Off-Balance Sheet Arrangements and Company Performance

during the Recent Financial Crisis

James H. Irving

jirving@clemson.edu

Assistant Professor of Accounting

School of Accountancy & Finance

Clemson University

Clemson, SC 29634

Kimberly J. Smith

kim.smith@mason.wm.edu

Professor and KPMG Accounting Fellow

Mason School of Business

College of William and Mary

Williamsburg, VA 23187

ABSTRACT: We investigate whether company performance during the 2007-2009 financial crisis is related to

the use of off-balance sheet arrangements (OBSAs), specifically those related to transferred

financial assets and variable interest entities. Using propensity-score matching for a sample of

non-bank companies, we show that the use of these OBSAs is associated with almost 10 percent

lower buy-and-hold returns during the crisis. We do not find a significant negative relation for a

placebo test one year before the crisis. In addition, we find no relation for companies that use

only on-balance sheet accounting.

Document Date: 19 May 2014

DRAFT−PLEASE DO NOT QUOTE

We appreciate comments from workshop participants at Clemson University, The College of William & Mary, and

from participants at the 3rd

Global Conference on Transparency Research hosted by HEC Paris. We also

acknowledge the support of a Steve Berlin/CITGO Grant received from the American Accounting Association in

August 2013. We thank Joe Carnazza for his excellent work on the data collection for this project.

1

1. Introduction

The recent financial “subprime” crisis (2007-2009) resulted in historic declines in the

market capitalization of publicly-traded companies. Studies of bank performance during the

crisis find that variation in crisis performance is explained by pre-crisis leverage, performance,

regulatory capital, and risk culture, but not with differences in compensation, governance, or

regulation (see e.g., Fahlenbrach and Stulz 2011; Beltratti and Stulz 2012; Fahlenbrach et al.

2012; Berger and Bouwman 2013). Researchers also have concluded that banks engaged in

regulatory arbitrage by structuring arrangements to remain off their balance sheets even when

risk had not been transferred, and banks with more exposure performed more poorly during the

crisis (see e.g., Higgins and Mason 2004; Niu and Richardson 2006; Gorton and Souleles 2007;

Bens and Monahan 2008; Acharya et al. 2013). In this study, we examine a sample of 353 non-

bank companies to determine whether their use off-balance sheet arrangements is associated with

crisis performance.

Although non-bank companies do not have the same incentives for regulatory arbitrage as

banks, there is evidence that they avoid reporting leverage. For example, Imhoff and Thomas

(1988) demonstrate that companies restructured their leases upon the issuance of new accounting

standards requiring the capitalization of leases. Callahan et al. (2012) find that companies

restructured their variable interest entities upon the issuance of FIN 46. Engel et al. (1999)

identify a group of companies that engaged in costly repackaging of debt into preferred stock

classifiable as equity on company balance sheets. In fact, the U.S. Securities and Exchange

Commission, in its study of off-balance sheet arrangements, notes that “…some transfers of

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financial assets appear to be significantly, primarily, or even solely entered into with accounting

motivations in mind” (SEC 2005, 45).1

The financial crisis is a powerful setting in which to study these off-balance sheet

arrangements because the bursting of the housing bubble and the subsequent spread of the

subprime crisis to other markets and to the broader economy was arguably unexpected by

investors (e.g., Acharya and Richardson 2009; Almeida et al. 2011; Longstaff 2010; Manconi et

al. 2010). The crisis shock dramatically increased the likelihood that companies would face the

need to provide support for (i.e., “rescue”) related unconsolidated entities (i.e., off-balance sheet

arrangements), either via the triggering of explicit contractual obligations and/or making the

choice to trigger implicit guarantees (e.g., moral recourse). As such, we expect the performance

of companies during the crisis would reflect the realization of bad outcomes stemming from the

use of off-balance sheet entities—outcomes that were heretofore viewed by many as

encompassing minimal risk.

We select a sample of companies that are involved in off-balance sheet arrangements,

broadly defined. We then gather data on the use of two specific types of off-balance sheet

arrangements: transfers of financial assets to unconsolidated entities and other unconsolidated

variable interest entities. These data are hand-collected from each company’s last Form 10-K

filing issued before the crisis. We find that 102 of our 353 sample companies use at least one of

these off-balance sheet arrangements as of the end of 2006. Of these 102 companies, 69 use

unconsolidated entities to transfer financial assets, 58 are involved with unconsolidated variable

1 The SEC’s “Report and Recommendations Pursuant to Section 401(c) of the Sarbanes-Oxley Act of 2002 on

Arrangements with Off-Balance Sheet Implications, Special Purpose Entities, and Transparency of Filings by

Issuers,” can be found at http://www.sec.gov/news/studies/soxoffbalancerpt.pdf.

3

interest entities, and 25 use both types of off-balance sheet arrangements. We find that pre-crisis

use of these arrangements is reasonably stable over the 2003 – 2006 time period.

We use two different methodologies to estimate the effect of using off-balance sheet

arrangements on company performance during the crisis. First we compare the performance of

the 102 sample companies reporting the use of off-balance sheet arrangements with the

performance of the 251 companies that do not report the off-balance sheet arrangements using

linear regression. However, this method restricts our analysis by assuming an identical linear

relation for companies with and without off-balance sheet arrangements. In addition, the analysis

may be biased by endogeneity introduced by the fact that companies choose to become involved

with off-balance sheet arrangements.

Our second analysis is based on a propensity-score matching approach (see Rosenbaum

and Rubin 1983; Rosenbaum 2002; Armstrong et al. 2010; Almeida et al. 2011). In this matched-

pair design, each company using off-balance sheet arrangements is paired with another company

that is similar along all other relevant observable dimensions. As a result, differences in

performance can theoretically be attributed to differences in use of off-balance sheet

arrangements. Armstrong et al. (2010, 227) state that “[t]his approach alleviates misspecification

that occurs when the research design assumes an incorrect functional form for the relationship

between the variables of interest (including controls) and the outcome.”

We find that companies using off-balance sheet arrangements delivered significantly

lower market-adjusted buy-and-hold returns during the crisis. Our linear model estimated this

difference as 6.3 percent and our matching model estimated the difference at 9.5 percent. Return

on assets during the crisis was also lower for companies using OBSAs, by approximately 1.5

percent, but this difference is significant only in the linear model. Consistent with prior literature,

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our control variables include size, beta, book-to-market, (on-balance sheet) leverage, free cash

flows, cash needs, buy-and-hold returns, and return on assets. These covariates are measured as

of the end of fiscal 2006. We also include a measure of marketable securities as a control, to

reflect the extent to which company losses were associated with their direct investments in

securities such as asset-backed securities. Placebo tests for the period one year before the crisis

do not find significant differences in the performance of companies with and without off-balance

sheet arrangements. We conclude from this analysis that companies using off-balance sheet

arrangements performed more poorly during the financial crisis, which suggests that these

companies were exposed to more risk than their counterparts not using off-balance sheet

arrangements.

Although our primary focus is the analysis of off-balance sheet arrangements, we

recognize that companies may also be involved with transferred financial assets or variable

interest entities that are fully reported on their balance sheets—either by design or as a result of

failing the required conditions for off-balance sheet treatment. Since companies with greater

exposure to the risks of a special purpose entity are required to use on-balance sheet treatment,

companies with entities reported on the balance sheet are arguably more exposed to risks from

those entities. If the accounting standards in place before the crisis were effective in allowing

companies to use off-balance sheet treatment only for entities with minimal risk, we would

expect that an economy-wide shock would, ceteris paribus, lead to poorer performance for

companies with on-balance sheet arrangements than for companies with similar arrangements

that are off company balance sheets. Alternatively, if companies achieving off-balance sheet

treatment were structuring their arrangements to keep them off the balance sheet—even if risk

had not transferred—these entities may expose the company to as much risk as similar

5

arrangements reported on company balance sheets (see Niu and Richardson 2006). Thus, we

would not expect to find a difference in the crisis performance of companies with arrangements

that were reported on versus off the company’s balance sheet.

We find that 116 of our sample companies are involved with off-balance sheet

arrangements or on-balance sheet arrangements, and 43 of these companies report both on- and

off-balance sheet arrangements. We set these 43 companies aside and compare crisis

performance for two other subgroups of companies: the 59 companies reporting only off-balance

sheet arrangements (i.e., no on-balance sheet arrangements) and the 14 companies reporting only

on-balance sheet arrangements (i.e., no off-balance sheet arrangements). In contrast to our

expectations, the crisis performance for the 14 companies reporting only on-balance sheet

arrangements is not significantly lower than its matched sample. Although inferences are limited

due to sample size, this finding suggests that these companies performed no differently than, for

example, companies that created wholly-owned subsidiaries (rather than variable interest

entities) to conduct their business ventures. In contrast, the 59 companies reporting only off-

balance sheet arrangements experienced lower market-adjusted buy-and-hold returns (9.7

percent) and ROAs (3.1 percent) than their matched companies that use neither off- nor on-

balance sheet arrangements. The significance for this subsample is smaller than for our full

sample (0.05 < α < 0.10), but this finding is consistent with the idea that the accounting rules

may not have effectively differentiated the risk exposures and suggests the possibility that

investors’ pre-crisis estimates of company value were too high for companies with off-balance

sheet arrangements. Our finding is also consistent with the idea that investors “underweight or

even ignore” information that is disclosed rather than recognized in financial statements

(Schipper 2007, 322).

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We also investigate the crisis period performance separately for transferred financial

assets and variable interest entities. As a result of the crisis, the accounting for transferred

financial assets was radically changed, eliminating the exemption that protected the off-balance

sheet status of many securitization entities (see SFAS No.166).2 This change was made due to

“concerns of financial statement users that many of the financial assets (and related obligations)

that have been derecognized should continue to be reported in the financial statements of

transferors” (FASB 2009). In contrast, while the accounting standards for variable interest

entities were also changed, the changes were less radical in nature. If the accounting rules for

transfers of financial assets were less effective in capturing the economics of the arrangements

before the crisis, then investors may have had more difficulty understanding the company’s true

exposure to these arrangements. Consequently, we would expect companies with transferred

financial assets to perform more poorly during the crisis than companies with variable interest

entities.

We find evidence consistent with our expectation when we consider only off-balance

sheet arrangements. Specifically, we find that the 69 companies with off-balance sheet entities

related to transfers of financial assets experienced 15.2 percent lower (at α < .01) market-

adjusted buy-and-hold returns; for the 58 companies with off-balance sheet variable interest

entities, returns were 7.5 percent lower (and not significantly different from zero). However,

when we consider on- and off-balance sheet arrangements, jointly or separately, we do not find

this result. In fact, when we consider companies with on- and off-balance sheet arrangements,

our findings are reversed: the 26 companies with both on- and off-balance sheet variable interest

2 Under SFAS No. 140, financial assets transferred via arrangements that conveyed control of the assets to the

special purpose entity were denoted “qualifying special purpose entities” and were exempted from the variable

interest entity rules of FIN 46(R).

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entities produced market-adjusted buy-and-hold returns 21.4 percent lower than their matched

companies, while the 15 companies with both on- and off-balance sheet entities related to

transfers of financial assets experienced no difference. Thus we cannot conclude that the

accounting for transfers of financial assets played a larger role in the crisis than the accounting

for variable interest entities.

In summary, our study contributes to the literature on off-balance sheet arrangements as

well as to the literature examining variation in crisis period returns. We believe our finding that

the performance of companies with off-balance sheet arrangements is significantly lower during

the financial crisis, but not before the crisis, provides strong evidence that the use of off-balance

sheet arrangements concealed risk from shareholders. This effect occurred in spite of post-Enron

improvements in financial reporting such as FIN 46 (see Callahan et al. 2012). Our failure to find

lower performance for companies using (only) on-balance sheet arrangements, although subject

to sample size concerns, reinforces this view. Finally, our finding that both variable interest

entities and entities used to transfer financial assets exhibit lower crisis performance suggests

that concerns about off-balance sheet arrangements are not limited to securitization entities.

These findings raise important questions about whether and how disclosures could take the place

of recognition on company balance sheets. The current disclosure framework projects undertaken

by the FASB and IASB provide an opportunity to carefully consider how best to communicate

off-balance sheet arrangement risks, especially as they pertain to “tail” events such as the

financial crisis.

The remainder of this paper is divided into four sections. Section 2 describes our sample

and Section 3 describes off-balance sheet arrangements before the crisis. Section 4 presents our

empirical analysis. Section 5 concludes.

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2. Sample

We study the effect of OBSAs on crisis performance for a sample of 353 companies that

have met certain criteria. First, we control for the existence of certain other off-balance sheet

arrangements (based on OBSAs identified in SEC (2005)) by selecting only companies that

report equity-method investments, operating leases, and defined-benefit retirement plans as of

the end of fiscal year 2005.3,4

This approach reduces the likelihood that our measures of off-

balance sheet arrangements based on transferred financial assets and variable interest entities

merely proxy for the use of these other off-balance sheet arrangements. Second, we required that

each company have complete data necessary to estimate our regression models. In implementing

this selection process, we began with 7,258 companies reporting non-null net income and stock

prices for fiscal year 2005 from the Compustat Combined Industrial Annual dataset. Due to data

limitations, we further eliminate foreign companies that trade in the U.S. using an American

Depository Receipt. We also exclude companies with dual classes of stock.

Table 1 compares our sample to a broader Compustat sample. Panel A compares our

industry distribution to that of Compustat. Our sample has more manufacturing and

transportation companies, but fewer financial and services companies than does Compustat.

Panel B compares our sample on a set of financial measures (see Appendix A for definitions).

The total assets and market capitalization are substantially larger than the Compustat sample.

The leverage of our sample is 14 percent (of total market capitalization), as compared to 9

3 Although companies do engage in other off-balance sheet arrangements (e.g., purchase obligations), incorporating

the use of such arrangements into sample selection is much more difficult as data for these types of arrangements is

not consistently available on Compustat.

4 More specifically, we narrowed the sample to companies with non-zero entries for equity-method investments

(Compustat data items IVAEQ (#31) or ESUB (#55)) and rental commitments (Compustat data item MRC1 (#96)),

as well as pension projected benefit obligation (Compustat data item PBPRO (#286)) from the Pension Annual

dataset.

9

percent for the Compustat sample. Return on assets for our sample is also higher, but book to

market does not differ for the two samples. In summary, our sample extends the study of off-

balance sheet arrangements beyond the financial industry but focuses on companies that are

larger, more highly leveraged, and more profitable.

3. Off-balance sheet arrangements before the crisis

3.1. Transferred financial assets and variable interest entities

As noted above, we study two types of off-balance sheet arrangements that were often

singled out as exacerbating factors in the financial crisis: transfers of financial assets to

unconsolidated entities where the company has continuing involvement and variable interests in

unconsolidated entities. These arrangements can yield real economic benefits. For example,

companies may transfer financial assets to an unconsolidated entity to transfer risk, achieve

lower-cost financing, and avoid bankruptcy costs (Schwarcz 2004; Gorton and Souleles 2007;

Schipper and Yohn 2007). In addition, companies may set up variable interest entities, such as

leasing transactions and research and development limited partnerships (Gorton and Souleles

2007), as a means of reducing agency costs, achieving optimal investment, capturing tax benefits,

and achieving economic efficiencies (Shah and Thakor 1987; Shevlin 1987; John and John 1991;

Beatty et al. 1995; Zhang 2006).

In spite of these benefits, the use off-balance sheet arrangements generally raises

concerns that investors may not understand all of the risks and obligations related to the entities

that remain off company balance sheets. Barth et al. (2012) showed that even credit rating

agencies may not have recognized risks related to securitizations. Some researchers argue that

investors will downplay items unless they are fully recognized as liabilities in company

consolidated balance sheets and reflected in important indicators associated with total

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borrowings, such as leverage ratios and debt covenant calculations (Schipper 2007). Other

researchers find that disclosure of off-balance sheet arrangements may be an adequate alternative

to recognizing the arrangements on the balance sheet, as long as the disclosures are complete and

salient (Bratten et al. 2013).5 But many have expressed concern that companies may use off-

balance sheet arrangements to “hide” leverage from investors and “manage” earnings. For

example, Mills and Newberry (2005) find that companies use more structured finance

arrangements when they are more credit-constrained or have incentives to manage debt ratings.

Feng et al. (2009) find that special purpose vehicles arranged for financial reporting purposes are

associated with increased earnings management.

The accounting rules for variable interest entities and transfers of financial assets during

the post-Enron period were intended to discourage manipulation by allowing off-balance sheet

treatment only if the majority of the risks and rewards related to the special purpose entity were

not held by the company.6 However, studies of securitizations find that some of these

arrangements did not provide the expected risk transfer (Higgins and Mason 2004; Niu and

Richardson 2006; Gorton and Souleles 2007; Acharya et al. 2013), and suggest that this failure to

transfer risk may relate to implicit recourse.7 Leading voices in the financial community argued

that it was the use off-balance sheet arrangements like these that “brought the financial system to

the brink of collapse” during the crisis (Partnoy and Turner 2010). For example, Robert Herz,

5 Although Bratten et al. (2013) show that lease disclosures are generally complete and salient, Chandra et al. (2006)

conclude that disclosures about off-balance sheet arrangements, more broadly defined, increased after Enron, but not

uniformly. Zechman (2010) finds that companies “with incentives to use off-balance sheet financing do not provide

transparent disclosure” (p. 725).

6 Note that the “risks and rewards” language was used in standards relating broadly to variable interest entities (see

FIN 46(R)). Financial assets transferred via arrangements that conveyed control of the assets to the special purpose

entity were denoted “qualifying special purpose entities” (see FAS 140) and were exempted from the variable

interest entity rules.

7 In a letter to CEOs of state member banks in 2002, the Federal Reserve Bank of Dallas states that”[i]mplicit

recourse is of supervisory concern because it demonstrates that the securitizing institution is re-assuming risk

associated with the securitized assets that the institution initially transferred to the marketplace.”

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former Chairman of the Financial Accounting Standards Board stated that off-balance sheet

treatment “masked the underlying risks” of the arrangements (Herz 2009). Stephen Schwarzman,

Founder and CEO of Blackstone, stated that “off-balance sheet vehicles that suddenly return to

the balance sheet to wreak havoc make a mockery of principles of disclosure” (Schwarzman

2008).

Implicit (moral) recourse is a term used to describe non-contractual agreements for a

company to provide support to a related entity beyond the company’s explicit contractual

obligation.8 Companies have incentives to provide this sort of support to maintain their

reputations and thus safeguard their access to the securitization markets, or to support companies

with which they have long-term strategic alliances (Higgins and Mason 2004; Gorton and

Souleles 2007; Ryan 2008).9 Accordingly, companies involved in these implicit recourse

arrangements may have had much more at risk than is reflected by their financial statements.

However, measuring and documenting implicit recourse arrangements is quite difficult, as these

arrangements are usually not documented. In addition, a company may not make the decision to

support the related entity until the entity is having financial difficulties. Thus, it is difficult for

investors to determine the existence, the magnitudes, and the associated probability of providing

support beyond the explicit contractual obligation. We believe that studying off-balance sheet

8 See the discussion of implicit guarantees in FIN 46(R), paragraph B10, which states that “[g]uarantees of the value

of the assets or liabilities of a variable interest entity, written put options on the assets of the entitiy, or similar

obligations such as some liquidity commitments or agreements (explicit or implicit) to replace impaired assets held

by the entity are variable interests if they protect holders of other interests from suffering losses..”

9 In his comment attached to Gorton and Souleles (2007), Tufano describes implicit recourse in securitizations as a

“wink-wink-wink equilibrium, where issuer, investor—and regulator—willingly turn a blind eye to the sponsor

providing credit support. In this equilibrium, even lenders to the firm are fully informed and do not object to the

credit support. To the contrary, all parties acknowledge that the bank might choose to voluntarily support the SPV in

all but the most dire circumstances, when it could not support itself first. In the same way that parents of healthy

adult sons and daughters are under no legal responsibility to continue to house and feed them, sponsors voluntarily

choose to take care of the liabilities of their progeny—the SPVs” (p. 599).

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arrangements during the financial crisis provides a unique opportunity to measure the effects of

using these arrangements.

3.2. Measuring Company Use of Off-Balance Sheet Arrangements

We measure the existence of off-balance sheet arrangements based on disclosures in SEC

10-K filings. We collect data from the Off-Balance Sheet Arrangements section of MD&A

required by the SEC’s Final Rule No. 67 (FR-67),10

as well as from the footnotes to the financial

statements. Table 2 provides descriptive statistics about off-balance sheet arrangements before

the crisis.

We categorize a company as using off-balance sheet arrangements (i.e., we set OFF = 1)

for each year if the company reports:

a) retained interests related to off-balance sheet transfers of financial assets in the Off-

Balance Sheet Arrangements section of MD&A, or

b) off-balance sheet transfers of financial assets in the footnotes to the financial statements,

or

c) off-balance sheet variable interest entities in the Off-Balance Sheet Arrangements section

of MD&A, or

d) off-balance sheet variable interest entities in the footnotes to the financial statements.

We set OFF= 0 if companies report none of the above.

Panel A of Table 2 presents the number of companies with and without off-balance sheet

arrangements. In 2006, we show that 102 companies (29 percent of the sample) report off-

balance sheet arrangements as described above, and 251 companies report none of the above.

10

FR-67 ( which can be found at http://www.sec.gov/rules/final/33-8182.htm) focuses on four specific types of

OBSA: 1) certain guarantees, 2) any retained or contingent interests the company might have in assets transferred to

an unconsolidated entity, 3) certain derivative instruments that are linked to the company’s stock, and 4) any

obligation arising from a variable interest as defined under FIN 46. The initial search terms we utilized were “off

balance” and “off-balance”, as FR-67 required information on these arrangements to be disclosed in a separate

section of MD&A, with a suggested title of “Off Balance Sheet Arrangements.” If the required section was not

found for a particular company, additional search terms were employed, including “retained,” “interest,” “retained

interest,” “VIE,” and “variable interest,” to further assist in locating the relevant disclosures in this section. In some

cases, although off-balance sheet arrangements were discussed in MD&A, these disclosures were not presented in a

separate section.

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Looking at prior years, we find that 99 companies (28 percent of the sample), 106 companies (30

percent of the sample) and 98 companies (28 percent of the sample) report off-balance sheet

arrangements in 2005, 2004, and 2003, respectively.

The net changes from year to year are relatively small. The gross changes (untabulated)

are slightly higher. For example, a total of 17 companies changed categories from 2005 to 2006.

Ten companies reported off-balance sheet arrangements in 2006 but not in 2005; seven

companies reported off-balance sheet arrangements in 2005 but not in 2006. A total of 13 (22)

companies changed categories from 2004 to 2005 (2003 to 2004). We conclude that the large

majority of companies are stable in their use of off-balance sheet arrangements over time

Panel B of Table 2 presents the number of companies reporting off-balance sheet

arrangements in the OBSA section of MD&A versus the footnotes to the financial statements. In

2006 we found that 84 companies reported off-balance sheet arrangements in the OBSA section

of MD&A and 86 reported such arrangements in the footnotes. Only 68 report the arrangements

in both places; and this number includes cases where companies mention an arrangement and

then present a cross-reference to the footnotes. 11

Again, these findings are stable over time.

Panel C of Table 2 presents the use of off-balance sheet arrangements across industries.

In general, the use of off-balance sheet arrangements is broadly distributed across industries, and

consistent over time. Thus, this sample of companies has the potential to provide insight into the

use of off-balance sheet arrangements beyond the banking sector.12

11

Note that the information in Panel B reconciles to that in Panel A. For example 84 + 86 – 68 = 102, which is the

number of companies reporting off-balance sheet arrangements in Panel A.

12 Our sample includes no companies with SIC codes 6000 – 6199.

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4. Use of off-balance sheet arrangements and company performance

Our central analyses in this study focus on the crisis-period performance of companies

involved in off-balance sheet arrangements. Below we first discuss the variables used in our

analyses. We then present descriptive statistics. Finally, we present the results from estimating

our multivariate linear models and our propensity-score matching models.

4.1. Variables and descriptive statistics

4.1.1. Measurement of company performance in the crisis and pre-crisis

We use two measures of company performance: market-adjusted buy-and-hold returns

and return on assets. We measure company Crisis returns using market-adjusted13

buy-and-hold

monthly returns (CRSP data item RET less CRSP data item VWRETD) from the CRSP Monthly

Stock File cumulated across July 2007 to December 2008.14

Similarly, we measure Pre-crisis

returns as market-adjusted buy-and-hold monthly returns from the CRSP Monthly Stock File

cumulated across the April 2006 to March 2007 window.

Panel A of Table 3 presents descriptive statistics for our measures of company

performance in both the financial crisis period and the pre-crisis period. The first set of columns

presents descriptive statistics for the full sample of 353 companies, which includes both 102

companies using off-balance sheet arrangements before the crisis (OFF=1) and 251 companies

not using off-balance sheet arrangements before the crisis (OFF=0). The second set of columns

13

Note that most of our models focus only on one year of data and thus adjusting for market returns is unnecessary.

However, to provide consistency in our tests of the pre-crisis period, we use market-adjusted returns throughout our

analysis.

14 Prior studies examining bank-only samples have commonly measured the crisis period as July or August of 2007

through December 2008 (Fahlenbrach and Stulz 2011; Bertratti and Stulz 2012; Fahlenbrach et al. 2012). However,

other studies (Longstaff 2010; Manconi et al. 2010; Gorton and Metrick 2012; Francis et al. 2013) have noted a

delay in the spreading of the crisis from the subprime market to other markets (e.g., treasury markets, corporate

bond markets, and the stock market). We also conduct our tests using two alternative measures of the financial crisis

returns: October 2007 to March 2009 and July 2007 to March 2009. There are no qualitative differences in

inferences drawn from these alternative measurement windows.

15

presents descriptive statistics for only OFF=1 companies, while the third set of columns presents

descriptive statistics for only OFF=0 companies.

During the crisis, the mean raw return for the full sample is negative 47 percent, while the

mean market-adjusted return for the full sample is negative 8 percent. The mean return on assets

is 3 percent. All three measures are significantly lower for the OFF=1 companies (i.e., those

using off-balance sheet arrangements before the crisis) relative to the OFF=0 companies (i.e.,

those not using off-balance sheet arrangements before the crisis), as indicated in the rightmost

column. Thus, at the univariate level, the existence of OBSA appears to be negatively related to

crisis-period returns.

In the pre-crisis period (i.e., the year before the crisis), the full sample mean raw return,

market-adjusted return, and return on assets were 19 percent, 7 percent, and 7 percent,

respectively. In contrast to the crisis period, the differences between the OFF=1 and OFF=0

subsamples for each of the three company performance measures (raw returns, market-adjusted

returns, or return on assets) are very small and not significant.

4.1.2. Selection and measurement of covariates for the multivariate analysis

We use the prior literature to select a set of control variables (i.e., covariates) for our

multivariate analysis. We include the traditional three Fama-French factors Size, Beta, and Book-

to-market, as well as on-balance sheet Leverage, all of which are included in recent studies of

variation in crisis returns (e.g., Acharya et al. 2010; Fahlenbrach and Stulz 2011; Bertratti and

Stulz 2012; Fahlenbrach et al. 2012; Acharya et al. 2013). We measure Size as the natural

logarithm of market capitalization, Beta as the annualized company-specific beta, and Book-to-

market as the equity book value scaled by equity market value. We measure on-balance sheet

Leverage as long term debt scaled by the market value of assets. All variables are measured as

16

of the end of the year preceding the period in which performance is measured (i.e. as of the end

of fiscal 2006).

We also include control variables capturing the supply and demand for cash, also

measured as of the end of fiscal 2006. These variables include Free cash flows (Zechman 2010)

and Cash needs (e.g., Duchin et al. 20102013). We measure Free cash flows as operating cash

flows less capital expenditures, scaled by average assets, and Cash needs as the sum of five items

leading to demands for cash: the current portion of long-term debt, capital expenditures, research

& development expense, common and preferred dividends, and purchase of common and

preferred stock, all scaled by total assets.

We also include pre-crisis Buy-and-hold returns and Return on assets as measures of pre-

crisis performance (see e.g., Fahlenbrach and Stulz 2011; Beltratti and Stulz 2012; Fahlenbrach

et al. 2012). We measure Buy-and-hold returns as market-adjusted buy-and-hold monthly returns

cumulated across the April 2006 to March 2007 window. We measure Return on assets as fiscal

2006 net income scaled by average total assets. Finally, we include Marketable securities in our

model, to act as a proxy for company direct investment in OBSA-related assets (e.g., asset-

backed securities). This variable is an important control, as it is quite likely that companies

which invested in securities sold by securitization entities experienced a decline in value during

the crisis. We measure Marketable securities as the investment in marketable securities as of the

end of fiscal 2006, scaled by 2006 total assets.

Panel B of Table 3 presents descriptive statistics for fiscal year 2006 for these

covariates.15

The average company in the full sample has a log of market value of 8.20, beta of

15

We make no adjustments to the data except for the book-to-market variable. Specifically, for company-years with

negative values for book equity, the book-to-market ratio is set to zero.

17

1.31, and book-to-market of 0.49. In addition, 17 percent of the average company’s assets are

obligated by long-term borrowings and 3 percent of its assets are invested in marketable

securities. Free cash flows average 15 percent of assets and cash needs average 13 percent of

assets. Several statistically significant differences exist between the OFF=1 and OFF=0

subsamples. In particular, companies using off-balance sheet arrangements just before the crisis

are significantly larger (α < 0.01), significantly higher risk (α < 0.05), have a significantly greater

proportion of on-balance sheet(α < 0.01), and have a significantly smaller level of free cash

flows (α < 0.10).16

4.2 Linear models and results

We first investigate the relation between the use of off-balance sheet arrangements before

the crisis and company performance during the crisis using a linear regression specification. Our

baseline model regresses crisis returns on the use of transfers of financial assets and/or variable

interest entities that are reported off the balance sheet, as well as control variables. The

regression specification is presented in Equation (1) below:

(1)

where Company performance is one of two proxies: Crisis returns, the company’s market-

adjusted buy-and-hold returns cumulated from July 2007 to December 2008, or Crisis return on

assets, a company’s weighted average return on assets for fiscal year 2007 and 2008. is an

indicator variable capturing if the company engages in arrangements involving transfers of

financial assets or variable interest entities that are accounted for off the balance sheet, is a

vector of control variables, and is a normally distributed disturbance. If we find a negative and

16

Note that Panel A of this table presents descriptive statistics for the pre-crisis performance variables Market-

adjusted returns, Buy-and-hold returns, and Return on assets, which are used as are independent variables in crisis-

period analyses, but as dependent variables in pre-crisis analyses.

18

significant coefficient on , we will interpret this finding as evidence that the use of off-balance

sheet arrangements is associated with more negative performance during the financial crisis. The

vector of x variables includes the nine covariates discussed in the previous section: Size, Beta,

Book-to-market, Leverage, Free cash flows, Cash needs, Buy-and-hold returns, Return on assets,

and Marketable securities, all measured as of the end of fiscal 2006. We estimate our regressions

using robust regression (see Leone et al. 2014) to mitigate the effects of influential

observations.17

Leone et al. (2014) argue that robust regression is based on statistical theory, and

is superior to approaches like truncation and/or winsorization, which reflect ad hoc researcher-

specific choices.

Table 4 presents the results for the linear model introduced in Equation (1). Column (1)

estimates the simple regression model without the vector of covariates. Column (2) adds the

Fama and French factors, Size, Beta, and Book-to-market, and Column (3) adds the remaining

covariates.

Panel A presents the results where Crisis returns is the dependent variable. In all three

model specifications, we find a negative and significant coefficient on OFF. Interpreting this

coefficient for the full regression specification in Column (3), a company using off-balance sheet

arrangements before the financial crisis experienced a 6.3 percent lower crisis-period return than

companies not using off-balance sheet arrangements. Larger, higher-risk, higher-growth, and

higher-levered companies also delivered lower stock returns during the crisis (as evidenced by

the significant, negative coefficients on Size, Beta, Book-to-market, and Leverage). These

17

Prior studies using robust regression include Kimbrough (2007), Ortiz-Molina (2007), Bell et al. (2008), Chen et

al. (2008), Choi et al. (2009), Dyreng and Lindsey (2009), and Aboody et al. (2010). We follow the approach used in

a majority of these studies by estimating a maximum likelihood estimation-like regression (Huber 1973), which

focuses on dependent variable outliers.

19

findings are consistent with extant research on the 2007-2009 financial crisis (e.g., Fahlenbrach

et al. 2012).

Panel B of Table 4 presents the results when Crisis return on assets is the dependent

variable. As in the Crisis returns model, the coefficient on OFF is significantly negative in all

three columns of Panel B. That is, is negative and significant, suggesting that companies

involved in off-balance sheet arrangements before the crisis suffered unfavorable accounting

performance during the recent financial crisis. Interpreting the coefficient for the full

regression specification in Column (3), companies using off-balance sheet arrangements before

the financial crisis delivered 1.5 percent lower crisis-period return on assets than companies not

using off-balance sheet arrangements.

From Table 4, we conclude that, on average, companies involved with off-balance sheet

arrangements as of the end of fiscal 2006 performed more poorly during the recent financial

crisis than companies without these arrangements. This finding is consistent for both a market-

based measure of company performance (Crisis returns) and an accounting-based measure of

company performance (Crisis return on assets).

4.3. Propensity-score matching models and results

The analysis in Table 4 assumes a linear relationship between company performance and

use of off-balance sheet arrangements, and assumes that the relations between company

performance and the control variables are identical for companies with and without off-balance

sheet arrangements. This subsection relaxes the condition that these relations between company

performance and use of off-balance sheet arrangements be linear and identical. This alternative

research design approach is especially important in our setting, since the hypothesized relation of

the use of off-balance sheet arrangements is an endogenous choice of a company’s management.

20

Accordingly, our second approach for estimating the relation of company performance

and use of off-balance sheet arrangements employs a propensity-score matching research design.

Armstrong et al. (2010, p. 228) argue that “using propensity scores to generate matched pairs

with maximum variation in the causal variable of interest while minimizing the variation in the

controls is, in many cases, a superior econometric approach to matching on the outcome variable

and relying on a linear or some other assumed functional form to control for confounding

variables.” This approach does not restrict the relation to being linear and identical for

companies with and without off-balance sheet arrangements. Rather, the propensity-score

matching approach will allow us to form matched pairs of company with similar company

characteristics but differ with respect to whether or not they use off-balance sheet arrangements.

We partition our full sample of 353 companies into two subsamples: (i) a “treated”

subsample; that is, companies using off-balance sheet arrangements before the recent financial

crisis (102 companies) and (ii) a “non-treated” subsample; that is, companies not using off-

balance sheet arrangements before the recent financial crisis (251 companies). Then, from the

population of non-treated observations, we estimate a propensity score model to select “control”

observations that best match with the treated observations on multiple dimensions. An

underlying assumption in propensity score matching is that, in the absence of the treatment, the

treated group would have behaved similarly to the control group. The matches are made so as to

ensure “covariate balance.” That is, after the matching process is complete, the distribution of

each treatment group covariate should not significantly differ from the distribution of each

control group covariate.

21

4.3.1 Propensity-score estimation and results

To generate the propensity scores that will be used identify control matches, we first

estimate a logistic regression of OFF on our control variables, which include variables from

prior studies on the determinants of off-balance sheet arrangements. The regression specification

is presented in Equation (2) below:

(2)

where if the company engages in transfers of financial assets or variable interest

entities that are accounted for off the balance sheet, is a vector of control variables, and is a

normally distributed disturbance. The vector of variables includes each of the nine covariates

discussed in the previous section: Size, Beta, Book-to-market, Leverage, Free cash flows, Cash

needs, Buy-and-hold returns, Return on assets, and Marketable securities.

Panel A of Table 5 presents the results from estimating the logistic regression of off-

balance sheet arrangement use. Variables included as explanatory variables in extant research

examining the determinants of off-balance sheet arrangements (e.g., Feng et al. 2009; Zechman

2010) are significant in this model. In particular, our results are consistent with prior studies

which find that companies engaged in the use of off-balance sheet arrangements are larger,

higher risk, and more highly levered than companies not engaged in the use of off-balance sheet

arrangements. Finally, Panel A indicates that our propensity-score model has reasonable

explanatory power, moderating the likelihood of random matching, as the determinants in our

model explain 15.3 percent of the variation in a company management’s choice to use off-

balance sheet arrangements.

For our subsequent analyses, we collected the propensity scores and employed a nearest-

neighbor matching algorithm. In this case, a binary treatment is present (companies either use

22

off-balance sheet arrangements or do not use off-balance sheet arrangements). Thus, a matched

pair was formed by selecting the smallest distance between the treated company using off-

balance sheet arrangements and a non-treated company that is uninvolved with off-balance sheet

arrangements.

4.3.2 Average treatment effects for crisis-period performance

Before turning to the main results of the propensity-score matching, Panel B of Table 5

presents a test of the covariate balance after the matching process is complete. We first refer the

reader back to Table 3, Panel B, which shows that several covariates, including Size, Beta,

Leverage, and Free cash flows are not in balance when we consider all 251 non-treated

observations. Our test of covariate balance for the sample of matched companies shows that all

of our variables in the propensity-score model are balanced (i.e., there are no significant

differences between the treated and control groups at α < 0.05).18

The fact that these significant

differences cease to exist and that there are no covariates out of balance in Table 5, Panel B,

provides evidence of a successful matching process.

Panel C of Table 5 presents our main result: the average treatment effects for the treated

and control groups during the crisis. The average treatment effect between the treated and control

groups for market-adjusted buy-and-hold returns is negative 9.5 percent, which is significant at α

<0.01 significance level. The average treatment effect between the two groups for return on

assets is negative 1.5 percent, which is not significant at any conventional significance level. Our

findings provide strong evidence that companies using off-balance sheet arrangements performed

more poorly during the recent financial crisis.

18

Since we match with replacement, we can use an eligible match more than once. Thus, we test for covariate

balance using the full set of matched values (which includes duplication) and using only the set of unique matched

values. Our results are consistent.

23

4.3.3 Average treatment effects for pre-crisis performance (placebo test)

In order to strengthen the interpretation of our result, we replicate our analysis for a

“placebo” period one year earlier. Specifically, we estimate a propensity score model using 2005

data on OBSA and our control variables, match companies based on the resulting score, and then

examine 2006 company performance for companies with and without off-balance sheet

arrangements. This test helps to rule out alternative explanations for our results in Panel C. For

example, if there are unobservable characteristics that are associated with both the use of off-

balance sheet arrangements and lower company performance, then these variables should be

correlated in the pre-crisis period as well.

We find that the average treatment effect between the treated and control groups for

market-adjusted buy-and-hold returns (return on assets) is 1.4 percent (negative 0.1 percent).

These average treatment effects are not statistically significant for either measure of company

performance measures in the pre-crisis period. Thus, the relation between company performance

and the use of off-balance sheet arrangements does not appear in the pre-crisis period.

4.4 Crisis-period company performance for on- versus off-balance sheet arrangements

While our emphasis to this point has concentrated on companies using off-balance sheet

arrangements, we next turn our attention to companies that also have on-balance sheet

arrangements. A company may have on-balance sheet arrangements either by design or as a

result of failing to meet the criteria to qualify for off-balance sheet treatment. Since companies

with greater exposure to the risks of a special purpose entity are required to use on-balance sheet

treatment, companies with entities reported on the balance sheet are arguably more exposed to

risks from those entities. If the accounting standards in place before the crisis were effective in

allowing companies to use off-balance sheet treatment only for entities with minimal risk, we

24

would expect that an economy-wide shock would, ceteris paribus, lead to poorer performance

for companies with on-balance sheet arrangements than for companies with similar arrangements

that are off company balance sheets. Alternatively, if companies achieving off-balance sheet

treatment were structuring their arrangements to keep them off the balance sheet—whether or

not risk had transferred—these entities may expose the company to as much risk as similar

arrangements reported on company balance sheets (see Niu and Richardson 2006). Thus, we

would not expect to find a difference in the crisis performance of companies with arrangements

that were reported on versus off the company’s balance sheet.

Table 6 presents the average treatment effects for arrangements recognized on and off the

balance sheet as partitioned in accordance with five treatment criteria. In total, 116 of our sample

companies are involved with off-balance sheet arrangements or on-balance sheet arrangements.

This is as compared with 102 companies in our main sample that are involved with off-balance

sheet arrangements, meaning that 14 companies report only on-balance sheet arrangements. We

find a negative 9.7 percent (negative 3.1 percent) average difference in market-adjusted buy-and-

hold returns (average difference in return on assets) for the 59 companies reporting only off-

balance sheet arrangements. Both are statistically significant with a p-value <0.10. In contrast,

we find a negative 1.6 percent (positive 2.9 percent) average difference in market-adjusted buy-

and-hold return (average difference in return on assets) for the 14 companies reporting only on-

balance sheet arrangements.

Taken together, these findings are consistent with the idea that the accounting rules may

not have effectively differentiated the risk exposures and suggests the possibility that investors’

pre-crisis estimates of company value were too high for companies with off-balance sheet

25

arrangements. This finding may also signify that investors do not fully process information that

is disclosed rather than recognized in financial statements.

4.5. Estimating transferred financial assets and variable interest entities separately

Table 7 presents our results for transferred financial assets and variable interest entities

separately. The results are similar for both types of OBSAs. As a result of the crisis, the

accounting for transferred financial assets was radically changed, eliminating the exemption that

protected the off-balance sheet status of many securitization entities (see SFAS No.166).19

This

change was made because of “concerns of financial statement users that many of the financial

assets (and related obligations) that have been derecognized should continue to be reported in the

financial statements of transferors” (FASB 2009). In contrast, while the accounting standards for

variable interest entities were also changed, the changes were less radical in nature. If the

accounting rules for transfers of financial assets were less effective in capturing the economics of

the arrangements before the crisis, then investors may have had more difficulty understanding

the company’s true exposure to these arrangements. Consequently, we would expect companies

with transferred financial assets to perform more poorly during the crisis than companies with

variable interest entities.

We find evidence consistent with our expectation when we consider only off-balance

sheet arrangements. Specifically, we find that the 69 companies with off-balance sheet entities

related to transfers of financial assets experienced 15.2 percent lower (at α < .01) market-

adjusted buy-and-hold returns; for the 58 companies with off-balance sheet variable interest

entities, returns were 7.5 percent lower (and not significantly different from zero). However,

19

Under SFAS No. 140, financial assets transferred via arrangements that conveyed control of the assets to the

special purpose entity were denoted “qualifying special purpose entities” and were exempted from the variable

interest entity rules of FIN 46(R).

26

when we consider on- and off-balance sheet arrangements, jointly or separately, we do not find

this result. In fact, when we consider companies with on- and off-balance sheet arrangements,

our findings are reversed: the 26 companies with off-balance sheet variable interest entities

produced market-adjusted buy-and-hold returns 21.4 percent lower than their matched

companies while the 15 companies with off-balance sheet entities related to transfers of financial

assets experienced no difference. Thus we cannot conclude that the accounting for transfers of

financial assets played a larger role in the crisis than the accounting for variable interest entities.

5. Conclusion

Numerous capital market participants, to include academics, practitioners, standard

setters, the financial press, and even Congress, have pointed to the prevalence of off-balance

sheet financing as (at least partially) accountable for exacerbating the financial crisis. In

particular, this criticism has been targeted at arrangements in which unconsolidated entities are

involved. In this study, we empirically examine the relation between the use of off-balance sheet

arrangements before the crisis and companies’ stock market and accounting performance during

the crisis. Our results reveal that the use of two commonly-vilified types of arrangements that are

often held off company balance sheets—financial assets transferred to unconsolidated entities

and variable interests in unconsolidated entities—is negatively and significantly associated with

company performance measures during the financial crisis. We do not find this negative

association for the pre-crisis period. In addition, we find no relation for companies that use only

on-balance sheet accounting. Our findings suggest that companies using off-balance sheet

arrangements were exposed to more risk than companies not using off-balance sheet

arrangements.

27

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31

APPENDIX A

Variable definitions

Variables used in Company Performance Models

Crisis returns Market-adjusted buy-and-hold monthly returns (CRSP data item RET less CRSP data item

VWRETD) from the CRSP Monthly Stock File cumulated across July 2007 to December

2008

Crisis return on assets The weighted average of Return on assets (as defined below) for fiscal years 2007 and

2008

OFF Use of arrangements off the balance sheet; set equal to 1 if a company reports transfers of

financial assets to unconsolidated entities or variable interests in unconsolidated entities in

the Off Balance Sheet Arrangements section of MD&A or in the footnotes the financial

statements; 0 otherwise

ON Use of arrangements on the balance sheet; set equal to 1 if a company reports transferred

financial assets as secured borrowings or variable interests that are consolidated in the Off

Balance Sheet Arrangements section of MD&A or in the footnotes the financial

statements; 0 otherwise

Size Natural logarithm of market capitalization [ln(PRCC_F * CSHO)]

Beta Annualized company-specific beta (CRSP data item BETAV)

Book-to-market Equity book value scaled by equity market value [(SEQ + MIB) / (PRCC_F * CSHO)]

Leverage Long term debt scaled by the market value of assets [(DLC + DLTT) / (AT – SEQ – MIB +

(PRCC_F * CSHO))]

Free cash flows Operating cash flows less capital expenditures scaled by average total assets (OANCF –

CAPX) / Average AT

Cash needs Sum of current portion of long-term debt, capital expenditures, research & development

expense, common and preferred dividends, and purchase of common and preferred stock,

all scaled by total assets [(DD1 + CAPX + XRD + DVP + DVC + PRSTCK) / AT]

Return on assets Net income scaled by average total assets (NI / Average AT)

Buy-and-hold returns Market-adjusted buy-and-hold monthly returns (CRSP data item RET less CRSP data item

VWRETD) from the CRSP Monthly Stock File cumulated across the April 2006 to March

2007 window

Marketable securities Investment in marketable securities scaled by total assets (IVST / AT)

32

TABLE 1

Industry composition and company characteristics for the OBSA sample and a

Compustat comparison sample

This table presents fiscal year 2006 summary statistics for the OBSA sample and a sample drawn from the Compustat

population. The Compustat comparison sample is constructed by including all companies reporting greater than $1

million in total assets, as well as non-null net income and stock prices. For both samples, Panel A reports the number of

observations in the full sample and per each industry grouping. Panel B reports the median values for several company

characteristics for the full sample. In Panel B, Assets is Compustat data item AT and Market capitalization is the product

of Compustat data items PRCC_F and CSHO. Both are stated in $billions. Refer to Appendix A for the variable

definitions of Leverage, Return on assets, and Book-to-market.

PANEL A: Industry composition OBSA sample Compustat sample

SIC codes N % N %

Mining & Construction 1000-1999 30 8.5% 807 10.8%

Manufacturing 2000-2999 90 25.5% 1090 14.6%

Manufacturing 3000-3999 103 29.2% 1617 21.7%

Transportation and Utilities 4000-4999 43 12.2% 666 8.9%

Wholesale and Retail 5000-5999 15 4.2% 543 7.3%

Finance, Insurance, and Real Estate 6000-6999 37 10.5% 1537 20.6%

Services 7000-8999 29 8.2% 1102 14.8%

Other 0-999, 9000-9999 6 1.7% 99 1.3%

Full sample 353 100.0% 7461 100.0%

PANEL B: Company characteristics OBSA sample median Compustat sample median

Assets 3.9 0.3

Market capitalization 3.3 0.3

Leverage 0.14 0.09

Return on assets 0.05 0.02

Book to market 0.44 0.44

33

TABLE 2

Use of off-balance sheet arrangements

This table presents the extent of companies’ involvement in off-balance sheet arrangements for 2003-2006, the immediate four years preceding the recent

financial crisis. Panel A presents a count of the number of companies with and without arrangements off the balance sheet involving transferred financial assets

and variable interest entities. Panel B presents a count of the number of companies with arrangements off the balance sheet appearing in the off-balance sheet

arrangements section of MD&A (OBSA), the financial statement footnotes (FNT), and in both OBSA and the FNT. Panel C presents a count of the number of

companies with and without off-balance sheet arrangements partitioned by industry.

PANEL A: Full sample 2006 2005 2004 2003

Number of companies with OFF=1 102 99 106 98

Number of companies without OFF=0 251 254 247 255

PANEL B: Data source 2006 2005 2004 2003

Number of companies disclosing arrangements in the

OBSA section 84 81 85 80

Number of companies disclosing arrangements in the

financial statement footnotes 86 81 89 76

Number of companies disclosing arrangements in both the OBSA

section and the financial statement footnotes 68 63 68 58

PANEL C: By industry 2006 2005 2004 2003

SIC codes OFF=1 OFF=0 OFF=1 OFF=0 OFF=1 OFF=0 OFF=1 OFF=0

Mining & Construction 1000-1999 12 18 9 21 10 20 9 21

Manufacturing 2000-2999 18 72 18 72 21 69 19 71

Manufacturing 3000-3999 30 73 32 71 33 70 30 73

Transportation and Utilities 4000-4999 17 26 17 26 17 26 12 31

Wholesale and Retail 5000-5999 4 11 3 12 4 11 5 10

Finance, Insurance, and Real Estate 6000-6999 13 24 12 25 12 25 14 23

Services 7000-8999 6 23 6 23 7 22 7 22

Other 0-999, 9000-9999 2 4 2 4 2 4 2 4

Full sample 102 251 99 254 106 247 98 255

34

TABLE 3

Descriptive statistics for dependent and independent variables

This table reports descriptive statistics for the full sample and separately companies using off-balance sheet arrangements (OFF=1) and companies not using off-

balance sheet arrangements (OFF=0). Panel A presents descriptive statistics for the company performance measures (raw buy-and-hold returns, market-adjusted

buy-and-hold returns, and return on assets). Panel B presents descriptive statistics for the covariates used in Tables 4 through 7. The rightmost column in the

table, labeled Diff, is a t-test for difference in means between the between the OFF=1 and OFF=0 groupings. Statistical significance with probability <10%, <5%,

and <1% (two-tailed) is indicated by *, **, and ***, respectively. Refer to Appendix A for variable definitions.

PANEL A: Company performance measures Full sample OFF=1 OFF=0

N Mean Med Stdev N Mean Med Stdev N Mean Med Stdev Diff

Financial crisis performance Raw returns (July 2007 –

December 2008) 353 –0.47 –0.49 0.31 102 –0.55 –0.56 0.27 251 –0.44 –0.45 0.32 ***

Market-adjusted returns (July

2007 – December 2008) 353 –0.08 –0.10 0.31 102 –0.16 –0.17 0.27 251 –0.05 –0.06 0.32 ***

Return on assets (average of

2007 and 2008) 352 0.03 0.04 0.09 102 0.02 0.03 0.09 250 0.04 0.05 0.09 **

Pre-crisis performance

Raw returns (April 2006 – March

2007) 353 0.19 0.14 0.33 102 0.18 0.15 0.30 251 0.19 0.14 0.35 –

Market-adjusted returns (April

2006 – March 2007) 353 0.07 0.02 0.33 102 0.06 0.03 0.30 251 0.07 0.02 0.35 –

Return on assets (fiscal 2006) 353 0.07 0.05 0.18 102 0.06 0.05 0.12 251 0.08 0.06 0.20 –

PANEL B: Covariates Full sample OFF=1 OFF=0

N Mean Med Stdev N Mean Med Stdev N Mean Med Stdev Diff

Size 353 8.20 8.12 1.77 102 8.91 8.91 1.63 251 7.91 7.93 1.74 ***

Beta 353 1.31 1.22 0.69 102 1.45 1.45 0.63 251 1.26 1.12 0.71 **

Book to market 353 0.49 0.44 0.32 102 0.48 0.47 0.25 251 0.49 0.42 0.35 –

Leverage 353 0.17 0.14 0.14 102 0.22 0.17 0.16 251 0.16 0.13 0.13 ***

Free cash flows 353 0.15 0.13 0.11 102 0.13 0.12 0.10 251 0.15 0.13 0.11 *

Cash needs 353 0.13 0.11 0.10 102 0.12 0.12 0.08 251 0.14 0.11 0.11 –

Marketable securities 353 0.03 0.00 0.06 102 0.02 0.00 0.05 251 0.03 0.00 0.06 –

35

TABLE 4

Linear models of company performance measures and off-balance sheet arrangements

This table reports the summary statistics from a linear regression of market-adjusted buy-and-hold returns from July

2007 to December 2008 on the existence of off-balance sheet arrangements and a vector of covariates (i.e., control

variables). All right-hand side variables are measured as of the end of fiscal year 2006. Refer to Appendix A for

variable definitions. The linear model is estimated using robust least squares, utilizing M-estimation, Bisquare

weighting, and Huber Type I standard errors. The Rw-squared statistic is a measure of goodness-of-fit, which

Renaud and Victoria-Feser (2010) note is a more appropriate statistic for robust regression models than conventional

Adjusted R-squared statistics. P-values are in parentheses directly below the coefficient estimates. Statistical

significance with probability <10%, <5%, and <1% (two-tailed) is indicated by *, **, and ***, respectively.

PANEL A: Crisis period market-adjusted buy-and-hold returns

(1) (2) (3)

Constant –0.073

(<0.001)***

–0.157

(0.111)

–0.030

(0.766)

OFF –0.095

(0.007)***

–0.105

(0.003)***

–0.063

(0.064)*

Size

0.030

(0.002)***

0.023

(0.012)**

Beta

–0.065

(0.004)***

–0.083

(<0.001)***

Book-to-market

–0.169

(0.001)***

–0.172

(<0.001)***

Leverage

–0.458

(<0.001)***

Free cash flows

0.384

(0.022)

Cash needs

–0.174

(0.331)

Buy-and-hold returns

0.030

(0.495)

Return on assets

–0.034

(0.675)

Marketable securities

–0.552

(0.021)

Number of observations 353 353 353

Rw-squared 2.6% 13.8% 25.5%

36

PANEL B: Crisis period return on assets

(1) (2) (3)

Constant 0.048

(0.000)***

–0.014

(0.529)

0.015

(0.393)

OFF –0.020

(0.022)**

–0.037

(0.000)***

–0.015

(0.001)***

Size

0.011

(0.000)***

0.005

(<0.001)***

Beta

0.016

(0.002)***

0.009

(<0.001)***

Book-to-market

–0.086

(0.000)***

–0.057

(<0.001)***

Leverage

–0.184

(<0.001)***

Free cash flows

0.118

(<0.001)***

Cash needs

0.151

(<0.001)***

Buy-and-hold returns

0.006

(0.681)

Return on assets

0.039

(<0.001)***

Marketable securities

–0.109

(<0.001)***

Number of observations 352 352 352

Rw-squared 2.0% 36.3% 64.7%

37

TABLE 5

Propensity-score matching models of company performance measures and off-balance

sheet arrangements

This table reports the results from our model of propensity-score matching. Panel A presents results from the

propensity-score model, which estimates a logistic regression of the existence of off-balance sheet arrangements on

variables expected to explain a company’s involvement in off-balance sheet arrangements. Panel B presents tests of

covariate balance between matched pairs. The treated group (i.e., OFF=1), which is involved with off-balance sheet

arrangements as of the end of fiscal year 2006, is compared with the control group (i.e., OFF=0), which is not

involved with off-balance sheet arrangements as of the end of fiscal year 2006. The t-test between OFF=1 and

OFF=0 is a parametric test of the difference in means. Panel C (Panel D) presents the average treatment effects of

the treated observations during the crisis (pre-crisis). Treated observations are paired with control observations

using a nearest neighbor propensity-score matching algorithm. Dependent and independent variables are measured

as of the end of fiscal year 2006. Refer to Appendix A for variable definitions. Statistical significance with

probability <10%, <5%, and <1% (two-tailed) is indicated by *, **, and ***, respectively.

PANEL A: Propensity-score model

Constant –7.191

(<0.001)***

Size 0.573

(<0.001)***

Beta 0.727

(<0.001)***

Book-to-market 0.398

(0.410)

Leverage 4.613

(<0.001)***

Free cash flows –1.622

(0.320)

Cash needs –1.801

(0.337)

Buy-and-hold returns –0.338

(0.413)

Return on assets –0.533

(0.651)

Marketable securities –2.558

(0.369)

Number of observations 353

Pseudo R-squared 15.3%

38

PANEL B: Tests of covariate balance for full sample

OFF = 1

OFF = 0

(Matched – with

Duplicates)

OFF = 0

(Matched – no

Duplicates)

Size 8.908 8.868 8.481

Beta 1.450 1.341 1.415

Book-to-market 0.478 0.478 0.452

Leverage 0.216 0.211 0.198

Free cash flows 0.132 0.115 0.127

Cash needs 0.124 0.116 0.128

Buy-and-hold returns

Return on assets 0.063 0.072 0.077

Marketable securities 0.022 0.010* 0.040

Number of observations 102 102 70

PANEL C: Average treatment effects for crisis-period performance

Market-adjusted

buy-and-hold returns Return on assets

Average for companies with OBSAs (i.e., OFF=1) –0.160 0.017

Average for matched companies without OBSAs (i.e., OFF=0) –0.065 0.032

Difference (average treatment effect) –0.095** –0.015

PANEL D: Average treatment effects for pre-crisis performance

Market-adjusted

buy-and-hold returns Return on assets

Average for companies with OBSAs (i.e., OFF=1) 0.041 0.044

Average for matched companies without OBSAs (i.e., OFF=0) 0.027 0.052

Difference (average treatment effect) 0.014 –0.008

39

TABLE 6

Crisis-period performance and on- versus off-balance sheet arrangements

This table reports matched estimates of average treatment effects for arrangements recognized on and off the

balance sheet as partitioned in accordance with five treatment criteria. Treated observations are paired with control

observations using a nearest neighbor propensity-score matching algorithm. Note that differences between the

average treatment effect of the treated and the average treatment effect of the control matches are tabulated below.

Statistical significance with probability <10%, <5%, and <1% (two-tailed) is indicated by *, **, and ***,

respectively.

Treatment criteria N

Difference in market-adjusted

buy-and-hold returns

Difference in

ROAs

Company reports off-balance sheet

arrangements (OFF =1) 102 –0.156*** –0.025**

Company reports off-balance sheet

arrangements or on-balance sheet

arrangements (OFF=1 or ON=1) 116 –0.116*** –0.022**

Company reports off-balance sheet

arrangements and on-balance sheet

arrangements (OFF=1 and ON=1) 43 –0.207*** –0.024

Company reports off-balance sheet

arrangements but no on-balance sheet

arrangements OFF=1 and ON=0 59 –0.097* –0.031*

Company reports on-balance sheet

arrangements but no off-balance sheet

arrangements ON=1 and OFF=0 14 –0.016 0.029

40

TABLE 7

Separate analyses for transferred financial assets and variable interest entities

This table presents two separate analyses: one for transferred financial assets only and one for variable interest

entities only. Panel A presents matched estimates of average treatment effects for transferred financial asset

arrangements recognized on and off the balance sheet as partitioned in accordance with five treatment criteria. Panel

B presents matched estimates of average treatment effects for variable interest entity arrangements recognized on

and off the balance sheet as partitioned in accordance with five treatment criteria. Treated observations are paired

with control observations using a nearest neighbor propensity-score matching algorithm. Note that differences

between the average treatment effect of the treated and the average treatment effect of the control matches are

tabulated below. Statistical significance with probability <10%, <5%, and <1% (two-tailed) is indicated by *, **,

and ***, respectively.

PANEL A: Crisis-period performance and transferred financial assets

Treatment criteria N

Difference in market-adjusted

buy-and-hold returns

Difference in

ROAs

Company reports off-balance sheet

arrangements (OFF =1) 69 –0.152*** –0.026

Company reports off-balance sheet

arrangements or on-balance sheet

arrangements (OFF=1 or ON=1) 78 –0.108** –0.026*

Company reports off-balance sheet

arrangements and on-balance sheet

arrangements (OFF=1 and ON=1) 15 –0.001 –0.027

Company reports off-balance sheet

arrangements but no on-balance sheet

arrangements OFF=1 and ON=0 54 –0.063 –0.0015

Company reports on-balance sheet

arrangements but no off-balance sheet

arrangements ON=1 and OFF=0 9 –0.079 0.072*

PANEL B: Crisis-period performance and variable interest entities

Treatment criteria N

Difference in market-adjusted

buy-and-hold returns

Difference in

ROAs

Company reports off-balance sheet

arrangements (OFF =1) 58 –0.075 –0.001

Company reports off-balance sheet

arrangements or on-balance sheet

arrangements (OFF=1 or ON=1) 72 –0.148*** –0.42***

Company reports off-balance sheet

arrangements and on-balance sheet

arrangements (OFF=1 and ON=1) 26 –0.214*** –0.002

Company reports off-balance sheet

arrangements but no on-balance sheet

arrangements OFF=1 and ON=0 32 –0.033 –0.021

Company reports on-balance sheet

arrangements but no off-balance sheet

arrangements ON=1 and OFF=0 14 –0.137 –0.064**