2 tax motivated loss shifting

THE ACCOUNTING REVIEW American Accounting AssociationVol. 88, No. 5 DOI: 10.2308/accr-504962013pp. 1657–1682

Tax-Motivated Loss Shifting

Merle M. Erickson

The University of Chicago

Shane M. Heitzman

University of Rochester

X. Frank Zhang

Yale University

ABSTRACT: This paper examines the implications of tax loss carryback incentives for

corporate reporting decisions and capital market behavior. During the 1981 through 2010

sample period, we find that firms increase losses in order to claim a cash refund of recent

tax payments before the option to do so expires, and we estimate that firms with tax

refund-based incentives accelerate about $64.7 billion in losses. Tax-motivated loss

shifting is reflected in both recurring and nonrecurring items and is more evident for

financially constrained firms. Analysts do not generally incorporate tax-motivated loss

shifting into their earnings forecasts, resulting in more negative analyst forecast errors

for firms with tax-based incentives than for firms without. Holding earnings surprises

constant, however, investors react less negatively to losses reported by firms with tax

loss carryback incentives.

Keywords: taxes; net operating losses; liquidity; analyst forecasts; capital markets.

Data Availability: Data are available from sources identified in the paper.

I. INTRODUCTION

Tax rules give the firm an option to obtain a cash refund of recently paid taxes by reporting a

tax loss. This cash infusion can be particularly valuable for financially constrained firms. In

fact, the purported liquidity benefits of these tax loss ‘‘carrybacks’’ played a central role in

at least two attempts to stimulate the business sector during the past decade, and refunds of

We appreciate comments from John Harry Evans III (senior editor), two anonymous referees, and workshop participantsat the National University of Singapore, Singapore Management University, Yale University, and the Iowa Tax ReadingsGroup. Professor Erickson appreciates the financial support of the Booth School of Business.

Editor’s note: Accepted by John Harry Evans III.

Submitted: January 2012Accepted: April 2013

Published Online: April 2013

1657

corporate tax payments by the federal government exceeded $95 billion in both 2009 and 2010.1 In

this paper, we address the accounting and economic consequences of allowing firms to carry back

tax losses. Specifically, we investigate whether firms increase their reported tax losses to obtain

cash refunds of prior tax payments, whether financial analysts anticipate the effect of these tax

incentives on reported earnings, and how investors respond to the earnings news for firms with

tax-based incentives to report losses.

U.S. tax law limits corporate tax refunds to the taxes paid in the most recent (currently two) years.

Thus, for taxes paid on profits in year t�2, year t is generally the last year the firm can claim a refund of

those taxes. To reduce the firm’s expected tax liability, and therefore increase the firm’s after-tax cash

flow, the manager will often have an incentive to report tax losses in year t to maximize the cash refund

of taxes paid on income in t�2. When the firm exhausts its capacity to carry a loss back to get a refund,

or delays recognizing a tax loss altogether, liquidity benefits disappear and the economic value of the

tax loss is discounted because of uncertainty and the time value of money.2

Our study complements and extends other research that focuses on corporate and market

responses to the Tax Reform Act of 1986 (TRA 86). The significant decline in corporate marginal

tax rates resulting from TRA 86 created strong incentives for managers to accelerate losses (Scholes

et al. 1992; Guenther 1994; Maydew 1997) so that the losses would apply under the higher tax rates

and thus reduce current taxes by a larger amount. Tax rate shifts of that magnitude are infrequent, so

evidence on the influence of loss-shifting incentives when statutory tax rates are relatively constant,

as in the last 25 years in the U.S., is important. A firm experiencing a negative economic shock will

often be in a position to decide between accelerating the recognition of a loss for a certain and

immediate cash refund or deferring the recognition of the loss for an uncertain and discounted tax

benefit. These negative shocks can be idiosyncratic or systematic, as in times of recession, and thus

tax-based incentives for accelerating tax losses represent a pervasive issue for managers, policy

makers, and capital market participants.

Each year we identify a set of firms in a position to recognize a tax loss in the current period

that would provide a cash refund of prior tax payments that otherwise would be lost after year-end.

These are firms that (1) paid income taxes on profits in the earliest carryback year that have not yet

been refunded, and (2) are expected to report a loss in the current year. The first condition ensures

that there is actually a cash refund available. The second condition narrows our focus to those firms

with expected benefits from tax-motivated loss shifting.

We find that firms with tax-motivated loss-shifting incentives report significantly larger losses

than a comparison sample of firms that are also expected to report a loss but do not have access to a

potential cash tax refund. The loss equates to 11.25 percent lower reported earnings for the average

loss firm with carryback incentives versus comparable firms and implies that incentives for

tax-motivated loss shifting play an important role in reported earnings. These accelerated losses

1 A comment letter from Financial Executives International to congressional leaders dated January 13, 2009stated, ‘‘Extending the carryback period to five years will enhance liquidity of businesses with current losses,while helping to insulate against future losses. This provides companies with more capital to make investmentsthat will help move the economy forward.’’ In a speech on the floor of the Senate on November 3, 2009, SenatorPatty Murray of Washington State argued that the Worker, Homeownership, and Business Assistance Act of2009 ‘‘would also provide a critical boost to businesses . . . by extending their ability to carry back losses they’vesuffered in 2008 or 2009. This tax provision will provide badly needed capital to help companies avoid layoffs,expand their operations, and create jobs.’’ See also Leone (2010).

2 The uncertainty in the tax loss carryforward is driven by uncertainty about future taxable income and thepossibility that a future ownership change will put a binding constraint on the firm’s ability to use thesecarryforwards. With respect to the latter, a number of firms have adopted so-called ‘‘NOL poison pills’’ designedto prevent the constraint triggered by Section 382 of the Internal Revenue Code, which limits tax loss usagefollowing a change in ownership (Erickson and Heitzman 2010). Section 382 concerns increase the incentive toaccelerate tax losses.

1658 Erickson, Heitzman, and Zhang

The Accounting ReviewSeptember 2013

reduce operating income and increase one-time losses. In addition, we find that financially

constrained firms more aggressively accelerate losses to obtain cash refunds. This result supports

the idea that these firms view tax refunds as an important source of cash and implies that liquidity

benefits outweigh the incremental direct and indirect costs of accelerating the tax loss.

We then investigate whether and how financial analysts account for these incentives when

forecasting earnings. A large body of literature argues that analysts are sophisticated and that their

forecasts are a reasonable proxy for market expectations.3 However, studies based on financial

reporting and tax law ‘‘events’’ suggest that analysts often do not fully utilize the information in tax

footnote disclosures (Amir and Sougiannis 1999) and have difficulty incorporating the predictable

effects of tax law changes (Chen and Schoderbek 2000; Plumlee 2003; Shane and Stock 2006). We

expand upon these studies by not constraining our analysis to one-time events. Although managers

respond to tax-loss reporting incentives in predictable ways, we find that analysts do not incorporate

these incentives into their earnings forecasts.4 Analysts’ forecasts are significantly less precise for firms

with tax incentives to shift losses than for comparable loss firms without these tax incentives. These

results are not driven by systematic differences in the measurement of forecasted and actual earnings.

Finally, we evaluate how investors react to the earnings of firms with incentives to accelerate

tax losses. Although these firms tend to have negative earnings surprises based on past earnings and

analyst forecasts, the market’s reaction to the news is less negative for those firms with incentives to

accelerate tax losses. This result implies that investors recognize the value inherent in tax-motivated

loss shifting. The results suggest that tax-motivated losses represent positive decisions by managers

to increase firm value in the presence of potential contracting and financial reporting consequences.

To put the main results of our paper in context, consider homebuilder Lennar Corporation.5 In

fiscal years 2005 and 2006, current tax expense figures indicate that Lennar incurred federal and

state income tax liabilities of $805 million and $547 million, respectively. Thus, 2007 was the last

year Lennar could claim a refund for the $805 million of taxes paid in 2005. Faced with a continued

decline in the homebuilding sector, Lennar took several actions to generate tax losses at the end of

2007, which was two years before the temporary extension of the loss carryback window. For

example, in the final month of the 2007 fiscal year, Lennar sold land to a partnership they formed

with Morgan Stanley, generating an $800 million tax loss for Lennar (Lennar 10-K). Lennar also

wrote off $530 million in deposits and pre-acquisition costs for building lots the company decided

not to pursue. According to the CEO of Lennar, ‘‘As a by-product of our strategic fourth quarter

3 See Brown et al. (1986), O’Brien (1987), and Doyle et al. (2006). During the earnings season, Wall Streettypically compares a firm’s reported earnings with analysts’ most recent forecasts to assess earnings surprises.Prior research concludes that similar patterns between analyst forecast errors and stock returns also support thisassumption. For example, Sloan (1996) shows that high-accrual firms have lower future stock returns, whereasBradshaw et al. (2001) find that high-accrual firms have overly optimistic earnings forecasts. Zhang (2006a)shows that investors underreact more to new information in cases of greater information uncertainty, whereasZhang (2006b) finds a similar pattern for analysts.

4 The results are consistent with the idea that analysts either do not have the ability to incorporate the tax-motivated loss shifting or that analysts understand such corporate reporting behavior but choose not toincorporate it in their earnings forecasts. Our evidence is more consistent with the first view. In Table 4, we findanalyst forecast errors are more negative for firms with carryback incentives. In the ‘‘Analyst Revisions of FutureEarnings’’ section, we find that analysts gradually incorporate the effect of NOL-related loss shifting in theirforecasts of current year’s earnings while keeping next year’s forecasts relatively unchanged.

5 While Lennar’s numbers may be unusually large, such strategic choices and corporate decisions are likely toapply to other companies, a phenomenon that underpins the key point of the paper. For example, one weekfollowing the passage of the five-year carryback window on November 6, 2009, William Lyon Homes revealedin the company’s 10-Q filing that, ‘‘In considering ways to maximize such tax refund, the Company isdetermining whether to elect to defer certain cancellation of indebtedness income generated from its repurchaseof Senior Notes during 2009.’’ The company subsequently recorded losses through the sale of assets, and by theend of fiscal year 2009 expected a federal income tax refund of $101.8 million. This would nearly double thefirm’s cash balance. See also Leone (2010).

Tax-Motivated Loss Shifting 1659


moves, we have generated losses that have resulted in the receipt of a cash tax refund of $852

million subsequent to the close of the quarter’’ (press release, January 24, 2008).6 The week before

earnings were released, analysts forecasted losses ranging from $0.00 to $4.45 per share, with an

average expected loss of $1.84 per share. Lennar surprised analysts by reporting a loss of nearly

$7.92 per share. With 159.9 million shares outstanding, this translates to an unexpected accounting

loss of nearly $972 million. Nevertheless, the market reacted positively to the news, with Lennar’s

three-day stock return around the earnings announcement date exceeding 25 percent, reflecting a

$493 million increase in market value.

Our study’s first contribution is to provide evidence that firms accelerate tax losses to obtain

cash inflows through refunds of prior tax payments, even when statutory tax rates are constant. We

show that this tax-motivated loss shifting is reflected in both recurring and nonrecurring items and

is more pronounced for financially constrained firms. To our knowledge, this paper is one of the

first to link liquidity demands to tax and financial reporting decisions. Second, we provide a broader

examination of the capital market implications of tax loss carryback incentives. We find that

analysts do not incorporate tax loss carryback incentives into their earnings forecasts, yet

stockholders react less negatively to reported losses when firms have tax incentives to accelerate

losses. Taken together, our evidence provides new insight on how managers and capital market

participants incorporate tax-based incentives to accelerate losses into their decision-making. Our

evidence is also relevant to understanding the growing importance of tax losses on firm value, as tax

losses are becoming increasingly important for fiscal and corporate policy decisions (Graham and

Kim 2009; Erickson and Heitzman 2010). Overall, our evidence suggests that the tax-based

incentive to accelerate reported losses plays a material and persistent role in corporate reporting

decisions and capital market activities.

Section II next reviews prior literature and develops hypotheses. Section III describes the data

and provides summary statistics. Section IV presents the results. Section V provides a variety of

additional analyses, and Section VI concludes.

II. PRIOR LITERATURE AND HYPOTHESIS DEVELOPMENT

Managers have incentives to reduce the firm’s total tax liability over the life of the firm because

a dollar less paid to the tax authority is a dollar more for shareholders. Moreover, firms face

asymmetric tax treatment of profits and losses because taxes are paid immediately on profits, but

taxes are not necessarily refunded on losses. This means that a firm with zero expected pretax

income will still have a positive expected tax liability that is increasing in income uncertainty

(Scholes et al. 2008, 172). Thus, Graham and Smith (1999) show that firms facing such a convex

tax schedule have incentives to hedge in order to reduce expected tax liabilities.

Net operating loss provisions contained in the tax code partially mitigate this asymmetry. To

illustrate, a firm that paid taxes in the recent past can claim a refund of those taxes in a year in which

it reports a loss. This is achieved through tax loss carryback rules that allow the firm to use its tax

loss in the current year to reduce taxable income in a prior tax year (i.e., a carryback of the current

tax loss for a refund of prior tax payments), starting with the earliest tax year of the carryback

period. Put differently, when the firm pays taxes on income, it effectively gets an option to claim a

refund of those taxes that expires after the length of the carryback window, T. This option gives

some firms an incentive to accelerate their losses to generate cash flow through a refund of prior tax

6 These tax losses were also reflected in Lennar’s financial statements, albeit in a somewhat different form. Thetransaction was treated as a sale for tax purposes, but not for GAAP purposes, because Lennar retained 50percent of the voting rights in the partnership. In Lennar’s financial statements, the decline in the value of theseassets was recorded, but as an asset impairment rather than as a loss on sale.



payments because otherwise the option will expire. For example, assume the loss carryback period

(T) is two years. If a firm paid taxes on profits in year t�2 and those taxes have not been refunded

through losses in year t�1, then the firm will have an incentive to accelerate losses into year t in

order to maximize a refund of taxes paid in year t�2. If the firm did not report taxable income in a

recent year, or there is no tax to be refunded, then it can carry the current year’s tax loss forward and

use it to reduce taxable income in a future year. For example, a startup firm with accumulated losses

cannot claim a refund of taxes they have not paid. Current tax losses are carried forward to reduce

taxable income if and when the firm becomes profitable. If the firm does not generate enough future

taxable income to use the loss before the carryforward period expires, then the loss expires unused.

An accelerated tax loss reduces current year taxes, and if the loss is large enough, leads to a

cash refund. But for the profitable firm this strategy will only increase the tax liability in the

following period. Because consistently profitable firms face more symmetric tax treatment, they

derive fewer benefits from accelerating losses (Graham and Smith 1999). Thus, the present value of

the cash tax benefit from accelerating a loss is strongest if the firm is already in a tax loss position

(that is, before considering the tax incentives to report additional losses) and does not expect to

immediately return to profitability. These firms obtain immediate and certain tax benefits by

accelerating the recognition of the loss to recoup prior tax payments and face uncertain and

discounted tax benefits if they do not. The benefit of accelerating those losses to generate cash flows

is therefore stronger when the firm expects losses in future periods as well.

Shifting a tax loss to generate a refund requires the manager to alter real decisions, reporting

decisions, or both. Since the incremental benefit of the refund must be weighed against the

incremental cost to shareholders, the expected tax benefits could go unclaimed if accelerating a loss:

(1) involves costly real actions—such as disposing of productive assets or deferring sales, (2)

generates financial reporting costs—such as violating a debt covenant, or (3) increases the taxing

authority’s scrutiny of the firm’s tax positions.7 Thus, whether the incentive to accelerate a tax loss

has a material effect on corporate reporting and capital market activity is an open question.

The incentives for tax-motivated loss shifting increase when the marginal tax rate during the

carryback window exceeds the expected marginal tax rate during current and future periods. Prior

research examines firms’ reporting behavior around TRA 86, which reduced the top statutory corporate

tax rate from 46 percent in 1986 to 34 percent in 1988 (Scholes et al. 1992; Guenther 1994; Maydew

1997; Shane and Stock 2006). Among all firms that report tax losses during a ten-year window,

Maydew (1997) finds that firms appear to report larger losses when the relative tax benefit of the

carryback, measured as difference between tax rates in the current and carryback years, is greater. He

finds that loss-shifting actions are evident in both operating income and nonrecurring losses.

We extend this research to a general setting in which statutory tax rates are effectively constant.

Maydew (1997) suggests that firms facing losses always have incentives to increase their refund of

prior years’ taxes for at least two reasons. First, the cash flows from tax refunds are certain, whereas

expected cash flows from operations are not. Second, the time value of money provides an incentive

to defer income and accelerate deductions. The ability to claim a certain refund of cash taxes paid is

permanently lost when the carryback window closes, substantially reducing the present value of the

tax loss.8 We extend these points by emphasizing that a tax refund represents real cash inflows that

7 There is an extensive literature that analyzes how managers consider the tradeoff between taxes (benefits andcosts) and GAAP accounting effects. See for example Matsunaga et al. (1992), Engel et al. (1999), Shackelfordet al. (2010), and Hanlon and Heitzman (2010).

8 Consider the change in the NOL carryback period in 2008–2010. The extension of the carryback window fromtwo to five years was motivated by a desire to provide a refund of prior cash taxes paid to firms in a difficulteconomic climate (Graham and Kim 2009). Such tax refunds were unavailable to firms prior to the law changesbecause the carryback period prevented firms from claiming refunds of taxes paid in years outside the then-current two-year carryback window.



provide liquidity. The liquidity motivation is likely to be more relevant for loss firms facing

difficulty raising external capital. In essence, even in periods of constant statutory tax rates, firms

will have incentives to accelerate losses.9 This leads to the following prediction:

H1: Reported earnings are decreasing in tax loss carryback incentives.

In testing this hypothesis, we explicitly consider variation in the costs and benefits to firms that

execute this strategy. For firms that are persistently profitable and consistently pay taxes, the

benefits of accelerating a loss to claim a tax refund are either unavailable or too small to make a

difference. Thus, more powerful tests of the hypothesized behavior require comparing firms with

tax-motivated loss-shifting incentives, which we operationalize as firms that paid taxes during the

earliest year of the carryback window and expect to have losses in the current year, to firms without

such incentives, which are then firms with similar expected losses but no tax payments to recoup.

Our tests assume that the loss reported to the tax authority is also reflected in GAAP earnings, so the

incremental tax benefits from increasing a tax loss should be weighed against the incremental costs

of increasing an accounting loss, such as violating a debt covenant.

If tax rules create incentives for firms to increase reported losses, then a natural question is

whether analysts anticipate corporate responses to these incentives when forecasting earnings.

Analysts are often viewed as sophisticated users of accounting information, but their forecasts may

ignore carryback-based incentives if the tax disclosures are complex or provide noisy signals of true

tax status (Chen and Schoderbek 2000; Dhaliwal et al. 2004). For example, Amir and Sougiannis

(1999) find that analysts do not incorporate the information in deferred taxes contained in the SFAS

No. 109 disclosure, while Chen and Schoderbek (2000) document that analyst forecasts do not

incorporate the predictable earnings effect from the revaluation of deferred tax assets and liabilities

following a one percentage point increase in the top marginal corporate tax rate in 1993 (from 34

percent to 35 percent). Based on capital market responses to TRA 86, Plumlee (2003) finds that

analysts do not incorporate the impact of tax incentives and tax disclosures, particularly for more

complex tax issues.10 Shane and Stock (2006) show that analysts do not incorporate income

shifting induced by the 1986 tax rate change when forecasting earnings.

While the existing evidence is informative, it is limited to a handful of events that radically

changed tax law or GAAP disclosures of tax circumstances. Even if analysts ignore the impact of

one-time macro events, the tax benefits of accelerating losses are recurring and apply to a significant

set of firms every year. This provides a stronger case for analysts to forecast such information. This

reasoning leads to the following hypothesis:

H2: Analyst forecasts do not incorporate tax loss carryback incentives.

Finally, we examine the equity market’s reaction to tax-motivated loss shifting. If analysts

do not incorporate tax-motivated loss shifting in their earnings forecasts, then their estimates

9 Like Maydew’s (1997) focus on declining statutory marginal tax rates, our setting can potentially be interpretedas a decline in expected marginal tax rates, holding statutory tax rates largely constant. In other words, a firm thataccelerates a loss to claim a refund of taxes paid in a prior year essentially secures a tax benefit at anundiscounted statutory tax rate. But if management decides to wait to report the loss, then it is less likely the firmwill be able to carry the loss back for an immediate refund, and instead the firm would have to carry the lossforward to offset income in some future period, reducing the effective tax benefit of the deduction. Directestimates of the marginal tax rate, such as the simulated tax rates of Shevlin (1990), Graham (1996), and Blouinet al. (2010), are not well suited to our analysis for several reasons. First, they provide an estimate of theexpected marginal tax rate for current year profits and losses, but not the expected marginal tax rate in futureyears. Second, they do not directly address the dollar amount of potential refunds from carrybacks. Third, thesemeasures are endogenous to the reporting decisions we are analyzing.

10 Moreover, Outslay and McGill (2002) provide evidence that the tax disclosures of some firms are quite difficultto interpret and understand.



are likely to be systematically optimistic. Such optimism would lead to negative earnings

surprises as reflected in analysts’ forecast errors, measured as actual earnings minus forecasted

earnings. However, ‘‘unexpected’’ losses that generate cash tax refunds arguably create value

by reducing the present value of taxes paid and providing liquidity, so the market’s response to

losses motivated by cash tax benefits should be tempered relative to the firm whose losses are

not. Prior evidence suggests that investors do not understand the implications of tax incentives

on reported earnings (Shane and Stock 2006). However, there is some evidence that

informative disclosure of the tax-based reasons behind the earnings surprise leads to more

efficient market responses (Chen and Schoderbek 2000). We predict that managers have

incentives to disclose the transitory nature of tax-motivated losses to investors through

financial reporting disclosures, conference calls, and future earnings guidance. This leads to the

final hypothesis:

H3: The market reacts less negatively to earnings surprises of firms with tax-motivated loss-

shifting incentives.

III. SAMPLE DATA AND DESCRIPTIVE STATISTICS

Each year, we identify firms that have the incentive to accelerate tax losses to obtain a tax

refund by analyzing the time-series of estimated taxable income.11 We first calculate tax loss

carryback capacity (NOLC), which is an estimate of refundable income taxes paid in the earliest

year of the carryback period in year t and is described in Appendix B.12 The length of the carryback

period ranges from two to five years over our sample period. Unrefunded tax payments in the

earliest carryback year will expire if the firm does not claim a refund in year t. Thus, our main test

variable (D_NOL) for the period is an indicator variable equal to 1 if in year t the firm has

unrefunded tax payments on income in the earliest carryback year and analysts expect the firm to

report a loss in year t. This approach identifies a set of firms that will lose the ability to claim a

refund of taxes paid in a prior year (D_NOL) and the corresponding amount of potentially

refundable taxes (NOLC).

Because tax returns are unobservable, we follow prior research and rely on GAAP earnings

numbers to identify tax-motivated loss shifting (Scholes et al. 1992; Guenther 1994; Maydew 1997;

Shane and Stock 2006). Thus, we focus on forecasts and realizations of GAAP earnings and assume

that book earnings reflect the underlying tax loss reporting. We expect GAAP earnings numbers to

11 An alternative research design is to focus on tax law changes, as in Maydew (1997), who compares firm-yearswith loss carrybacks during a period immediately after TRA 86 to firm-years with loss carrybacks in otherperiods. This alternative setting differs from ours in two primary ways. First, Maydew (1997) tests the additionaltax incentives introduced by TRA 86, whereas we are interested in the general phenomenon whereby firmsalways have incentives to shift income and carryback losses. Second, Maydew (1997) conducts an ex postanalysis using firm-years with loss carrybacks. We do not require firms to have loss carrybacks, because avariable based on realized loss carrybacks would have a look-ahead bias in our capital market tests (that is, weare interested in capital market responses to expected tax loss shifting, which is measured prior to the returnwindow). Instead, we focus on an ex ante variable and predict whether firms and the capital market behave incertain ways. Maydew (1997) carefully controls for the look-ahead bias issue because he takes firm-years withloss carrybacks in other periods as the benchmark and examines the impact of additional tax incentivesintroduced by TRA 86.

12 Ideally, we would incorporate the firm’s actual net operating loss carryforwards to calculate tax status. NOLcarryforwards can arise from domestic, foreign, and local tax jurisdictions, but we do not have access to firms’tax returns, and information on the source of the NOL carryforwards is inconsistently disclosed in financialreports. Moreover, Compustat provides a single data item for NOL carryforwards, and this has been shown tohave significant measurement problems (Mills et al. 2003).



be a noisy proxy for the reported tax numbers, and therefore this approach works against finding

evidence consistent with our predictions.13

The ex ante nature of the D_NOL measure of the tax incentives to shift losses is important when

drawing inferences about capital market behavior. All information to construct D_NOL is available at

the beginning of year t. In this way, we test whether measures of tax loss carryback incentives are

associated with subsequent actions of managers, analysts, and investors. Because we focus on a single

year of tax payments and require that the firm have an expected loss for the year, D_NOL is a

conservative measure of the incentive to shift losses into the current year. We compare firms with

incentives to accelerate tax losses (D_NOL¼ 1) to a set of firms also expected to report a loss, but

without cash taxes available for refund. To do this, first we define an indicator variable D_NEG equal

to 1 if analysts forecast a loss eight months before the fiscal year-end, independent of tax carryback

opportunities. Because D_NOL is an interaction between D_NEG and the indicator for potential tax

refunds, the coefficient on D_NEG is the estimated effect for expected loss firms without carryback

opportunities, while the coefficient on D_NOL represents the incremental effect for expected loss firms

with carryback opportunities.

We focus on the manager’s financial reporting decisions to understand analysts’ and investors’

reaction to tax-motivated loss shifting. We include all firm-year observations with non-missing

earnings or analysts’ earnings forecasts, resulting in a final sample of 99,564 firm-year observations

from 1981 to 2010. Table 1 provides summary statistics of the primary variables used in this study.

Annual earnings, as a percentage of stock price, average�8.4 percent with a median of 4.4 percent.

Unexpected earnings, based on the difference between reported earnings and analysts’ forecasts

eight months prior to year-end (scaled by stock price), has a mean of�3.1 percent and median of

0.4 percent, respectively. With regard to financial variables, the average firm in our sample has a

market value (MV) of $2.967 billion and a book-to-market equity ratio (BM) of 0.576. The average

book leverage ratio for sample firms is 22.2 percent, while EBITDA averages 11.2 percent of total

assets. All financial variables are measured at the end of year t�1. On average, about 10.8 percent of

firms are expected to report losses in our 1981 through 2010 sample period (D_NEG ¼ 1). Of all

such firms with expected losses, the subset of firms with the incentive to accelerate tax losses under

our definition (D_NOL ¼ 1) account for 2.6 percent of all firm-year observations, compared with

7.8 percent (10.8 percent � 2.6 percent) for loss firms without these incentives.

IV. EMPIRICAL EVIDENCE

Evidence of Tax-Motivated Loss Shifting Based on a Time-Series Earnings Model

To test whether firms with tax-motivated loss-shifting incentives increase losses, we estimate

the following regression that includes a set of control variables:

Et ¼ b0 þ b1D NOL þ b2D NEGþ b3Et�1 þ b4Et�2 þ b5LOSSt�1 þ b6LOSSt�2

þ b7Et�1 � LOSSt�1 þ b8Et�2 � LOSSt�2 þ b9LogðMVt�1Þ þ b10ACCt�1

þb11RETt�1 þ et; ð1Þ

13 Tax reporting rules generally start with financial reporting rules, but there are significant exceptions. First, theGAAP financial statements will tend to consolidate more entities than the firm’s U.S. tax return. GAAPeffectively requires the firm to consolidate all entities owned at least 50 percent, whereas the U.S. tax returnignores foreign subsidiaries and those where the firm owns less than 80 percent. Second, tax rules often requirean actual transaction to record a loss. For example, an impairment of an asset’s value would show up as anexpense on the GAAP financial statements but would be absent from the firm’s tax returns, and hence thecalculation of taxable income, until the firm actually sells the asset. Maydew (1997) finds that income shiftingaround the 1986 Tax Act was concentrated in gross margin and SG&A numbers.



TABLE 1

Descriptive Statistics

Panel A: Univariate Statistics

Variable Mean Std. Dev. Min. Q1 Median Q3 Max.

Et �0.084 0.768 �15.620 �0.008 0.044 0.073 0.445

FEt �0.031 0.117 �1.556 �0.028 �0.004 0.005 0.617

RETt 0.135 0.676 �0.997 �0.211 0.060 0.346 22.881

ARETt 0.000 0.050 �0.664 �0.022 0.000 0.023 0.978

D_NOL 0.026 0.160 0.000 0.000 0.000 0.000 1.000

D_NEG 0.108 0.310 0.000 0.000 0.000 0.000 1.000

MVt�1 2.967 13.941 0.000 0.099 0.328 1.239 508.329

BMt�1 0.576 0.476 �1.321 0.278 0.479 0.754 5.699

RETt�1 0.192 0.823 �0.996 �0.189 0.083 0.383 53.660

ACCt�1 �0.032 0.090 �0.407 �0.076 �0.036 0.005 0.372

LEVt�1 0.222 0.201 0.000 0.044 0.186 0.345 0.954

EBITDAt�1 0.112 0.178 �1.175 0.074 0.134 0.197 0.538

COVt 7.090 6.990 1.000 2.000 5.000 10.000 50.000

Panel B: Correlation Matrix

Et FEt RETt D_NOL D_NEG MVt�1 BMt�1 COVt�1

Et 1

FEt 0.38 1

RETt 0.15 0.16 1

D_NOL �0.11 �0.09 0.02 1

D_NEG �0.24 �0.06 0.00 0.43 1

MVt�1 0.03 0.05 �0.02 �0.02 �0.06 1

BMt�1 �0.11 �0.19 0.10 0.09 �0.01 �0.09 1

COVt 0.07 0.11 0.00 �0.04 �0.13 0.31 �0.14 1

RETt�1 0.11 0.15 �0.06 �0.07 �0.06 0.01 �0.19 0.00

Et is annual Compustat earnings before extraordinary items per share scaled by stock price at the firm’s fiscal year-end.Forecast error (FE) is calculated as the difference between the actual earnings and the median analyst forecast made eightmonths prior to fiscal year-end, scaled by stock price at the forecast date. RETt is year t’s annual returns starting eightmonths prior to fiscal year-end. ARETt is average three-day earnings announcement return in year t, measured as rawreturns minus value-weighted market returns over the three-day [�1, 1] period, where day 0 is the earningsannouncement date. NOLC is the net operating loss carryback capacity limit (see Appendix B for variable measurement).D_NOL is a dummy variable with the value of 1 if NOLC is positive for a firm in an expected loss position, and 0otherwise, indicating the firm’s incentives to carry back NOLs and to claim tax refunds. D_NEG is a dummy variablewith the value of 1 if analysts’ forecasts of year t’s earnings are negative, where forecasts were made eight months priorto fiscal year-end. MV is a firm’s market value of equity at the end of year t�1. BM is the book-to-market ratio at the endof year t�1. COV is the number of analysts covering the firm at the forecast date. ACC is total accruals scaled by averageassets. LEV is the leverage ratio. EBITDA is earnings before interest, taxes, depreciation, and amortization scaled byaverage total assets. Please see Appendix A for detailed variable definitions. The sample includes 99,564 firm-yearobservations with non-missing observations of FE or annual earnings from 1981 to 2010. All variables except forD_NOL, D_NEG, COV, and return variables are winsorized at the 1st and 99th percentiles.



where:

E ¼ the reported earnings per share scaled by the firm’s stock price at fiscal year-end;

D_NOL¼ a dummy variable indicating a firm’s incentive to carry back tax losses to claim a tax

refund;

D_NEG¼ a dummy variable that takes a value of 1 if analysts are forecasting negative annual

earnings for year t four months after year t begins;

Et�1 and Et�2 ¼ reported earnings for years t�1 and t�2, respectively;14

LOSSt�1 (LOSSt�2) ¼ a dummy variable taking a value of 1 if Et�1 (Et�2) is negative, and 0

otherwise;

MV ¼ a firm’s market value of equity at the beginning of the year;

ACC ¼ total accruals scaled by average assets; and

RET ¼ the 12-month buy-and-hold return beginning eight months before a firm’s fiscal year-

end.

Earnings variables and ACC are winsorized at the 1st and 99th percentiles.

Table 2 presents results for three progressively richer regression models, with D_NOL as the

main variable of interest. We expect the coefficient on D_NOL to be negative, reflecting the

hypothesized tax-motivated loss shifting. Because expected earnings for D_NOL firms are negative

by definition, we include D_NEG in the regression to ensure that the coefficient on the D_NOLvariable does not pick up any difference in earnings surprises between profit and loss firms (Hayn

1995). Because D_NOL is implicitly an interaction between D_NEG and an indicator for carryback

potential, the coefficient on D_NOL reflects the incremental loss reported by firms with an option to

use losses to obtain a cash refund of taxes paid in the earliest carryback year.

Across the three specifications of Equation (1) in Table 2, the coefficient on D_NOL is

significantly negative. For example, in column (1), the coefficient on D_NOL is�0.073 (t¼�2.61),

indicating that earnings are about 7.3 percent of prior-year-end market value lower in years when

the firm has a tax-based incentive to accelerate losses.15 The magnitude of D_NOL is also

economically significant. As the coefficient on D_NEG is �0.157 (t ¼�7.90), we interpret the

results to mean that reported earnings are 46 percent (¼ 0.073/0.157) lower for loss firms with tax-

based incentives to accelerate losses than for loss firms without those incentives. This result holds

across specifications with additional controls in columns (2) and (3). Overall, the results in Table 2

provide consistent support for the prediction that firms engage in tax-motivated loss shifting even

when statutory tax rates are constant.

Firms have a number of ways to accelerate losses, such as frontloading expenses, writing down

inventory, and selling assets at a loss. To provide more evidence on the drivers of reported losses

for firms with tax loss carryback incentives, we analyze recurring and nonrecurring items during the

event years, and compare them to both previous and subsequent years. As the literature offers no

well-established model for earnings components, we adopt a simple random walk model and use

both previous and subsequent years as the benchmark.

14 Following the capital markets research, we scale earnings and earnings surprises by stock price. In this way, thedeflator is stock price for both stock returns and earnings surprises in the return regressions.

15 The economic magnitude is large for two reasons. First, we use annual earnings, and some firms report largelosses relative to their market values. Second, we winsorize the data and do not introduce any ad hoc cutoff ofdata based on reported earnings. If we delete observations with earnings scaled by market value in the top andbottom 1 percent, the coefficient on D_NOL becomes �0.049 (p , 0.01). If we delete observations with anabsolute value of earnings scaled by market value above 1, the coefficient on D_NOL becomes �0.026 (p ,0.01). Although the magnitude of the coefficient drops as we drop more observations with extreme values, the t-statistics and p-values change very little. In all our tables, we follow the general practice in the literature ofwinsorizing the variables at 1 percent and 99 percent.



Panel A of Table 3 documents firm performance in the D_NOL years relative to previous or

subsequent years. Using the previous year as the benchmark, we find that loss firms with carryback

incentives have lower unexpected earnings than loss firms without such incentives, with the mean

and median differences of �11.3 percent (t ¼�3.17) and �0.5 percent (t ¼�1.97), respectively.

Unexpected operating income is also significantly lower for D_NOL firms, but unexpected special

items are similar to those of loss firms without carryback incentives. Because D_NOL firms are

presumed to accelerate tax losses from future periods, it is also useful to compare the reported

numbers to earnings in the subsequent year. When we do this in the lower half of Panel A, earnings,

operating income, and special items all show significant differences between loss firms with

TABLE 2

Analysis of Reported Earnings Based on Time-Series Models

(1) (2) (3)

Intercept �0.053 0.005 �0.080

(�2.64) (0.94) (�5.52)

D_NOL �0.073 �0.079 �0.084

(�2.61) (�3.06) (�3.14)

D_NEG �0.157 �0.129 �0.114

(�7.90) (�7.32) (�5.94)

Et�1 0.948 0.408 0.594

(5.10) (6.87) (6.15)

Et�2 0.130 �0.142 �0.308

(2.35) (�1.12) (�1.55)

LOSSt�1 �0.064 �0.035

(�3.89) (�2.35)

LOSSt�2 �0.030 �0.039

(�2.42) (�2.75)

Et�1 � LOSSt�1 0.555 0.270

(2.74) (1.99)

Et�2 � LOSSt�2 0.292 0.549

(1.52) (1.89)

ln(MVt�1) 0.011

(7.60)

ACCt�1 �0.159

(�4.16)

RETt�1 0.094

(3.86)

Adj. R2 0.252 0.258 0.281

This table reports multivariate regression results. The dependent variable is Et, the annual Compustat earnings beforeextraordinary items per share scaled by stock price at a firm’s fiscal year-end. LOSSt�1 (LOSSt�2) is a dummy variablewith the value of 1 if Et�1 (Et�2) is negative, and 0 otherwise. NOLC is the net operating loss carryback capacity limit(see Appendix B for variable measurement). D_NOL is a dummy variable with the value of 1 if NOLC is positive for afirm in an expected loss position, and 0 otherwise, indicating the firm’s incentives to carry back tax losses to claim a taxrefund. D_NEG is a dummy variable with the value of 1 if analysts’ forecasts of year t’s earnings are negative, whereforecasts were made eight months prior to fiscal year-end. MV is a firm’s market value of equity at the end of year t�1.ACC is total accruals scaled by average assets. RET is the 12-month buy-and-hold return starting from eight monthsbefore a firm’s fiscal year-end. Please see Appendix A for detailed variable definitions. The sample includes 85,577 firm-year observations with non-missing earnings variables (Et, Et�1, and Et�2) from 1981 to 2010. t-statistics in parenthesesare based on the Fama-MacBeth regression approach. Earnings variables and ACC are winsorized at the 1st and 99thpercentiles.



TABLE 3

Analysis of Recurring and Nonrecurring Items

Panel A: Performance Relative to Previous or Subsequent Year

Loss Firms withCarryback Incentives

(D_NOL ¼ 1)

Loss Firms withoutCarryback Incentives

(D_NOL ¼ 0)Difference

Mean Median Mean MedianMean

(t-stat.)Median(Z-stat.)

Performance relative to previous year (t�1)

Earnings �0.181 �0.013 �0.068 �0.008 �0.113*** �0.005**

[(Et � Et�1)/P] (�3.17) (�1.97)

Operating earnings �0.077 �0.009 0.016 �0.003 �0.093*** �0.006***

[(OPINCt � OPINCt�1)/P] (�5.63) (�2.74)

Special items �0.013 0.000 �0.051 0.000 0.038 0.00

[(SIt � SIt�1)/P] (1.68) (1.82)

Performance relative to subsequent year (tþ1)

Earnings �0.347 �0.046 �0.268 �0.012 �0.080** �0.034***

[(Et � Etþ1)/P] (�2.14) (�9.96)

Operating earnings �0.275 �0.049 �0.162 �0.016 �0.113*** �0.033***

[(OPINCt � OPINCtþ1)/P] (�3.00) (�10.41)

Special items �0.170 �0.001 �0.072 0.000 �0.098*** �0.001***

[(SIt � SItþ1)/P] (�4.97) (�6.88)

Panel B: Logistic Model of Negative One-Time Items in Event Years

Negative Special Items Loss on Sale of PP&E and Investment

Coefficient(p-value) Marginal Effects

Coefficient(p-value) Marginal Effects

Intercept �1.847 �1.756

(, 0.01) (, 0.01)

D_NOL 0.622 14.23% 0.247 5.01%

(, 0.01) (, 0.01)

D_NEG �0.294 �6.65% �0.268 �5.41%

(, 0.01) (, 0.01)

Et�1 �0.411 �9.41% �0.083 �1.67%

(, 0.01) (0.011)

Et�2 �0.406 �9.32% �0.103 �2.03%

(, 0.01) (0.009)

LOSSt�1 0.519 11.76% 0.032 0.63%

(, 0.01) (0.338)

LOSSt�2 0.287 6.49% 0.047 1.00%

(, 0.01) (0.145)

ln(MVt�1) 0.221 5.03% 0.134 2.71%

(, 0.01) (, 0.01)

ACCt�1 0.347 8.00% �0.759 �15.55%

(, 0.01) (, 0.01)

RETt�1 �0.198 �4.53% �0.035 �0.71%

(, 0.01) (0.002)

(continued on next page)



carryback incentives and loss firms without. In general, the magnitude of the loss is larger when we

use the subsequent year rather than the previous year as the benchmark, especially with medians.

This result indicates that the earnings changes are transitory, and is consistent with D_NOL firms

shifting expenses and losses from the subsequent year to the current year. Finally, the magnitude of

the difference in operating income is similar to that of the earnings difference, especially for

medians, and this is driven by the infrequency and variability of special items.

In Table 3, Panel B, we further examine whether firms with tax incentives are more likely to

report negative one-time items. We focus on negative special items and the loss on the sale of

PP&E and investment under the premise that firms can sell assets at a loss to recapture taxes paid in

previous years. Both logistic models show significant coefficients on D_NOL, and the marginal

effects suggest that firms with carryback incentives are 14.23 percent more likely to report a

negative special item and 5.01 percent more likely to report a loss from an asset sale. As D_NOL is

implicitly an interaction between D_NEG and an indicator for carryback potential, these effects are

incremental to what we observe for loss firms without carryback incentives (D_NEG ¼ 1 and

D_NOL ¼ 0). Finally, the coefficients on control variables are largely significant with expected

signs. Profitable firms and firms with high past returns are less likely to report negative one-time

items, whereas past loss firms and firms with high accruals tend to report negative one-time items.

In sum, we find strong evidence that firms increase losses in order to claim tax refunds. Both

core earnings and one-time items are significantly lower in D_NOL years than in adjacent years,

suggesting that firms use both recurring and non-recurring items to shift losses.

Do Analysts Incorporate Tax-Motivated Loss Shifting in Their Earnings Forecasts?

If analysts anticipate tax-motivated loss shifting and incorporate these incentives into their

forecasts, then we expect to find no difference in analyst forecast errors—actual earnings less the

analysts’ forecast of earnings—between firms with tax incentives to accelerate losses (D_NOL¼ 1)

and those without (D_NOL¼0). But if analysts ignore or do not fully incorporate information about

taxes, then their forecast errors should be more negative for firms with tax incentives to accelerate

losses. We conduct both univariate and multivariate analyses on analysts’ forecast errors. In

univariate analyses, Panel A of Table 4 shows that the average analyst forecast error for firms with a

tax-motivated loss-shifting incentive (D_NOL firms), scaled by stock price, is �9.5 percent (p ,

0.01). For all firms without this carryback incentive, including firms with expected positive earnings

TABLE 3 (continued)

**, *** Indicate significant differences at the 5 percent and 1 percent levels, respectively.Panel A reports financial performance relative to previous and subsequent years, respectively. Loss firms are defined as

firms with negative analyst earnings forecasts made eight months prior to fiscal year-end. Earnings are earnings beforeextraordinary items. Operating income (OPINC) is operating income after depreciation and amortization. P is the marketvalue of equity. There are 2,210 and 8,068 firm-year observations for loss firms with and without carryback incentives,respectively. Panel B reports the results of estimating a logistic model for negative one-time items. The data on gain/losson sale of PP&E and investment start in 1995. D_NEG is a dummy variable with the value of 1 if analysts’ forecasts ofyear t’s earnings are negative, where forecasts were made eight months prior to fiscal year-end. E is annual Compustatearnings before extraordinary items per share scaled by stock price at a firm’s fiscal year-end. LOSSt�1 (LOSSt�2) is adummy variable with the value of 1 if Et�1 (Et�2) is negative, and 0 otherwise. NOLC is the net operating loss carrybackcapacity limit (see Appendix B for variable measurement). D_NOL is a dummy variable with the value of 1 if NOLC ispositive for a firm in an expected loss position, and 0 otherwise, indicating the firm’s tax incentive to accelerate losses.MV is a firm’s market value of equity at the end of year t�1. ACC is total accruals scaled by average assets. RET is the12-month buy-and-hold return starting from eight months before a firm’s fiscal year-end. The sample includes 85,577firm-year observations with non-missing earnings variables from 1981 to 2010.



TABLE 4

Analyst Forecast Error Properties

Panel A: Analyst Forecast Errors for Firms with and without Tax-Motivated Loss-ShiftingIncentives

Loss Firms withTax Incentives(D_NOL ¼ 1)

Other Firms(D_NOL ¼ 0)

Difference(t-stat.)

Loss Firms withoutTax Incentives

(D_NEG ¼ 1, D_NOL ¼ 0)Difference

(t-stat.)

A B A � B C A � C

Entire Sample

Forecast error �0.095 �0.029 �0.066

(�27.85)

�0.042 �0.053

(�9.91)

1987–1990

Forecast error �0.204 �0.055 �0.149

(�10.37)

�0.143 �0.071

(�3.24)

Panel B: Multivariate Regressions of Analyst Forecast Errors (FEt)

(1) (2)

Intercept �0.078 �0.048

(�10.82) (�11.06)

FEt�1 0.426

(10.88)

D_NOL �0.048 �0.027

(�6.25) (�3.81)

D_NEG �0.015 0.004

(�1.86) (0.72)

ln(MVt�1) 0.009 0.005

(8.92) (7.60)

ln(COVt) 0.004 0.002

(2.86) (1.87)

BMt�1 �0.023 �0.018

(�5.42) (�4.38)

RETt�1 0.035 0.027

(6.23) (5.45)

ACCt�1 �0.006 �0.058

(�0.87) (�8.96)

Adj. R2 0.112 0.180

Forecast error, FE, is calculated as the difference between I/B/E/S actual earnings and the median analyst forecast madeeight months prior to a firm’s fiscal year-end, scaled by stock price at the forecast date. NOLC is the net operating losscarryback capacity limit (see Appendix B for variable measurement). D_NOL is a dummy variable with the value of 1 ifNOLC is positive for a firm in an expected loss position, and 0 otherwise, indicating the firm’s tax incentive to acceleratelosses. D_NEG is a dummy variable with the value of 1 if analysts’ forecasts of year t’s earnings are negative, whereforecasts were made eight months prior to fiscal year-end. MV is a firm’s market value of equity at the end of year t�1.COV is the number of analysts covering the firm at the forecast date. BM is the book-to-market ratio at the end of yeart�1. RET is the 12-month buy-and-hold return starting from eight months before a firm’s fiscal year-end. ACC is totalaccruals scaled by average assets. Please see Appendix A for detailed variable definitions. The sample includes 83,875firm-year observations with non-missing FE from 1981 to 2010. In all columns, t-statistics in parentheses are based onthe Fama-MacBeth approach. FE, BM, and ACC are winsorized at the 1st and 99th percentiles.



surprises, analyst forecast errors are lower, averaging �2.9 percent.16 The resulting difference in

analyst forecast errors of 6.6 percent of stock price (p , 0.01) is highly significant. We also

compare the forecast errors in D_NOL firms to expected loss firms without carryback incentives and

find that D_NOL firms have significantly greater negative forecast errors, differing by 5.3 percent of

stock price (p , 0.01) between the two groups of firms. Overall, these results are consistent with

the conclusion that analysts do not fully incorporate tax-motivated loss-shifting behavior into their

earnings forecasts.

To control for other factors that could be correlated with both the tax incentives to shift losses

and the forecast error, we estimate the following equation:

FEt ¼ b0 þ b1FEt�1 þ b2D NOL þ b3D NEG þ b4LogðMVt�1Þ þ b5LogðCOVtÞþb6BMt�1 þ b7ACCt�1 þ b8RETt�1 þ et; ð2Þ

where FE is analyst forecast error. If firms increase losses in year t to capture tax refunds and

analysts do not anticipate this behavior, then we expect a negative coefficient on D_NOL, where

again the coefficient on D_NOL represents the effect that is incremental to the association between

expected losses and realized forecast errors estimated by the coefficient on D_NEG. Following

previous research, we control for other determinants of earnings forecast bias. The literature argues

that analysts have incentives to issue optimistic forecasts for firms with poor information

environments to obtain access to management (Francis and Philbrick 1993; Lim 2001). We include

firm size (MV) and analyst coverage (COV) to capture a firm’s information environment (Atiase

1987; Zhang 2006b). We also include the book-to-market ratio (BM) to control for growth

opportunities, as growth firms have stronger incentives to meet or beat earnings targets, suggesting

a negative coefficient on BM. Prior literature documents evidence indicating that analysts are not

fully rational (see Kothari [2001] for a review). To allow for this possibility, we include RET and

FEt�1 to control for analyst underreaction to the information contained in stock returns and forecast

errors in the prior period. We further include accruals to control for the possibility that analysts do

not fully understand the implication of accruals for future earnings (Bradshaw et al. 2001; Teoh and

Wong 2002).

Table 4, Panel B summarizes the results of the forecast error regressions with and without the

lagged dependent variable as a control. In column (1), the coefficient on D_NEG is only marginally

significant (coeff. ¼�0.015, t ¼�1.86), providing weak evidence that analysts are surprised by

negative earnings news even when they expect the firm to report a loss. Turning to our variable of

interest, the coefficient on D_NOL is�0.048 (t¼�6.25), which is much larger than the coefficient

on D_NEG, and indicates that analysts fail to anticipate the additional losses reported by firms with

tax incentives to shift income and that analysts’ forecast errors are predictable ex ante. We add the

lagged forecast error as a control in column (2) to control for unobservable firm effects, and find

that the coefficient on D_NOL drops to �0.027 but remains highly significant (t ¼ �3.81).

Economically, analysts’ forecast errors, scaled by stock price, are about 2.7 percentage points larger

for loss firms with carryback incentives than for firms without. This translates into about $64.7

billion of unexpected loss shifting for firms with incentives to use those losses to obtain cash tax

refunds between 1981 and 2010.17

16 The observed negative forecast errors for D_NOL ¼ 0 firms is consistent with the optimistic bias in analysts’earnings forecasts documented in the prior literature (O’Brien 1987). Analysts’ forecast errors are reliablynegative when the forecast horizon is long, and turn to zero or positive immediately before earningsannouncements (Richardson et al. 2004).

17 We estimate the $64.7 billion as the 2.7 percent multiplied by the average sample firm’s market value of $1.204billion and then multiply that by 1,990 firm-year observations with tax incentives in Model (2).



Equity Market Response to Tax-Motivated Loss Shifting

Finally, we examine the reaction of equity investors to tax-motivated loss shifting. If investors

naively follow analysts’ earnings forecasts, which tend to miss the tax incentives for accelerating

losses, then the market price will not fully reflect the expected tax benefits and the market reaction

will be determined by analysts’ forecast errors. Alternatively, if investor reactions are determined

by the revision in expected future cash flows and discount rates, then the positive cash flow effects

associated with accelerating a tax loss should lead to more positive (or less negative) market

reactions for firms with the tax-based incentives to shift losses, independent of the size of the

forecast error.

We consider both annual returns and earnings announcement returns and report the results in

Table 5. The first two columns are based on annual returns, and we estimate them with and without

the analyst forecast error as a control. In the full model, we control for analyst forecast errors, firms

with expected losses at the beginning of the period using D_NEG, as well as size, book-to-market,

and prior returns. Column (1) shows that the partial correlation between D_NOL and annual returns

is positive, albeit insignificant, suggesting that the market reaction to negative earnings

announcements by D_NOL firms is similar to those by non-D_NOL firms, even though D_NOLfirms report much larger losses as shown in Tables 2 and 4. Since D_NOL is negatively correlated

with the forecast error, we add analyst forecast errors as a control variable in column (2), and find a

positive and significant coefficient on D_NOL (coeff.¼ 0.074, t¼ 2.17), implying that the annual

stock return of a firm with tax-motivated loss-shifting incentives is about 7.4 percent greater than a

loss firm without such incentives, holding analyst forecast errors constant.

In columns (3) and (4), we use abnormal return during the earnings announcement window as

the dependent variable. This allows us to focus on the importance of the earnings release in

conveying information about cash flow effects of the tax loss. Similar to the annual return

regressions, we find a positive yet insignificant coefficient on D_NOL without controlling for

analyst forecast errors, and this turns positive and significant once we include analyst forecast errors

(coeff. ¼ 0.006, t ¼ 3.23). Overall, the results suggest that investors understand either the tax

benefits of the loss carryback, the transitory nature of the accelerated losses claimed by carryback

firms, or both.

In summary, we find that the market reaction to earnings reported by firms with incentives to

accelerate tax losses is less negative than the market reaction to earnings by firms without such

incentives. This occurs despite the fact that firms with tax incentives to shift losses actually report

larger losses using both prior earnings and analyst forecasts as a benchmark, and is consistent with

these losses being transitory, increasing cash flows and providing liquidity.18

V. ADDITIONAL ANALYSIS AND SENSITIVITY CHECKS

Cross-Sectional Variation in Tax-Motivated Loss Shifting

In Table 4, we provide some evidence of the time-series variations in firms’ incentives to

engage in tax-motivated loss shifting—firms have particularly strong tax incentives to shift losses

around TRA 86. As Maydew (1997) shows, TRA 86 reduced the corporate tax rate from 46 percent

in 1986 to 34 percent in 1988, providing firms with stronger incentives to accelerate losses between

18 Our market reaction results are in contrast to those in Shane and Stock (2006), who show that investors do notunderstand income shifting around one-time tax rate change around 1986. We attribute the different resultsbetween these two research settings to the recurring nature of loss carrybacks and better firm communicationwith the market in the 1990s and 2000s. Management guidance was virtually non-existent in the 1980s.



1987 and 1990 in order to generate a refund of taxes paid at the higher rate. In this section, we

explore the cross-sectional variations in firms’ incentives to carry back tax losses.

While firms have a variety of incentives to carry back losses, we specifically test whether the

demand for liquidity drives the firm’s decision to increase cash flow through a tax refund. This

implicitly assumes that the incremental cost of external funds is higher than the cost of funds

obtained through a tax refund. Empirically, we measure financial constraints (FC) using a firm’s

debt minus cash and short-term investment scaled by total assets. We then add FC, measured at

prior year-end, and its interaction term with D_NOL (D_NOL � FC) to Equations (1) and (2) as

follows:

Et ¼ b0 þ b1D NOLþ b2FCt�1 þ b3D NOL � FCt�1 þ b4D NEGþ b5D NEG � FCt�1

þ b6Et�1 þ b7Et�2 þ b8LOSSt�1 þ b9LOSSt�2 þ b10LOSSt�1 � Et�1

þ b11LOSSt�2 � Et�2 þ b12LogðMVt�1Þ þ b13ACCt�1 þ b14RETt�1 þ et: ð3Þ

TABLE 5

Analysis of Stock Returns

Dependent Variable ¼

RETt

(1)RETt

(2)ARETt

(3)ARETt

(4)

Intercept 0.153 0.277 �0.0026 0.0055

(2.07) (3.68) (�1.67) (3.54)

FEt 1.554 0.1109

(10.72) (8.73)

D_NOL 0.003 0.074 0.0014 0.0058

(0.09) (2.17) (0.71) (3.23)

D_NEG �0.018 0.004 �0.0086 �0.0070

(�0.39) (0.09) (�6.61) (�5.09)

ln(MVt�1) �0.007 �0.023 0.0001 �0.0008

(�0.99) (�3.04) (1.22) (�4.10)

BMt�1 0.032 0.063 0.0031 0.0050

(1.60) (3.19) (4.52) (6.67)

RETt�1 �0.016 �0.064 0.001 �0.0029

(�0.47) (�1.95) (0.24) (�6.43)

Adj. R2 0.043 0.113 0.008 0.045

RETt is year t’s annual returns starting from eight months prior to fiscal year-end. ARETt is the average three-day earningsannouncement return in year t, measured as raw returns minus value-weighted market returns over the three-day [�1, 1]period, where day 0 is the earnings announcement date. FEt is the forecast error for year t, calculated as the differencebetween I/B/E/S actual earnings and the median analyst forecast made eight months prior to a firm’s fiscal year-end,scaled by stock price at the forecast date. NOLC is the net operating loss carryback capacity limit (see Appendix B forvariable measurement). D_NOL is a dummy variable with the value of 1 if NOLC is positive for a firm in an expectedloss position, and 0 otherwise, indicating the firm’s incentives to carry back NOLs and to claim tax refunds. D_NEG is adummy variable with the value of 1 if analysts’ forecasts of year t’s earnings are negative, where forecasts were madeeight months prior to fiscal year-end. MV is a firm’s market value of equity at the end of year t�1. BM is the book-to-market ratio at the end of year t�1. RETt is the 12-month buy-and-hold return starting from eight months before a firm’sfiscal year-end. Please see Appendix A for detailed variable definitions. The sample includes 83,875 firm-quarterobservations with non-missing observations of FE and return variables from 1981 to 2010. In all columns, t-statistics inparentheses are based on the Fama-MacBeth regression approach. FE and BM are winsorized at the 1st and 99thpercentiles.



FEt ¼ b0 þ b1D NOLþ b2FCt�1 þ b3D NOL � FCt�1 þ b4D NEGþ b5D NEG � FCt�1

þ b6LogðMVt�1Þ þ b7LogðCOVtÞ þ b8BMt�1 þ b9ACCt�1 þ b10RETt�1 þ et: ð4Þ

Our main prediction is that the coefficients on the interaction term D_NOL � FC should be

significantly negative in Equations (3) and (4), suggesting that more financially constrained firms

have stronger loss-shifting incentives. In Table 6, we find this is indeed the case. In the regression

of future earnings, the coefficient on the interaction term D_NOL � FC is�0.105 (t¼�1.97). In the

regression of analyst forecast errors, the coefficient on the interaction term D_NOL � FC is�0.028

(t ¼�2.24). If we further add the prior-period forecast errors as an additional control variable to

Equation (4), then the interaction term D_NOL � FC becomes marginally significant (coeff. ¼�0.018, t ¼�1.65). The results provide some support for the role of liquidity benefits because

financially constrained firms appear more likely to accelerate losses to access cash from tax refunds,

and this behavior results in more negative earnings and analyst forecast errors for those firms.

Analyst Revisions of Future Earnings

Section IV shows that analysts do not fully incorporate tax-related losses in their earnings

forecasts. It is possible that some analysts may learn about such tax-motivated loss shifting over

time, for example when the firm announces earnings or an asset sale, and revise their earnings

forecasts accordingly. If tax-related losses represent one-time attempts to shift income, then a

testable implication is that current-year earnings forecasts should fall, but future earnings forecasts

should be relatively unchanged, suggesting a smaller correlation in analysts’ forecast revisions

between the current and subsequent years’ earnings. To test this prediction, we analyze the

following model:

REVtþ1 ¼ b0 þ b1D NOLþ b2D NEG þ b3REVt þ b4REVt � D NOLþ b5REVt � D NEGþ b6LogðMVt�1Þ þ b7LogðCOVtÞ þ b8BMt�1 þ b9RETt�1 þ b10ACCt�1 þ etþ1;

ð5Þ

where REVt measures analyst forecast revision for year t’s earnings from eight months before year

t’s fiscal year-end to one month after, and REVtþ1 measures analyst forecast revision for year tþ1’s

earnings during the same period. We expect that on average b3 will be positive (revisions are

positively correlated), b5 will be negative (less correlated for negative earnings shocks), and b4 will

be negative (revisions of tþ1 forecasts are even less sensitive to current revisions given tax

incentives to report losses).

The results are consistent with our predictions. Untabulated results show a positive coefficient

on REVt (b3 ¼ 0.558, t ¼ 22.50) and a negative coefficient on REVt � D_NEG (b5 ¼�0.114, t ¼�4.44). More importantly, the coefficient on REVt � D_NOL is significantly negative (b4¼�0.068,

t¼�2.05). The results suggest that revisions of future years’ earnings are less sensitive to revisions

in current-year earnings for firms with tax loss carryback incentives, consistent with the idea that

tax-related losses reflect one-time attempts to shift income. In other words, to the extent that

analysts revise their current-year forecasts downward to reflect better information about the firm’s

tax-based incentive to accelerate losses into that year, they are less likely to revise forecasts of

future earnings in the same direction.

Other Robustness Checks

Our main analysis is based on an indicator variable version of NOLC, the capacity for tax loss

carrybacks. In untabulated analysis, we incorporate the magnitude of tax loss carrybacks by

replacing D_NOL with NOLC and re-estimate the regressions. The results continue to hold. For

example, the coefficient on NOLC in Equation (2) is�0.795 (t¼�3.54), while the coefficient on



TABLE 6

Analysis of Financial Constraints

Panel A: Regression of Earnings (Et)

Intercept �0.118

(�9.46)

D_NOL �0.060

(�2.48)

D_NEG �0.133

(�4.47)

FCt�1 �0.055

(�5.52)

D_NOL � FCt�1 �0.105

(�1.97)

D_NEG � FCt�1 �0.241

(�3.65)

Et�1 0.509

(7.79)

Et�2 �0.038

(�0.45)

LOSSt�1 �0.029

(�2.49)

LOSSt�2 �0.029

(�3.23)

Et�1 � LOSSt�1 0.174

(2.43)

Et�2 � LOSSt�2 0.133

(1.20)

ln(MVt�1) 0.016

(10.77)

ACCt�1 �0.108

(�4.58)

RETt�1 0.079

(5.94)

Adj. R2 0.259

Panel B: Regression of Analyst Forecast Errors (FEt)

Intercept �0.082

(�14.21)

D_NOL �0.030

(�4.86)

D_NEG �0.014

(�2.02)

FCt�1 �0.021

(�6.40)

D_NOL � FCt�1 �0.028

(�2.24)

D_NEG � FCt�1 �0.062

(�2.85)

(continued on next page)



NOLC in Equation (3) is �0.151 (t ¼�2.29). One caveat of the NOLC regressions is that actual

losses may not be linearly correlated with loss carryback capacity.

We use annual observations in the main analysis on the premise that tax reporting is done

annually. As a robustness check, we rerun our analysis based on quarterly observations by

assigning D_NOL in year t�1 to four quarterly earnings surprises in year t. A caveat of the quarterly

approach is that four quarterly observations within a firm-year are not independent. As an

alternative, we execute our analyses using only the fourth-quarter observations. In both

specifications, the tenor of the results is unchanged (e.g., significantly negative coefficients on

D_NOL in the regressions of earnings and analyst forecast errors, and positive coefficients on

D_NOL in the return regressions). We also examine how the coefficient pattern varies across

quarters and find that our results are driven by the third and fourth fiscal quarters, consistent with

the idea that firms are more likely to shift losses in later quarters as they receive more precise

information about expected tax and accounting profits, loss-shifting opportunities, and liquidity

needs.

VI. CONCLUSION

We identify firms with an incentive to exercise an option to obtain a cash refund of prior tax

payments that would otherwise expire at the end of the year. Over the 1981–2010 period with

relatively stable statutory tax rates, firms appear to accelerate reported losses to generate cash flows

from tax refunds, and do so to a greater extent when they are financially constrained. This evidence

suggests that such tax-based incentives have pervasive effects on reporting decisions across time

even when statutory tax rates are stable and thus have broad implications for capital markets

research.

TABLE 6 (continued)

ln(MVt�1) 0.010

(11.18)

ln(COVt) 0.003

(2.70)

BMt�1 �0.018

(�6.29)

RETt�1 0.029

(7.27)

ACCt�1 �0.013

(�2.42)

Adj. R2 0.128

Panels A and B report regression results for earnings (Et) and analyst forecast errors (FEt), respectively. E is annualCompustat earnings before extraordinary items scaled by stock price at a firm’s fiscal year-end. FE is the differencebetween I/B/E/S actual earnings and the median analyst forecast made eight months prior to a firm’s fiscal year-end,scaled by stock price at the forecast date. D_NOL is a dummy variable with the value of 1 if a firm has carrybackincentives, and 0 otherwise. D_NEG is a dummy variable with the value of 1 if analysts’ forecasts of year t’s earnings arenegative, where forecasts were made in the fourth month after fiscal year-end. FC is financial constraint measured as afirm’s debt minus cash and short-term investment scaled by total assets. LOSSt�1 (LOSSt�2) is a dummy variable with thevalue of 1 if Et�1 (Et�2) is negative, and 0 otherwise. MV is a firm’s market value of equity at the end of year t�1. ACC istotal accruals scaled by average assets. RET is the 12-month buy-and-hold return starting from eight months before afirm’s fiscal year-end. COV is the number of analysts covering the firm at the forecast date. Please see Appendix A fordetailed variable definitions. The sample includes 85,577 firm-year observations with non-missing earnings variables (Et,Et�1, and Et�2) in Panel A and 83,875 firm-year observations with non-missing FE in Panel B from 1981 to 2010.Earnings variables FE, BM, and ACC are winsorized at the 1st and 99th percentiles, and t-statistics in parentheses arebased on the Fama-MacBeth regression approach.



Our evidence also suggests that analysts do not incorporate these tax-motivated loss-shifting

incentives into their earnings forecasts because we find that analyst forecast errors are significantly

more negative for firms with incentives to accelerate tax losses. This evidence contrasts with the

view that analyst forecast errors proxy for earnings surprises, and reinforces the evidence on

analysts’ disregard for the information in the firm’s tax disclosures. Despite the relatively larger

unexpected losses for firms with tax incentives to accelerate losses, the stock market reacts in a way

that suggests investors value tax-motivated loss shifting. If we control for the size of the reported

loss, then the stock returns of firms with tax-motivated loss-shifting incentives are significantly less

negative than similar loss firms without such incentives.

Our evidence is relevant to understanding the growing importance of tax losses on firm value.

Recent innovations in takeover defenses are designed specifically to protect the value of tax losses,

and within the last decade, Congress has argued that the temporary liberalization of tax loss

carryback rules injects needed liquidity into the business sector. Taken together, our evidence

suggests that the incentive to carry back tax losses plays a material and persistent role in corporate

reporting decisions and capital markets activities.

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APPENDIX A

Variable Definitions

Et E is annual Compustat earnings before extraordinary items per share scaled by stock price

at a firm’s fiscal year-end;

FEt analyst forecast error, defined as (E � F)/Pt�1, where E is reported annual earnings per

share from I/B/E/S, F is the median analysts’ earnings-per-share forecast made eight

months prior to fiscal year-end, and Pt�1 is stock price at the forecast date;

NOLCt�1 net operating loss carryback capacity (see the estimation process in Appendix B for

details), indicating a firm’s tax paid in the earliest year of the NOL carryback window

that has not been refunded yet;

D_NEG a dummy variable with the value of 1 if analysts’ forecasts of year t’s earnings are

negative, where forecasts were made eight months prior to fiscal year-end;

D_NOL a dummy variable with the value of 1 if the firm has an expected loss (D_NEG ¼ 1) and

the net operating loss carryback capacity is positive (NOLC . 0), and 0 otherwise;

MVt�1 the market value of equity at the end of year t�1;

COVt the number of analysts following the company at the forecast date (eight months prior to

fiscal year-end);

BMt�1 the book-to-market ratio, measured as the book value of equity divided by its market

value, at the end of year t�1;

RETt the 12-month buy-and-hold return starting from eight months before a firm’s fiscal year-

end;

ARETt average earnings announcement return of four quarterly earnings announcements for year t.Quarterly earnings announcement return is defined as raw returns minus value-weighted

market returns over the three-day [�1, 1] period, where day 0 is the earnings

announcement date;

ACCt�1 total accruals in year t�1, measured as (DCA � DCash) � (DCL � DSTD � DTP) �DEPEXP scaled by average total assets, where DCA ¼ change in current assets, DCash¼ change in cash and cash equivalents, DCL ¼ change in current liabilities, DSTD ¼change in debt in current liabilities, and DEPEXP ¼ depreciation and amortization

expense;

FCt�1 financial constraint, measured as total debt minus cash and short-term investment scaled by

total assets, at the end of year t�1;

LEVt�1 the leverage ratio, measured as total debt divided by total assets, at the end of year t�1;

and

EBITDAt�1 the ratio of earnings before interest, taxes, depreciation, and amortization scaled by average

total assets, measured in year t�1.



APPENDIX B

Variable Measurement

Timeline of the Variables (using December Fiscal Year-End as an Example)

Estimating Net Operating Loss Carryback Capacity (NOLC)

NOLC is meant to capture tax paid in the earliest year of the NOL carryback window that has

not been refunded by the beginning of year t. We estimate net operating loss carryback capacity

(NOLC) depending on the time period of the loss. Before 1997, firms could carry back NOLs up to

three years and carry forward NOLs up to 15 years. Since 1997, firms can carry back NOLs up to

two years and carry forward NOLs up to 20 years.

The Post-1997 Period (Fiscal Year-End of August 1998 and Later)

Firms can carry back NOLs up to two years:

Our main test is whether firms have incentives to report larger losses in year t based on whether

the firm can carry back losses against income in the earliest carryback year (t�2). We define taxable

income (TI) as current tax expense divided by the top statutory tax rate. We calculate NOLC as

follows:

NOLC ¼ MAX 0; TIt�2 þMIN½0; TIt�1 þMAXð0; TIt�3Þ�f g: ðA1Þ

In words, A1 defines the capacity to carry back losses (NOLC) in year t as the amount of taxable

profits in t�2 that has not been used to claim a refund from tax losses in year t�1. If the firm did not

generate a taxable profit in t�2 (TIt�2 � 0), then there is no tax available to refund, and the outside

maximization function ensures that NOLC will simply equal 0. But if the firm has taxable profits in

t�2 (TIt�2 . 0), then we must first determine how much of those taxable profits in t�2 were offset

through tax losses in year t�1. This adjustment is given by MIN[0,TIt�1 þ MAX(0,TIt�3)] in

Equation (A1). If the firm has a taxable profit in year t�1, then this adjustment equals 0 and the full

amount of t�2 taxes paid is available for refund. If the firm had taxable losses in year t�1 (TIt�1 ,

0), then we must ask whether the firm had taxable income in t�3. A loss in year t�1 will first be

carried back against profits in year t�3, and the profits available to be offset by year t�1 losses are

presented by MAX(0,TIt�3). If the year t�1 loss exceeds the year t�3 income (if any), then the



excess loss is taken against t�2 income, and this is the adjustment given by MIN[0,TIt�1 þMAX(0,TIt�3)].

To illustrate the potential values of NOLC as a function of prior taxable profit and loss patterns,

consider the cases in the following table:

Case

TaxableProfits/Losses

in EarliestCarryback Year

TIt�2

TaxableProfit/Lossin Year t�1

TIt�1

Taxable Profitsin Year t�3

Available to OffsetYear t�1 Losses

MAX(0,TIt�3)

Amount of Yeart�1 Loss Not Covered

by Profits in t�3MIN[0,TIt�1

þ MAX(0,TIt�3)]

Max. Amountof Loss in Year

t that can beCarried Back

to Obtain Refundof t�2 Taxes

NOLC

(1) (2) (3) (4) (5)

1. 10 0 20 0 10

2. 5 5 10 0 5

3. 10 �10 10 0 10

4. 10 �20 10 �10 0

5. 10 �15 10 �5 5

6. �10 0 0 0 0

Cases 1 through 5 represent firms with taxes paid on profits in the earliest carryback year. Cases 1

and 2 represent firms with no tax loss in t�1 (and hence no offset of t�2 profits). Case 3 provides an

example of a firm whose tax loss in year t�1 was offset entirely against profits in year t�3,

preserving the entire capacity of t�2 profits to offset losses in t. Case 4 represents the case where a

loss in t consumed both t�3 and t�2 profits, while in Case 5, only a portion of the profits in t�2 are

used up to offset losses in t�1. The remainder in Case 5 is the amount available for use against

losses in t. The final case simply illustrates that firms that paid no taxes in t�2 (or reported a loss)

will have no capacity for carryback of t losses regardless of what happens in year t�1 or t�3.

The Pre-1997 Period

Firms can carry back NOLs up to three years.

Here, we calculate NOLC as follows:

NOLC ¼ MAX

�0; TIt�3 þMIN

h0; TIt�2 þMAXð0; TIt�5Þ þMAXð0; TIt�4Þ

i

þMINh0; TIt�1 þMAX

�0; TIt�4 þMIN

�0; TIt�2 þMAXð0; TIt�5Þ

��i�: ðA2Þ

Due to the dynamics involved with three carryback years, the formula is more complex. But the

intuition and results are similar as in A1. Numerical examples using this formula are available from

the authors upon request, but can be easily replicated.



D_NOL

D_NOL is a dummy variable that takes a value of 1 if NOLC is positive for a firm in an

expected loss position, and 0 otherwise. Firms with expected losses are those with negative

analysts’ consensus (median) forecasts made eight months prior to fiscal year-end (D_NEG ¼ 1).

Our NOLC (and hence D_NOL) measure is subject to a potential sources of measurement error.

First, taxable income includes both operating income/loss and capital gain/loss, which have

different carryback and carryforward rules. We do not make such a distinction because the

carryback periods are largely similar and because we can only estimate taxable income. Second,

measurement errors arise because the average tax rate is not equal to the top statutory tax rate owing

to progressive tax schedules. Third, current tax expense does not reflect the tax benefits from the

exercise of non-qualified employee stock options (reflected as a reduction in current tax liability)

and is reported net of tax cushions and tax credits (Hanlon 2003). Fourth, we do not incorporate the

magnitude of expected losses related to previous years’ profits, as analysts’ earnings forecasts are a

noisy proxy for a firm’s tax profits/losses. Incorporating the magnitude of expected losses would

make our NOLC formula too complicated. Fifth, firms were allowed to carryback NOLs for five

years in 2001/2002 and 2008/2009. We do not incorporate the five-year carry backs in our measure,

as the five-year carryback period was often not known until a later date. In addition, a five-year

carryback would make our measure extremely complicated. Finally, we do not incorporate the

information of net operating loss carryforwards on the balance sheet because of jurisdictional

differences and the impact of NOLs acquired in mergers and acquisitions. Moreover, research by

Mills et al. (2003) raises a number of additional concerns using Compustat data to infer net

operating loss positions. We hand-checked a number of cases and find that many firms were able to

claim tax refunds when reporting non-missing NOL carryforwards in their financial statements.

While we can certainly refine our NOLC measure along the directions mentioned above,

incorporating such issues would make our measure unduly complicated. We believe that such

measurement errors introduce noise to our measure and, if anything, are likely to bias against

finding any significant results. With that in mind, we prefer a relatively simple measure as used in

the paper.

Alternative Specifications of NOLC and D_NOL

We consider the following alternative specifications of NOLC and D_NOL. In the first

alternative specification, we replace expected losses proxied by analysts’ forecasts with those based

on a time-series earnings models in which we regress current year’s earnings (from Compustat) on

last year’s earnings and earnings in the year before. Then we use coefficient estimates based on

historical data and the most recent set of earnings data to calculate forecasted earnings for the next

year. D_NOL is equal to 1 if NOLC is positive and forecasted earnings are negative. In the second

alternative specification, we drop the requirement of expected losses. Rather, we require taxable

income to be positive in year t�2 and to be negative in year t�1 in the post-1997 period. In the pre-

1997 period, we require taxable income to be positive in year t�3 and to be negative in year t�2 and

t�1. In both cases, we require that tax paid in the first year of the NOL carryback window has not

been fully refunded. We find robust empirical results across these two alternative specifications

(results not tabulated).



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