, such as the number of resources that are made available to analysts,

evaluation techniques, opportunities for training, and the accessibility of

within- and cross-industry sources, all of which could lead to differential

job performance. Thus, I compare only equity analysts who both are em-

ployed by the same brokerage house and reside in the same local area but

are subject to differential real estate shocks at the zip code level.

The baseline results suggest that sell-side equity analysts who are exposed

to housing wealth shocks produce less accurate and more pessimistic earn-

ings forecasts relative to their counterparts employed by the same broker-

age house and living in the same local area. The treatment effect is econom-

ically significant: A one-standard deviation decrease in analysts’ housing

wealth lowers their forecast accuracy by about 9%–16% (from the average

3For instance, Gray, Maguen, and Litz [2004] compare housing distress to the grief experi-

enced by families and individuals who have lost close friends and relatives.

4The psychology literature suggests that negative emotions, such as those caused by sudden,

unexpected events, are related to the use of more complex and careful processing strategies

(Isen et al. [1982], Schwarz [1990], Jorgensen [1998]), more gathering (Hildebrand-Saints

and Wearx [1989]), and more chunking of diagnostic information (Isen, Daubman, and Gor-

goglione [1987]).