Examining which tax rates investors use for equity valuation · which we estimate a statistically...

Examining which tax rates investors use for

equity valuation

Kathleen Powers

University of Texas at Austin

[email protected]

Jeri Seidman

University of Virginia

[email protected]

and

Bridget Stomberg

University of Georgia

[email protected]

May 2016

Acknowledgements: We thank John Campbell, Judson Caskey, Novia Chen (discussant), Michael Clement, Lisa De

Simone, Ross Jennings, Lisa Koonce, Lillian Mills, Casey Schwab, Brian White, the University of Arizona and the

University of Iowa tax readings groups, workshop participants at University of Georgia, the University of Texas at

Austin and the University of Waterloo, and participants at the 2014 AAA Annual Meeting for helpful suggestions.

Powers gratefully acknowledges financial support from the AICPA Foundation through the Accounting Doctoral

Scholars Program and the Red McCombs School of Business.

Page ii

Examining which tax rates investors use for equity valuation

ABSTRACT:

We propose that investors rely on tax rate heuristics to reduce information processing costs

associated with understanding complex income tax information and examine which information

investors use to impound income taxes into firm value. We find that tax expense estimated using

the top U.S. statutory rate is more associated with firm value than the firm’s prior-year effective

tax rate (ETR), prior three-year average ETR or prior-year industry-average ETR. However, we

find that investors rely less on the statutory tax rate when the benefits (costs) of doing so are higher

(lower). Investors incorporate industry-specific tax information more for firms with high future

tax planning opportunities. Additionally, investors incorporate firm- and industry-specific

information more when information processing costs are expected to be lower. Our findings

advance the literature regarding information processing costs, inform the valuation of tax literature

and have implications for management in communicating tax information to investors.

Keywords: Valuation, Tax expense, Stock returns

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

JEL classification: G12, M40, M41

I. Introduction

We examine which income tax information investors incorporate into firm value. Income

taxes are a material expense for U.S. corporations and, as such, should affect valuation. Yet the

complexity of the tax code and the rules that govern accounting for income taxes make it difficult

for investors to comprehend income tax disclosures and incorporate future tax outcomes into firm

value. Indeed, prior literature finds that sophisticated financial statement users struggle to impound

anticipated changes in tax expense into estimates of future performance (e.g., Chen and

Schoderbek 2000; Plumlee 2003; Weber 2009). Similarly, in concurrent work, Graham, Hanlon,

Shevlin and Shroff (2016) provide evidence that managers often use statutory tax rates when

making decisions despite having the skills and information necessary to more precisely estimate

tax effects.1 These findings suggest that the cost of processing tax information to develop refined

expectations may not outweigh the benefits for investors, analysts and managers when making

valuation decisions.

We propose that investors reduce their information processing costs by relying on

heuristics when impounding taxes into firm value (Payne 1976, 1982). Following Gigerenzer and

Gaissmaier (2011), we define a heuristic as “a strategy that ignores part of the information, with

the goal of making decisions more quickly, frugally and/or accurately than more complex

methods.” We identify four tax rates heuristics investors can use to estimate tax expense and

evaluate which measure is most associated with firm value, on average. The four heuristics are:

the firm’s prior-year GAAP effective tax rate (ETR), the firm’s three-year average GAAP ETR,

the average GAAP ETR of the firm’s industry, and the top U.S. corporate federal statutory rate of

1 On average, across seven types of corporate decisions including M&A and capital structure decisions, public (private) firm managers report using the following rates to incorporate taxes into forecasts or decision making processes: U.S. Statutory rate: 20% (34.1%); GAAP ETR: 27.4% (20.5%); Jurisdiction-specific statutory tax rate: 21.0% (14.6%); Jurisdiction specific ETR: 17.6% (15.0%); Marginal tax rate: 10.8% (12.5%).

35 percent. Each rate has strengths and weaknesses in estimating tax expense, and so we make no

prediction regarding which measures of tax expense are associated with firm value, on average.2

Our research methodology is similar in spirit to Francis, Schipper and Vincent (2003) who examine

the relative and incremental explanatory power of various earnings measures “to provide evidence

about aggregate investor behavior … for valuation.”

To determine if a measure of tax expense calculated using a tax rate heuristic is associated

with firm value, we estimate annual cross-sectional regressions of 12-month contemporaneous

buy-and-hold returns from 1996 through 2013 as a function of pre-tax earnings surprise, tax

surprise, and controls. The valuation of tax literature commonly assumes tax expense follows a

random walk similar to earnings (e.g., Ayers, Jiang and Laplante, 2009; Hanlon, Laplante and

Shevlin, 2005; Thomas and Zhang, 2014). These models therefore implicitly assume both the

GAAP ETR and pre-tax income follow a random walk (because tax expense is ETR multiplied by

pretax income). Consistent with this literature, we allow ETRs (and therefore tax expense) to

follow a random walk by including the difference between current and prior-year tax expense as a

measure of tax surprise (“PY tax surprise”). We also modify this model to include “other tax

surprise”, which represents the difference between prior year tax expense and expected current

period tax expense estimated using the remaining tax rate heuristics that we examine (i.e., firm’s

three-year average GAAP ETR, the firm’s industry average GAAP ETR and the U.S. top statutory

rate of 35 percent, respectively). This design allows us to determine whether other tax information

is incrementally informative to investors when impounding taxes into firm value.

2 Additionally, we acknowledge that the tax rates we examine are not an exhaustive set of tax information available to investors when making valuation decisions. It is therefore possible that our results reflect investors’ use of information that is highly correlated with, but different from, our chosen measures. For example, investors might use a 5-year firm-average ETR, which we expect would be highly correlated with our three-year measure.

A significant coefficient on PY tax surprise suggests investors use a random walk to value

current year tax expense. A significant coefficient on other tax surprise suggests tax information

other than that reported in the prior period is incrementally informative to investors when

impounding taxes into value. Following the methodology in Francis et al. (2003), we test the

relative information content of each model by: (1) counting the number of annual regressions for

which we estimate a statistically significant coefficient on PY or other tax surprise and (2) using a

Vuong (1989) test to count the number of years in which a particular model has the highest (is tied

with another model for the highest) adjusted R2.

On average, we estimate statistically significant coefficients on other tax surprise in nearly

every year for models using either the top U.S. statutory tax rate or the industry average rate to

estimate tax expense. The statutory model generates the largest adjusted R2 in as many as 18 of

the 18 years, depending on the specification, whereas the industry model generates the largest

adjusted R2 or is tied with the statutory model in at most four years. We therefore conclude that

investors most often impound taxes into firm value using the statutory tax rate, on average in a

broad sample of firms. This result compliments recent survey evidence by Graham et al. (2016),

as well as experimental evidence by Amberger, Eberhartinger, and Kasper (2016), that individuals

often use statutory tax rates instead of firm-specific tax information when making decisions.

Although the coefficient on firm average surprise is significant in as many as 16 of the 18 years,

the firm average model does not generate the largest adjusted R2 in any year. PY tax surprise also

does not generate the largest adjusted R2 in any year, even when we restrict the sample to

observations with historically low ETR volatility, where we expect prior-year information to be

most useful. Finding that the statutory model dominates the PY tax surprise model is potentially

surprising given that prior literature often specifies tax surprise relative to the prior-year and almost

never relative to the statutory rate. We further explore these results below.

The statutory rate is an extremely low cost heuristic. However, Dyreng, Hanlon, Maydew

and Thornock (2015) find that reported ETRs can be significantly lower than 35 percent, such that

if investors use the statutory tax rate to impound taxes, they may overestimate tax costs and

underestimate firm value. We conduct analyses to investigate whether investors’ reliance on the

statutory rate is reasonable and whether it changes when benefits (costs) of using a different

heuristic are higher (lower). First, we document that for the median profitable firm, ETR converges

to 35 percent after only four years, suggesting that the costs of using the statutory tax rate may not

outweigh the benefits of developing a more refined model.

We further test whether investors rely less on the statutory tax rate when the benefits of

incorporating additional information are presumably higher. We re-estimate our regressions on

subsamples of firms with potential opportunities for future tax avoidance evidenced by either high

levels of research and development (R&D) expenditures and foreign sales, or prior tax avoidance

(i.e., firms with historically low industry-adjusted ETRs). Greater potential opportunities for future

tax savings should result in larger errors if investors use the statutory rate to impound taxes. We

continue to find that the statutory model generates the highest number of statistically significant

coefficients and the highest explanatory power in the most number of years in these two

subsamples. However, in these subsamples we estimate an indistinguishable difference between

the explanatory power of the statutory and industry models in eight (seven) of 18 years. This

suggests that though investors continue to focus on the statutory tax rate when valuing taxes for

firms with greater opportunities for long-term tax planning (evidence of successful tax planning),

they incorporate industry-specific information more often than in the full sample of firms. We also

find some evidence of investors relying more on firm-specific information in this subsample

relative to the full sample.

Second, we examine whether investors use different tax rates when information processing

costs are lower. Though each of the heuristics is relatively easy to calculate, investors still must

devote time to understanding whether the heuristic yields a reasonable estimate of tax expense.

We measure information processing costs in two ways. First, we use the presence of analyst

coverage as an indication of a richer information environment and hence, lower investor

processing costs. Analysts can reduce investor processing costs by gathering, summarizing, and

interpreting a broader set of both firm- and industry- specific information for investors (Healy and

Palepu 2001). Second, we consider more sophisticated investors (i.e., institutional owners) to be

better able to process complex tax information (Blankenspoor 2015; Dye 1998; Fishman and

Hagerty 2003). We find evidence that investors incorporate firm- and industry-specific tax

information more when firms have active analyst coverage and when firms are in the top quintile

of institutional ownership. Specifically, we find that the industry model estimates a statistically

significant coefficient in more years than does the statutory model and that the industry model

generates the statistically highest R2 at least as frequently as does the statutory model.

Additionally, we find much more reliance on firm-specific tax information relative to the full

sample of firms. Taken together with the previous set of cross-sectional tests, we conclude that

investors incorporate firm- and industry-specific tax information into their valuation decisions

more when the benefits (costs) of doing so are higher (lower).

Our study complements and extends the literature on how capital market participants use

tax information (Ayers et al. 2009; Chen and Schoderbek 2000; Hanlon et al. 2005; Plumlee 2003;

Schmidt 2006; Thomas and Zhang 2014) by examining which tax information investors impound

into firm value. Whereas much prior literature assumes that investors use a firm’s prior-year tax

expense to set expectations about future taxes, our results suggest the U.S. statutory tax rate is

most associated with firm value. We also contribute to the literature that analyzes how various

stakeholders incorporate taxes into investment decisions. Our finding that the statutory tax rate is

most associated with firm value reveals that investors, as stakeholders outside the firm, use the

simplest heuristic to impound taxes. These results are consistent with survey evidence from

Graham et al. (2016) that corporate managers, as stakeholders inside the firm, often rely on the

statutory tax rate when making investment decisions. Finally, we contribute to the literature

documenting the effects of information processing costs on investors’ use of financial statement

information. Consistent with prior literature (Hong, Lim, and Stein 2000; Soffer and Lys 1999;

Walther 1997), we find that in a richer information environment, investors incorporate more

industry-specific information into firm value.

Our results also have several implications for managers who seek to understand how

investors use financial statement information and incorporate this information into price. Our

finding that investors rely heavily on the statutory tax rate, and that they tend to supplement with

the industry average rate when they seek additional tax information, implies they often ignore firm-

specific tax information when making valuation decisions. This finding is consistent with prior

research documenting investors’ limited attention for understanding and absorbing financial

statement information (Daniel, Hirshleifer, and Teoh 2002; Hirshleifer, Lim, and Teoh 2009). To

decrease investors’ information processing costs, managers can focus discussions on persistent

differences between the firm’s ETR and the statutory tax rate or industry average rate, which may

allow better assimilation of more relevant firm-specific information into price.

II. Background and Prior Literature

Overview

Income taxes are a material and recurring expense for most U.S. corporations and are

therefore an important component of firm value. From 1996 through 2013, the average (median)

profitable Compustat firm reported income tax expense equal to 25.4% (32.3%) of pre-tax income,

3.6% (2.7%) of sales, and 2.8% (2.4%) of market capitalization. In contrast, R&D, which is often

considered important for valuation purposes, is only 2.1% (0.0%) of sales and 1.4% (0.0%) of

market capitalization. Tax expense is also a larger percentage of sales than either interest expense

or advertising expense. 3 These statistics reveal not only that income taxes are of sufficient

magnitude to warrant investors’ consideration in valuation decisions but that they are perhaps one

of the most significant expenses to consider.

Prior studies provide evidence that income taxes are value relevant. Lev and Thiagarajan

(1993) identify income taxes as one firm “fundamental” that explains equity prices. However,

recent literature on tax-law and tax-disclosure changes demonstrates that income taxes are difficult

for even sophisticated users to comprehend. Chen and Schoderbek (2000) find that analysts failed

to properly adjust their earnings forecasts to include the effect of the statutory tax rate change from

the 1993 Omnibus Budget Reconciliation Act on the deferred tax accounts even though the

information required to estimate the one-time expense or benefit was available. Similarly, Plumlee

(2003) examines the tax-law changes resulting from the Tax Reform Act of 1986 and finds that

analysts incorporated the effect of less complicated tax law changes into their earnings forecast

3 Statistics in this paragraph are based on 91,585 Compustat observations from 1996 to 2013 where pre-tax income (PI), sales (SALE) and market capitalization (PRCC_F*CSHO) are all greater than 0. Research and development (XRD), advertising (XAD) and interest expense (XINT) are all set to 0 if missing. All variables are scaled by either sales or market capitalization and ratios are winsorized by year at one and 99 percent to avoid inflated averages resulting from small denominators.

but did not incorporate the effect of more complicated changes. Even when no tax law changes

occur, Bratten, Gleason, Larocque and Mills (2015) and Kim, Schmidt and Wentland (2015) find

that analysts struggle to properly incorporate tax information into earnings forecasts. Regarding

tax disclosure changes, Robinson, Stomberg and Towery (2015) find no evidence that investors

can identify firms with tax reserves that are most likely to be settled in cash based on tax reserve

disclosures.

Indeed, it remains unclear whether or when changes in tax expense are positively or

negatively associated with returns. Hanlon et al. (2005) document that taxable income estimated

from financial statement tax expense is positively related to returns and is incrementally

informative to pre-tax income in explaining returns. The authors posit that taxable income is an

alternative measure of firm performance. Building on the idea that income tax expense can serve

as a proxy for economic profitability, Ayers et al. (2009) find that investors rely more (less) on

taxable income as an alternative performance measure when earnings quality is low (tax planning

is high). However, Thomas and Zhang (2014) show that tax expense is informative about future

profitability only in model specifications that do not otherwise control for estimated future

performance. In samples where earnings surprises are small and in specifications that include

controls for expected future profitability, they find that income tax expense is valued as a cost that

represents value lost to tax authorities (i.e., is negatively associated with returns).

These results collectively suggest that the cost of processing firms’ income tax information

to develop a tax forecasting model is not trivial for the average investor. This assertion is further

supported by the fact that the majority of firms’ tax information is contained in footnote

disclosures, which have been shown in experimental studies to increase processing cost (e.g., Hirst

and Hopkins 1998). Thus, we propose that investors rely on heuristics to incorporate tax

information into firm value instead of trying to understand the drivers and characteristics of firms’

tax expense.

The psychology literature demonstrates that people rely on simple decision mechanisms to

help them process complex information. Payne (1976) finds that as task complexity increases,

participants resort to limited-information decision strategies that allow them to eliminate some

options as quickly as possible. One such simple decision mechanism is a heuristic, which is “a

strategy that ignores part of the information, with the goal of making decisions more quickly,

frugally, and/ or accurately than more complex methods” (Gigerenzer and Gaissmaier 2011). In

concurrent work, Graham et al. (2016) present evidence consistent with corporate managers using

statutory tax rates as a heuristic when evaluating investing, financing and operating decisions

despite the availability of relevant firm-specific information that would lead to more accurate

estimations. We examine which of four tax rate heuristics investors use to impound taxes into firm

value and, in doing so, identify which tax information investors may be ignoring in their valuation

decisions.

Tax rates as heuristics

We use four tax rates in our analysis, each of which meets the definition of a heuristic

because it allows the user to ignore some relevant information with the goal of making decisions

more quickly than more complex methods. The four rates are: (1) the highest corporate U.S.

statutory tax rate (Stat_Rate), (2) the firm’s prior-year ETR (PY_ETR), (3) the average of the firm’s

three prior annual ETRs (FirmAvg_ETR), and (4) the prior-year industry-average ETR

(IndAvg_ETR).4 All four rates are relatively easy for investors to obtain. We discuss our motivation

4 We limit the rates we examine to those that are available to the general public and acknowledge that our list is not exhaustive.

for selecting these rates below, beginning with the heuristic with the lowest processing cost and

ending with heuristics that are more difficult to calculate or to understand.

Stat_Rate, equal to 35 percent since 1993, is readily-available to investors because all

public corporations must reconcile their ETR to the U.S. corporate statutory tax rate in their income

tax footnote. The business press also frequently uses the U.S. statutory rate as a benchmark against

which to evaluate firms’ taxes. For example, in conjunction with Apple’s testimony before the

U.S. Senate, Bloomberg noted that the company’s “30.5 percent tax rate in the U.S. lags behind

the corporate tax rate of 35 percent” (Drucker 2013). Articles such as these make the statutory tax

rate very salient to investors and perhaps give the impression that this is the rate investors should

be using when evaluating corporate taxes. Evidence also suggests that analysts view reductions in

tax expense not caused by changes in the statutory tax rate as transitory (Abarbanell and Bushee

1997, 1998; Lev and Thiagarajan 1993), suggesting these deviations are down-weighted in

valuation decisions. Finally, the statutory tax rate is potentially a useful heuristic because many

tax reduction strategies are temporary in nature. Thus, benefits claimed in one period reverse in a

future period such that corporations will pay an average rate of 35 percent to the U.S. on all pre-

tax earnings over their lifetime, absent permanent tax reduction strategies and ignoring the time-

value-of-money.

However, using the statutory rate to estimate future tax outcomes ignores the fact that many

tax planning strategies allow significant deferral of tax payments. Schmidt (2006) notes that many

reductions in tax expense relative to the statutory tax rate reflect “long-term (and therefore

persistent) strategic tax planning.” These strategies include transfer pricing, tax-efficient supply

chain management and tax-favored intercompany debt structures. Therefore, although using the

statutory tax rate to impound taxes for valuation is low cost and may be valid over very long

periods, it ignores information about firm- or industry-specific opportunities to generate value-

relevant tax savings and may result in investors overestimating tax costs in the short term.

Using a firm-specific tax rate overcomes this limitation. Corporations must present

information about both their current and prior-year taxes in the income tax footnote of the annual

report and frequently discuss differences between the two in the MD&A. Prior-year tax expense

and pre-tax income are also presented on the face of the income statement, allowing investors to

easily calculate this ratio without relying on footnote disclosures. Thus, PY_ETR (calculated as

TXTt-1/PIt-1) is salient to investors and of relatively low cost to obtain. It also has the advantage of

containing the most recent information about firm-specific characteristics that contribute to a

firm’s ability and willingness to avoid tax.

Although PY_ETR has certain information advantages, it can also be noisy due to periodic

settlements with taxing authorities, significant one-time corporate transactions, and earnings

management through the tax accrual (Dhaliwal, Gleason and Mills 2004; Robinson et al. 2015).

Understanding whether a firm’s prior-year ETR is likely to generate a reasonable estimate of future

taxes is not costless because it requires investors to understand which components of tax expense

are persistent and which are transitory. Raedy, Seidman and Shackelford (2012) document the

difficulty inherent in understanding the income tax footnote through a collection of detailed

footnote data. The examples of rate reconciling items they provide demonstrate the inconsistent

language firms use to refer to similar underlying transactions. They further find that approximately

90 percent of the rate reconciliations they collect include a line entitled “Other,” “Miscellaneous,”

or a similarly vague description that gives no information on the underlying transactions (Raedy

et al. 2012). The authors state, “During the process of collecting, interpreting and categorizing the

information, we were repeatedly struck by the difficulty in understanding these data.” Therefore,

we expect even sophisticated investors experience difficulty in processing the information

underlying firms’ ETRs.

To gain a better understanding of which deviations from the statutory tax rate are persistent

rather than transitory and to reduce the noise in the one-year measure, market participants can

average tax expense over multiple periods to arrive at a firm-average ETR (e.g., Dyreng, Hanlon,

and Maydew 2008). This heuristic will reduce the effect of significant one-time deviations from

normal trends. However, because firms often provide information for only three years, calculating

a longer-horizon ETR will require effort on the part of investors. Long-run averages can also mask

informative tax volatility. Keeping with the spirit of a heuristic, we use only the information

included in a single annual report and calculate FirmAvg_ETR over a three-year window. Thus,

FirmAvg_ETR is the average ETR from t-3 through t-1 where ETR is defined as (TXT/PI).

Industry-wide tax rates can provide relevant information about a firm’s taxes because firms

within the same industry tend to have comparable income tax avoidance opportunities and are

oftentimes similarly affected by changes in tax legislation (e.g., Balakrishnan, Blouin, and Guay

2012; De Simone, Stomberg, and Mills 2015). Consistent with this notion, prior studies show that

analysts and investors frequently use industry-average performance to set expectations about and

evaluate firm-specific performance (Lev 1989). Industry-average tax rates can therefore provide

information about potential changes or trends that are not yet reflected in a particular firm’s tax

expense. However, this rate is more difficult for investors to calculate because it requires

cumulating information across multiple companies and knowing at what level to define an

industry.5 We include IndAvg_ETR to capture the average ratio of tax expense to pre-tax income

5 To be useful as a tax rate heuristic, an industry-average ETR must effectively combine firms with similar opportunities for income tax avoidance. Common industry definitions include one-, two-, or three-digit SIC codes, as well as other groupings based on 4-digit codes such as those provided by Fama and French. GCIS and NAICS codes

in a given industry and calculate IndAvg_ETR as the firm’s industry average ETR in year t-1 where

industry is defined using the Fama-French 30 industry classifications.

To summarize, we propose that investors weigh the relative advantages and disadvantages

of each of the tax rates discussed above when deciding how to impound taxes into firm value.

Therefore, it is not clear, ex ante, which calculation of tax expense is most associated with firm

value. Thus, we make no predictions about which rate(s) investors use and instead examine this

question empirically.

III. Research Method

Regression Specification

Much prior research examining the association between taxes and firm value assumes that

both pre-tax income and tax expense follow a random walk. These studies therefore measure

surprises using one-year changes in pre-tax income and tax expense (e.g., Lev and Thiagarajan

1993; Thomas and Zhang 2014).6 To determine whether other tax information is relevant to

investors when impounding taxes into price, we estimate the following specification using annual

cross-sectional regressions:

Retit =β0 +β1PY_Tax_Surpriseit + β2Other_Tax_Surpriseit +β3Income_Surpriseit +β4LogMVEit-1 +β5Retit-1 +β6BTMit-1 +εt (1)

where Retit is the buy-and-hold return to security i over a 12-month window beginning at the end

of the third month of year t and ending at the end of the third month of year t+1. Our approach is

motivated by Francis et al. (2003), who examine the association between long-window returns and

are also commonly used. For example, Lev (1989) classifies industry using two-digit SIC codes while Balakrishnan et al. (2012) and Dyreng et al. (2008) use the Fama and French 30 (FF30) classification. 6 Some studies use analysts’ consensus earnings forecasts as a proxy for investors’ expectations. However, forecasts of pre-tax earnings are not well populated in IBES. According to Mauler (2015), only 1,031 (3,500) pre-tax earnings forecasts are available in IBES in 2002 (2013). Thus, relying on IBES for pre-tax income forecasts would reduce our sample by up to 65 percent in some years. Our analysis focuses on the relative explanatory power of the models we test and holds the value of PI_Surprise constant across models. Thus, we have no reason to believe that our results depend on the measure of PI_Surprise we use. However, we explore analysts’ ETR estimates in section VI.

various measures of firm performance “to provide evidence about aggregate investor behavior.”

We follow prior studies that allow tax expense to follow a random walk and calculate

PY_Tax_Surpriseit as follows:

PY_Tax_Surpriseit = (TXTt – TXTt-1) / MVEt (2)

where MVE is the market value of equity three months after the end of year t. This definition of

PY_Tax_Surpriseit implicitly assumes ETRs follow a random walk and allows us to test the prior-

year effective tax rate (PY_ETR) as a heuristic investors use to value current period tax expense.

We calculate PY_Tax_Surpriseit such that if actual tax expense is lower than prior-year tax

expense, the value is negative. Prior studies document that investors view tax expense either as

representing value lost to tax authorities or as a proxy for future profitability (Ayers et al. 2009;

Thomas and Zhang 2014). Although an investigation of the differing roles of tax expense is not

the focus of this study, in our tests, a negative coefficient on PY_Tax_Surprise (β1 < 0) is consistent

with taxes being viewed as an expense, or value lost (e.g., Lipe 1986). Conversely, β1 > 0 is

consistent with taxes serving as a proxy for profitability in our sample. Because of these conflicting

suppositions, we make no prediction as to the sign of β1.7

We evaluate whether investors incorporate information other than prior-year tax expense

by including a second measure of tax surprise. Other_Tax_Surpriseit captures the difference

between prior-year tax expense and expected current year tax expense calculated by multiplying

each heuristic (other than PY_ETR) in turn, by PIt-1:

Other_Tax_Surpriseit = (TXTt-1 – TROTHER * PIt-1) / MVEt (3)

7 A potential concern is that because of these competing roles for tax expense, leading to opposite signs, a heuristic may be used for both roles leading to an insignificant coefficient, on average. To mitigate this concern, we follow Thomas and Zhang (2014) and present results both before and after trimming extreme values of PI_Surprise.

where TROTHER is Stat_Rate, FirmAvg_ETR or IndAvg_ETR. Other_Tax_Surpriseit represents the

difference between tax expense predicted using a random walk and tax expense predicted using

one of our three alternative tax rate heuristics. Thus, if prior-year tax expense is lower than tax

expense predicted using the heuristic, the value is negative. When we evaluate PY_ETR as a

heuristic, Other_Tax_Surprise equals zero. Estimating β2 < 0 is consistent with an increase in

expected taxes representing value lost while estimating β2 > 0 is consistent with increased tax

expense signifying future profitability. For all heuristics, the sum of PY_Tax_Surpriseit and

Other_Tax_Surpriseit captures the total difference between reported tax expense and expected tax

expense calculated using each of the benchmarks and PIt-1.

Estimating a significant coefficient on Tax_Surprise suggests that particular tax rate

heuristic contains information relevant for investors’ valuation. Following Francis et al. (2003),

we determine if one particular model includes a tax expense that is more closely aligned with the

information investors use to impound taxes into firm value than the other models in two ways.

First, we count the number of years in which the coefficient on each measure of Tax_Surprise is

statistically significant. A model with a higher number of years of coefficients different than zero

is considered more associated with firm value. Second, we use a Vuong (1989) z-statistic to test

whether the explanatory power of any model is significantly higher than the explanatory power of

all other models each year. We consider the model that dominates all others in the greatest number

of years to contain the calculation of tax expense investors most closely associate with firm value.

Insignificant differences in explanatory power between the models are consistent with no investor

preference for a particular tax rate, on average.

We include several controls in our model. To ensure our measure of tax surprise is not

simply picking up changes in profitability, we control for pre-tax income surprise (PI_Surprise)

as follows:

PI_Surpriseit = (PIt – PIt-1) / MVEt (4)

and expect β3 >0. Additionally, following Thomas and Zhang (2014), we include controls for other

determinants of observed returns including the natural log of the market value of equity and book-

to-market ratio at the end of year t-1, as well as returns for the prior-year’s 12-month period with

a one-month lag relative to Rett. Prior literature suggests returns to be decreasing in logged, lagged

market value (β4 <0) and in prior-year returns (β5< 0), and increasing in lagged book-to-market

ratio (β6 >0). Henceforth, we omit firm and year subscripts for simplicity.

Sample

We begin our sample by selecting all firm-years in the intersection of Compustat and CRSP

from 1993 through 2013 with data necessary to calculate the required variables. Consistent with

Thomas and Zhang (2014), we do not eliminate observations reporting pre-tax losses or

observations with extreme ETRs (i.e., less than zero or greater than one) but do winsorize all

variables except Ret at the top and bottom one percent.

We first estimate equation (1) on our full sample of winsorized observations. To assess

how observations with extreme income surprises affect our results, we also estimate equation (1)

on two subsamples where we trim observations based on PI_Surprise. Thomas and Zhang (2014)

argue that this step-wise approach strengthens the relation between pre-tax income and future

profitability, thereby allowing tax expense to better represent value lost to tax authorities and serve

less as a proxy-for-profitability. This approach also allows us to test whether investors use different

heuristics in situations where tax surprise might be heavily influenced by extreme changes in

profitability.

Table 1 details our sample selection process. We require observations to have sufficient

data to calculate each of the tax rate heuristics we examine so that differing results across model

specifications are not due to changes in sample composition. Because we want all observations in

our sample to account for income taxes consistent with ASC 740, we begin the calculation of

FirmAvg_ETR, which requires three years of data, in 1993. Thus, our regression period is 1996

through 2013. To calculate a meaningful industry-average ETR, we require each industry to have

at least 10 observations per year. Our final sample consists of 86,310 firm-year observations from

11,503 unique firms. Firms are in our sample for 7.5 years, on average.

[Insert Table 1 here.]

IV. Results

Descriptive Statistics

Panel A of Table 2 reports descriptive statistics for GAAP ETRs as well as for the tax rate

heuristics we test. We compute GAAP ETR (ETR) as total tax expense scaled by pre-tax income

(TXT/(PI)). The average (median) ETR for firms in our sample is 19.7 (29.2) percent. The U.S.

statutory rate is 35 percent for every year in the sample. The average values for the other ETR

heuristics are 20.4 percent for PY_ETR, 20.6 percent for FirmAvg_ETR, and 24.4 percent for

IndAvg_ETR.


We scale all of our tax and pre-tax earnings surprise variables by market value of equity

when we estimate regressions. We present descriptive statistics on scaled regression variables in

Panel C.8 For ease of interpretation, we report descriptive statistics for unscaled values (in $M) in

Panel B. The average unscaled value of PI_Surprise is $12.63M, indicating that firms report a

year-over-year increase in pre-tax earnings, on average. PI_Surprise is also positive at the median.

PY_Tax_Surprise is $3.36 on average, indicating that increases in pre-tax income generate

increases in total tax expense. Stat_Surprise is negative, on average, consistent with descriptive

statistics in Panel A showing that, on average, reported ETRs are less than 35 percent.

FirmAvg_Surprise and IndAvg_Surprise are both positive at the mean and median. Descriptive

statistics on the scalar, market value of equity, show that the average observation in our sample is

large, with market capitalization of over $2B. In Panel C, we report that the average (median)

annual buy-and-hold return is 18.7 (6.2) percent in our sample.

Table 3 provides Pearson and Spearman correlations among regression variables. Pearson

correlations are listed above the diagonal with Spearman correlations below. Most Tax_Surprise

variables are significantly correlated with two-tailed p-values ≤ 10 percent. However, untabulated

VIF scores indicate that multicollinearity is not a significant concern.


Main Analysis

Table 4 presents results of estimating equation (1) as annual cross-sectional regressions.

We present results in a stepwise manner by first estimating returns as a function of total net income

surprise. This model allows total net earnings to assume a random walk model for expected future

income. We then disaggregated the total change in net income and estimate equation (1) annually

substituting each of the four tax rate benchmarks, in turn, and present the average coefficients and

average adjusted R2.

8 Untabulated T-tests indicate that all measures of Tax_Surprise are statistically different.

Panel A presents results of estimating equation (1) on our full sample. Following Thomas

and Zhang (2014), we also trim the sample based on extreme values of PI_Surprise to increase the

likelihood that tax expense represents value lost to tax authorities and is not a proxy for

profitability. In Panel B, we report results after trimming the top and bottom five percent of

PI_Surprise, and Panel C presents results after trimming the top and bottom ten percent of

PI_Surprise. Because the models offer the highest explanatory power in Panel B, we focus our

discussion of results on this sample. We also use this as our baseline specification in all cross-

sectional tests in Section V.


In column (1) we estimate returns as a function of NI_Surprise. We estimate a positive

coefficient on NI_Surprise, as expected. The average R2 for this model appears relatively low and

does not increase appreciably in column (2) when we decompose NI_Surpise into its pre-tax

income and tax expense components. Thus, changes in current-year tax expense relative to prior-

year tax expense do not appear to contribute a large amount of explanatory power for returns. In

all columns of Panel B, the coefficient on PI_Surprise is positive and significant, and the sign and

magnitude of coefficients on control variables are as predicted. We estimate negative and

significant average coefficients on PY_Tax_Surprise in all four columns, which implies that firms

reporting decreases (increases) in tax expense have greater (lower) returns, all else equal.9

We test whether other tax information has explanatory power for returns in columns (3)

through (5), where we include Other_Tax_Surprise. In all three columns, we estimate negative

and significant average coefficients on Other_Tax_Surprise, indicating that information contained

9 Comparing across the three panels, though we estimate an insignificant coefficient on PY_Tax_Surprise in two of the four columns in Panel A, we estimate significantly negative coefficients on PY_Tax_Suprise in all four columns of both Panels B and C. This is consistent with tax expense taking a weaker role as a proxy for profitability when PI_Surprise is less extreme.

in the measures of tax expense calculated using other tax rate heuristics is incrementally

informative to one-year changes in tax expense when explaining cross-sectional variation in

returns.10 We tabulate the number of years that the coefficient of interest – PY_Tax_Surprise in

Column (2) and Other_Tax_Surprise in Columns (3) through (5) – is significant. The statutory

model and industry model produce a significant coefficient on Other_Tax_Surprise in 17 of the 18

years while the PY model and firm average model generate significant coefficients in only five

and ten of the 18 years, respectively. The average explanatory power of the statutory and industry

models also appears much larger than the other models. We test the relative explanatory power of

each model annually using Vuong (1989) tests and find that the statutory model produces the

largest R2 in 14 of the 18 years in our sample.11 The industry model produces the largest R2 in two

years and in the remaining two years, the explanatory power of the statutory model and the industry

model are statistically equivalent and significantly higher than either firm-specific model. Thus,

results in Table 4 suggest that tax expense estimated using the U.S. statutory rate is more associated

with firm value than that calculated using IndAvg_ETR. Additionally, tax expense calculated using

IndAvg_ETR is more associated with firm value than that calculated using either PY_ETR or

FirmAvg_ETR.

V. Cross-sectional Tests

Our results suggest tax expense calculated using either the statutory tax rate or an industry-

specific tax rate is more associated with firm value than tax expense calculated using firm-specific

tax rates. This result is surprising because prior literature (e.g., Ball and Watts 1972; Beaver 1970;

10 Further consistent with tax expense taking a weaker role as a proxy for profitability when PI_Surprise is less extreme, the coefficient on the various specifications of Other_Tax_Surprise generally becomes more negative across the panels as we trim observations with extreme pre-tax earnings surprises. 11 Comparing across the panels, the statutory model continues to yield the highest average adjusted R2 as we trim observations with more extreme PI_Surprise. However, the number of years in which the statutory model generates the highest annual adjusted R2 falls from all 18 years in Panel A to 14 and 11 years in Panels B and C, respectively.

Watts and Leftwich 1988) documents that net income follows a random walk or a random walk

with a drift. As such, tax researchers (e.g., Ayers et al. 2009; Hanlon et al. 2005; Thomas and

Zhang 2014) generally calculate tax surprise as the year-over-year change in tax expense, which

implicitly assumes that tax expense also follows a random walk. We address these potentially

surprising results in two ways. First, we provide descriptive statistics on long-run ETRs to see if

using the statutory tax rate to impound taxes into firm value appears reasonable, on average.

Second, we examine if investors incorporate firm- or industry-specific information more when the

expected benefits (costs) of doing so are higher (lower). Although using the statutory tax rate is

possibly the lowest cost way to impound taxes into firm value, the cost-benefit trade-off of using

this heuristic likely varies with firm and investor characteristics. Therefore, we expect the benefits

of incorporating firm- or industry-specific information are higher when firms have greater

potential opportunities for long-run tax avoidance. We expect the costs of incorporating firm- or

industry-specific information are lower when information processing costs are lower.

Time-trends in Long-Window ETRs

We first examine time-series trends in long-window ETRs for a broad sample of firms.

Figure 1 plots rolling ETRs over periods of one to 20 years. We calculate these ETRs as the sum

of total tax expense (TXT) scaled by the sum of pre-tax income (PI) over various time periods. We

restrict this analysis to observations where the sum of pre-tax income over each specified time

interval is positive to ease interpretation and mitigate the effects of transitory losses. We observe

that the median ETR averages 35 percent after four years and continues to average 35 percent for

the remainder of the windows we estimate. Untabulated results are consistent if we restrict the

sample to firms with sufficient data available to calculate a 20-year ETR, without regard to overall

profitability. It therefore appears that for the median profitable firm, 35 percent is a reasonable

estimation of long-run tax effects. This gives some comfort to results that suggest tax expense

estimated using the statutory rate is more associated with firm value than tax expense estimated

using an industry- or firm-specific effective tax rate.

[Insert Figure 1 here.]

Tax Planning

Dyreng et al. (2008) report that some firms sustain low cash ETRs for periods of up to ten

years. Thus, some firms are able to enhance firm value through strategic tax planning to a greater

extent than others. We therefore examine whether investors impound taxes into firm value using

different tax rate heuristics when firms have either demonstrated significant historical tax planning

or when firms have characteristics associated with opportunities for tax savings. Both a history of

tax avoidance and the availability of tax avoidance opportunities can indicate potential future tax

savings.

Our first proxy for potential future tax savings is an ex post measure based on ETR

realizations. Balakrishnan, Blouin and Guay (2012) propose using an industry- and size-adjusted

ETR to capture aggressive or unexpected tax avoidance. They posit that all else equal, firms in

similar industries and of similar size have similar tax planning opportunities. Thus, ETR

realizations lower than the industry-size average ETR indicate firms have undertaken additional

tax minimizing strategies in that year. To identify firms who have achieved more significant tax

savings, we follow Balakrishanan et al. (2012) and compute a three-year industry-size adjusted

GAAP ETR, which is the difference between the industry-size average ETR and firm’s ETR from

t-3 to t-. The measure is constructed so that positive values represent tax avoidance in excess of

industry-size peers. We consider observations in the top quintile of this adjusted ETR to have

greater long-term tax avoidance. A strength of this measure is that it does not require us to identify

specific tax minimizing strategies. However, a disadvantage is that we could misclassify firms

because GAAP ETRs reflect both real tax planning and financial accounting decisions. For

example, tax contingency reserves can mask the extent of tax avoidance.

To mitigate this disadvantage, we also identify firms with high levels of R&D and foreign

sales because these characteristics are associated with opportunities for long-term tax avoidance.

Claiming R&D tax credits permanently reduces taxes and therefore lowers a firm’s reported ETR.

Indeed, Dyreng et al. (2008) report that firms with the most significant long-run tax savings report

larger amounts of R&D expenditures than firms reporting only moderate savings. Similarly, an

extensive presence in low-tax foreign jurisdictions allows companies to minimize current taxes by

deferring U.S. tax on qualified foreign earnings. Firms can additionally reduce the incremental

U.S. taxes due upon repatriation of these earnings through strategic planning, such as tax-efficient

supply chains that locate high return activities in low-tax jurisdictions. Using the statutory tax rate

exclusively to impound taxes for these firms may therefore result in under-valuation. To identify

firms with greater opportunities for long-term tax savings, we independently rank firm-years into

quintiles of R&D expense and percent of foreign sales by year. For each ranking, we assign an

observation a score of one if it is in the lowest quintile and five if it is in the highest quintile. We

then sum the two ranks such that each observation can earn a score ranging from two to 10.

Observations in the top quintile of this composite score are deemed to have greater tax planning

opportunities.

We present the average coefficients and average adjusted R2 from estimating equation (1)

using these two subsamples in Table 5. For comparison purposes, Panel A repeats results from

Panel B of Table 4 where we trim the full sample at the top and bottom five percent of PI_Surprise.

Panel B of Table 5 presents results where the subsample is defined using industry-size adjusted

ETR, and Panel C presents results using the subsample defined using R&D and foreign sales. We

trim both subsamples at five and 95 percent based on PI_Surprise.


In Panel B, we estimate that the statutory model has the largest adjusted R2 in eight years

and is tied with another model for the highest adjusted R2 in eight years. Thus, it produces the

highest or one of the highest adjusted R2 in 17 of the 18 years in the sample. The industry model

has the largest adjusted R2 in only one year but is tied with another model for the highest adjusted

R2 in eight years. Thus, it produces the highest or one of the highest adjusted R2 in nine of the 18

years in the sample. In column (4), the FirmAvg_ETR model produces one of the highest adjusted

R2 in four years while the PY_ETR model in column (1) produces one of the highest adjusted R2

in only two years.12 In results estimated on the full sample of observations trimmed at five and 95

percent presented in Panel A, the statutory model had significantly greater explanatory power than

the industry model in twelve years whereas it has significantly greater explanatory power in only

eight years in Panel B. Results in Table 5 therefore suggest that investors in firms with greater

opportunities for potential future tax savings incorporate industry-specific tax information to a

greater degree than for the average firm. Results in Panel C are consistent.

Information Processing Costs

We next consider whether investors incorporate firm- and/or industry-specific information

more when information processing costs are lower. Information processing costs include the costs

of acquiring, evaluating and weighing information (Maines and McDaniel 2000). Presumably, one

of the main reasons investors rely on Stat_Rate is because it requires the least effort to acquire and

incorporate into valuation decisions. We posit that the cost of acquiring, evaluating and weighing

12 We confirm that in years where both firm-specific models are one of the most associated with firm value, they are also statistically indistinguishable from either the industry or statutory model, rather than simply from each other.

tax information is lower for investors of firms with active analyst following or high institutional

ownership. Analysts reduce investors’ information processing costs by gathering and synthesizing

information about peers (Healy and Palepu 2001). Therefore, we posit that investors of firms with

an active analyst following incorporate information other than the statutory rate when impounding

tax expense into firm value. We use the IBES database to identify observations with at least one

analyst forecast issued in the 30 days before the annual earnings announcement. Of our full sample,

24,471 firm-years have active analyst following.

We also propose information processing costs are lower for firms with a high proportion

of institutional investors. Sophisticated investors, such as institutional owners, have greater

attention and are better able to process financial statement disclosures (Dye 1998; Fishman and

Hagerty 2003). Institutional investors also serve a monitoring role and are associated with better

financial statement disclosures (Blankenspoor 2015; Bushee and Noe 2000; Healy, Hutton, and

Palepu 1999) thereby potentially reducing processing costs for less sophisticated investors as well.

Taken together, these findings suggest that when firms have a significant percentage of

institutional investors, the aggregate investor base may use additional firm- or industry-specific

tax information instead of relying only on Stat_Rate when impounding taxes into value. We gather

institutional holdings data from the Thomson Reuters database and consider firms with

institutional holdings in the top quintile by year to have high institutional ownership.

Table 6 presents the average coefficients and average adjusted R2 from estimating equation

(1) using the subsample of firms with an IBES analyst following (Panel B) and in the top quintile

of institutional ownership (Panel C). We continue to present the relevant results from Table 4 in

Panel A and trim both subsamples at five and 95 percent based on PI_Surprise. In panels B and C,

we find that the industry model yields the largest average adjusted R2 and generates a statistically

significant coefficient in more years than the other three models. In Panel B, the industry model

produces the highest or one of the highest adjusted R2 in 16 years; the statutory model in thirteen

years; the firm average model in ten years and PY model in nine years.13 We therefore conclude

that active analyst following allows investors to consider information other than the statutory tax

rate when impounding taxes into firm value. A similar pattern emerges in Panel C. Compared with

results estimated on the full sample (Panel A), all heuristics other than Stat_Rate are associated

with firm value in more years for subsamples of firms with analyst following (Panel B) or high

institutional ownership (Panel C). Thus, results in Table 6 suggest that lower information

processing costs allow investors to incorporate additional information when impounding taxes into

firm value and that they incorporate both industry- and firm-specific information.


VI. Additional Analysis and Robustness Tests

Analyst Forecasts of Tax Expense

Although we do not examine analyst forecasts in our main tests to maintain sample size,

we recognize that analysts play an important role in setting investors’ expectations. Therefore, in

this section, we evaluate analysts’ tax expense forecast as a heuristic. We use ETR forecasts

obtained from ValueLine as a fifth heuristic. Although analysts’ ETR forecasts can be implied

from IBES data, we use ValueLine data because they encompass a longer time-series than do

implied ETR forecast data from IBES, which are available only after 2002. Additionally, because

13 Results are qualitatively similar if we estimate equation (1) on the sample of observations with an analyst forecast anytime during the year or if we limit the sample to firms with an above-median number of analyst forecasts in the 30 days before the earnings announcement (median=2).

ValueLine analysts forecast an explicit ETR, values are less subject to unreasonable estimates

arising from data errors.14

In the sample of 14,884 observations with a ValueLine ETR forecast, ValueLine_ETR is

higher at both the mean and the median than all other heuristic rates.15 Though it is significantly

correlated with each of the other rates, ValueLine_ETR is most highly correlated with PY_ETR

(67.1%) and FirmAvg_ETR (61.2%). Thus, it appears that ValueLine analysts rely heavily on the

firms’ historical tax information when generating ETR forecasts. Table 7 presents the average

coefficients and average adjusted R2 from estimating equation (1) for four values of TROTHER in

this sample. We continue to trim the sample at five and 95 percent based on PI_Surprise. In this

sample, we estimate that the industry model generates either the highest adjusted R2 or one of the

highest adjusted R2 in 13 of the 14 years. No other model, including the ValueLine model,

generates a statistically highest R2 in any year. However, each of the other models generates one

of the highest adjusted R2 in at least eight of the 14 years. These results corroborate those in Panel

B of Table 6 that analyst following reduces information processing costs and contributes to

investors’ use of both firm- and tax-specific information when impounding taxes into firm value.

However, these results do not suggest investors rely exclusively on analysts’ ETR forecasts.


Alternative model specification

Inferences from results presented in Table 4 are robust to estimating a model that contains

only one value of Tax_Surprise and therefore does not decompose tax surprise into

14

For example, we find instances of implied ETR forecasts well outside of [0,1] that result from apparent errors in the input value of the pre-tax income forecast, which is the denominator of the implied ETR forecast. This is not an issue with Value Line data. 15 In this sample, the mean/median for each calculated rate are as follows: PY_ETR (0.314/0.350), FirmAvg_ETR

(0.314/0.349), IndAvg_ETR (0.252/0.212) and ValueLine_ETR (0.342/0.360).

PY_Tax_Surprise and Other_Tax_Surprise. We re-estimate results for the full sample measuring

Tax_Surprise as follows:

Tax_Surpriseit = (TXTt - TRHEURISTIC *PIt-1) / MVEt (5)

where TRHEURISTIC is PY_ETR, Stat_Rate ,FirmAvg_ETR or IndAvg_ETR.16

In Table 8, we present the average coefficients and average adjusted R2 from estimating

equation (1) replacing PY_Tax_Surprise and Other_Tax_Surprise with Tax_Surprise defined

above. Table 9 mimics the formatting of Table 4 in that Panel A presents results of estimated on

our full sample, Panel B repeats our analysis after trimming the sample at the top and bottom five

percent of PI_Surprise, and Panel C presents this analysis after trimming the top and bottom ten

percent of PI_Surprise. Consistent with results in Table 4, we continue to estimate that the

statutory model generates the largest or one of the largest adjusted R2 in the most number of years.

We further estimate that the industry model generates the largest or one of the largest adjusted R2

some of the time and that the PY model and the firm average model generate one of the largest

adjusted R2 only occasionally. Our primary inferences are thus unchanged when tests are estimated

using this alternative model specification.


VII. Conclusion

We examine which information investors use to impound taxes into firm value. Given the

complexity of processing tax information we posit investors rely on heuristics to reduce

information processing costs. We estimate four measures of tax expense based on four different

heuristics and examine which are associated with firm value. The four heuristics are: the firm’s

prior-year GAAP ETR, the top U.S. corporate statutory tax rate, the average of the firm’s prior

16 When TRHEURISTIC is defined as PY_ETR, Tax_Surprise equals PY_Tax_Surprise as calculated in our main tests.

three GAAP ETRs and the firm’s industry average GAAP ETR. We find that tax expense

calculated using the statutory tax rate is most associated with firm value for a broad sample of

firms. This result is consistent with investors using the lowest cost heuristic to reduce the high

information processing cost of understanding the tax consequences of firms’ operations. In support

of this conjecture, we find evidence that investors incorporate firm- and industry-specific tax

information more when the benefits (costs) of doing so are higher (lower). Specifically, we

estimate that investors also impound industry-specific tax information for firms with greater tax

planning and impound both industry- and firm-specific tax information when investors face lower

information processing costs.

This paper extends the literature that examines investor valuation of tax expense (Ayers et

al. 2009; Hanlon et al. 2005; Thomas and Zhang 2014) and contributes to an emerging literature

on how various stakeholders incorporate taxes into investment decisions (Graham et al. 2016). Our

finding that investors primarily impound the statutory tax rate into firm value should be of interest

to managers and standard setters because it suggests that investors often ignore or fail to understand

industry- and firm-specific tax information in financial statements. Standard setters, in particular,

may want to consider how the tax footnote could be simplified to lower investors’ information

acquisition costs. Our findings also highlight the importance of communicating with shareholders

about income taxes as investors’ inability to understand firms’ tax planning may have mispricing

implications.

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Figure 1: Time-series convergence in GAAP ETRs

Panel A: Graph of trends in ETR over long-windows

Panel B: Descriptive statistics on trends in ETR over long windows

ETR is the GAAP effective tax rate calculated as total tax expense scaled by pre-tax income in year t (TXT/PI). We estimate rolling long-window values by summing total tax expense and pre-tax income over periods from one to twenty years. The sample is all observations from Table 1 where the sum of pre-tax income over the specified window is positive.

0.000

0.050

0.100

0.150

0.200

0.250

0.300

0.350

0.400

0.450

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Time-series convergence of ETR

25th Pctl 50th Pctl 75th Pctl

No. years

ETR

averaged:

N 25th Pctl 50th Pctl 75th Pctl

No. years

ETR

averaged:

N 25th Pctl 50th Pctl 75th Pctl

1 50,993 0.202 0.339 0.385 11 8,814 0.288 0.355 0.391

2 40,209 0.214 0.341 0.386 12 6,964 0.291 0.355 0.39

3 34,028 0.227 0.344 0.387 13 5,317 0.294 0.355 0.391

4 29,162 0.24 0.347 0.388 14 3,933 0.295 0.354 0.392

5 25,119 0.25 0.349 0.389 15 2,967 0.297 0.355 0.395

6 21,577 0.259 0.351 0.39 16 2,186 0.298 0.356 0.397

7 18,437 0.266 0.352 0.391 17 1,539 0.296 0.353 0.396

8 15,622 0.273 0.353 0.391 18 1,033 0.293 0.351 0.394

9 13,102 0.279 0.354 0.391 19 649 0.289 0.349 0.391

10 10,856 0.284 0.354 0.391 20 368 0.285 0.346 0.387

Table 1: Sample Selection

Rett is the 12-month buy and hold return from the end of the third month of year t to the end of the third

month of year t+1. PIt (PIt-1) is pre-tax income (PI) in year t (year t-1). MVEt is the market value of equity

measured three months after the end of year t (PRC*SHROUT). BTMt-1 is the book value of equity at year t-

1 divided by the market value of equity at year t-1 (CEQt-1/ (PRCC_Ft-1*CSHOt-1)). ETRt is the GAAP

effective tax rate (TXT/PI) for year t. FirmAvg_ETRt-1 is the firm’s average ETR from year t-3 through year

t-1. IndAvg_ETRt-1 is the firm’s industry-average ETR in t-1 where industry is defined using the Fama-

French 30 industry classifications.

Compustat US Firm Year Observations from 1993-2013 with Ret t from CRSP 105,161

Less: Observations with missing PI t or PI t-1 (5,866)

Less: Observations missing MVE t (96)

Less: Observations missing BTM t-1 (198)

Less: Observations missing ETR t (12)

Less: Observations missing three consecutive years of data to calculate FirmAvg_ETR t-1 (12,429)

Less: Observations with insufficient industry observations to calculate IndAvg_ETR t-1 (250)

Sample 86,310

Table 2: Descriptive Statistics

Mean Std. Dev P25 Median P75

ETR t 0.197 0.366 0.000 0.292 0.371

Tax rate heuristics

PY_ETR 0.204 0.348 0.000 0.299 0.374

Stat_Rate 0.350 0.000 0.350 0.350 0.350

FirmAvg_ETR 0.206 0.328 0.004 0.287 0.370

IndAvg_ETR 0.244 0.656 0.124 0.194 0.277


PI_Surprise 12.63 265.9 -6.692 1.302 16.48

NI_Surprise 7.104 220.4 -6.479 0.794 12.20

PY_Tax_Surprise 3.364 76.79 -1.129 0.012 3.821

Other_Tax_Surprise

Stat_Surprise -3.874 62.73 -1.375 0.166 2.939

FirmAvg_Surprise 1.232 37.78 -0.376 0.000 0.701

IndAvg_Surprise 11.86 163.7 -0.130 1.316 8.995

Scalar

MVE t-1 2,396 7,358 59 265 1,252


PI_Surprise -0.043 0.641 -0.037 0.007 0.037

NI_Surprise -0.048 0.615 -0.033 0.004 0.027

PY_Tax_Surprise 0.034 0.190 -0.005 0.001 0.011

Other_Tax_Surprise

Stat_Surprise -0.003 0.109 -0.006 0.000 0.009FirmAvg_Surprise 0.007 0.066 -0.001 0.000 0.003IndAvg_Surprise 0.020 0.142 -0.001 0.007 0.021

Dependent Variable

Ret 0.188 0.975 -0.241 0.057 0.374

Control Variables

LogMVE t-1 5.627 2.064 4.087 5.528 7.039

Ret t-1 0.187 0.822 -0.233 0.062 0.382

BTM t-1 0.672 0.698 0.298 0.531 0.859

Panel C: Scaled regression variables

Panel A: ETRs and tax rate heuristics

Panel B: Unscaled variables of interest (in millions)

Table 2 (cont.): Descriptive Statistics

ETR is the GAAP effective tax rate (TXT/PI). PY_ETR is ETR in year t-1. Stat_Rate is the top U.S. corporate

tax rate of 35 percent. FirmAvg_ETR is the firm’s average ETR from year t-3 through year t-1. IndAvg_ETR is

the firm’s industry average ETR in year t-1, where industry is defined using the Fama-French 30 industry

classifications. PI_Surprise is the change in pre-tax income (PI) from year t-1 to t. NI_Surprise is the change in

net income (NI) from year t-1 to t. PY_Tax_Surprise is calculated as the difference between TXT in year t minus

PI in t-1 multiplied by PY_ETR. Other_Tax_Surprise is the difference between TXTt-1 and expected tax in year

t-1, where expected tax is calculated by multiplying PIt-1 by one of the other tax rate heuristics: Stat_Rate is the

top U.S. corporate statutory rate of 35 percent; FirmAvg_ETR is the firm’s average ETR from year t-3 through

year t-1; and IndAvg_ETR is the firm’s industry average ETR in year t-1, where industry is defined using the

Fama-French 30 industry classifications. In Panel C, all Surprise variables are scaled by MVE, which is the

market value of equity at the end of the third month of year t+1, from CRSP (PRC*SHROUT). Ret is the

12-month buy and hold return from the end of the third month of year t to the end of the third month of year t+1.

LogMVEt-1 is the lagged natural log of MVE; Rett-1 is the lagged value of Ret, and BTMt-1 is (CEQt-1/ (PRCC_Ft-

1*CSHOt-1)).

Table 3: Correlations

Ret is the 12-month buy and hold return from the end of the third month of year t to the end of the third month

of year t+1. PI_Surprise is the change in pre-tax income (PI) from year t-1 to year t. PY_Tax_Surprise is the

difference between total tax expense in year t (TXT) and tax expense in year t-1. Other_Tax_Surprise is the

difference between tax expense using a random walk (TXT in t-1) and expected tax expense in year t-1 calculated

as PIt-1 multiplied by one of the other tax rate heuristics: Stat_Rate is the top U.S. corporate statutory rate of 35

percent; FirmAvg_ETR is the firm’s average ETR from year t-3 through year t-1; and IndAvg_ETR is the firm’s

industry average ETR in year t-1, where industry is defined using the Fama-French 30 industry classifications.

All Surprise variables are scaled by MVE, which is the market value of equity at the end of the third month of

year t+1, from CRSP (PRC*SHROUT). LogMVEt-1 is the lagged natural log of MVE; Rett-1 is the lagged value

of Ret. BTMt-1 is (CEQt-1/ (PRCC_Ft-1*CSHOt-1)).Pairwise Pearson correlations are presented above, and

Spearman are presented below the diagonal. Correlations significant with a p-value of ≤ 0.10 are bold.

Variable (1) (2) (3) (4) (5) (6) (7) (8) (9)

(1) Ret 0.121 0.061 -0.075 -0.021 -0.064 -0.096 -0.100 -0.044

(2) PI_Surprise 0.268 0.444 0.138 0.000 -0.007 -0.001 0.022 -0.043

(3) PY_Tax_Surprise 0.167 0.574 -0.064 -0.149 -0.189 -0.005 0.031 -0.033

(4) Stat_Surprise -0.096 0.137 -0.100 0.379 0.677 -0.170 -0.126 0.013

(5) FirmAvg_Surprise -0.024 0.029 -0.112 0.295 0.317 -0.071 -0.062 0.010

(6) IndAvg_Surprise -0.169 -0.022 -0.246 0.610 0.227 -0.117 -0.075 0.001

(7) LogMVE t-1 0.013 -0.024 0.020 -0.232 -0.074 -0.086 0.136 -0.023

(8) Ret t-1 -0.034 0.073 0.125 -0.198 -0.048 -0.131 0.243 -0.042

(9) BTM t-1 -0.045 -0.009 -0.006 -0.075 -0.006 -0.036 -0.015 -0.034

Table 4: Results from estimating the effect of different tax surprise measures on Ret

Variables of interest

PY_Tax_Surprise 0.082 -0.517 *** -0.046 -0.469 ***

Other_Tax_Surprise -1.645 *** -0.908 *** -1.295 ***

Income_Surprise 0.424 *** 0.408 *** 0.636 *** 0.429 *** 0.487 ***

Adj R2

5.91% 6.01% 10.36% 6.33% 8.09%

7 18 16 17

0 (0) 18 (0) 0 (0) 0 (0)


PY_Tax_Surprise -0.568 *** -1.537 *** -0.741 *** -1.397 ***


Income_Surprise 1.529 *** 1.608 *** 1.933 *** 1.637 *** 1.724 ***

Adj R2

7.34% 7.73% 10.44% 7.82% 9.10%

5 17 10 17

0 (0) 14 (2) 0 (0) 2 (2)


PY_Tax_Surprise -1.006 *** -1.952 *** -1.243 *** -1.919 ***


Income_Surprise 2.208 *** 2.517 *** 2.813 *** 2.565 *** 2.640 ***

Adj R2

6.98% 7.62% 9.49% 7.70% 8.66%

6 16 9 16

0 (1) 11 (4) 0 (1) 3 (4)

No. years coefficient significant, of 18 years

No. years highest (No. years tied for highest), of 18 years

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



Panel C: Sample trimmed at 10% based on PI_Surprise (69,068 firm-year observations)

Tax Rate Heuristic =

NI_Surprise PY_ETR Stat_Rate FirmAvg_ETR IndAvg_ETR

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

Panel B: Sample trimmed at 5% based on PI_Surprise (77,698 firm-year observations)





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


Ret it =β 0 + β 1 PY_Tax_Surprise it + β 2 Other_Tax_Surprise it + β 3 PI_Surprise it + β k Controls it + ε t

Panel A: Full Sample (86,310 firm-year observations)


Table 4 (cont.): Results from estimating the effect of different tax surprise measures on Ret

Table 4 presents results from estimating annual cross-sectional regressions from 1996-2013. Average coefficients and average Adj. R2 are presented in all panels. Ret is the 12-month buy and hold return from the end of the third month of year t to the end of the third month of year t+1.

PY_Tax_Surprise is the difference between total tax expense in year t (TXT) and tax expense in year t-1. Other_Tax_Surprise is the difference between tax expense using a random walk (TXT in t-1) and expected tax expense in year t-1 calculated as PIt-1 multiplied by one of the other tax rate heuristics: Stat_Rate is the top U.S. corporate statutory rate of 35 percent; FirmAvg_ETR is the firm’s average ETR from year t-3 through year t-1; and IndAvg_ETR is the firm’s industry average ETR in year t-1, where industry is defined using the Fama-French 30 industry classifications. Income_Surprise is the change in net income (NI) from year t-1 to year t in column (1) and the change in pre-tax income (PI) in columns (2) - (5). All Surprise variables are scaled by MVE, which is the market value of equity at the end of the third month of year t+1, from CRSP (PRC*SHROUT). In all specifications, we include three untabulated control variables: LogMVEt-1 is the lagged natural log of MVE; Rett-1 is the lagged value of Ret, and BMt-1 is (CEQt-1/ (PRCC_Ft-1*CSHOt-1)). Number of years the coefficient is significant is the number of years in which the coefficient on PY_Tax_Surprise (Other_Tax_Surprise) is significant according to the Z2 statistic (Barth 1994). Number of years highest (Number of years tied) is the number of years the Adj. R2 is statistically higher than all other models (statistically equivalent to the model with the highest Adj. R2) according to a Vuong (1989) test. ***, ** and * represent two-tailed significance at 1%, 5% and 10% respectively.


Table 5 presents results from estimating annual cross-sectional regressions from 1996-2013 for subsamples

of firms with high tax planning. Panel A reproduces Panel B of Table 4 for comparison purposes. Panel B

presents results for the subsample of firms with an industry-size adjusted ETR in the top quintile of all

observations in year t (Balakrishanan et al. 2012), whereas Panel C presents results for the subsample of

firms with R&D expense and foreign sales in the top quintile of observations in year t. Average coefficients

and average Adj. R2 are presented in all panels. Ret is the 12-month buy and hold return from the end of

the third month of year t to the end of the third month of year t+1. PY_Tax_Surprise is the difference

between total tax expense (TXT) in year t and year t-1. Other_Tax_Surprise is the difference between

expected tax expense using a random walk (TXT in t-1) and expected tax expense in year t-1 calculated as

PIt-1 multiplied by one of the other tax rate heuristics: Stat_Rate is the top U.S. corporate statutory rate of

35 percent; FirmAvg_ETR is the firm’s average ETR from year t-3 through year t-1; and IndAvg_ETR is the

firm’s industry average ETR in year t-1, where


PY_Tax_Surprise -0.568 *** -1.537 *** -0.741 *** -1.397 ***


PI_Surprise 1.608 *** 1.933 *** 1.637 *** 1.724 ***

Adj R2

7.73% 10.44% 7.82% 9.10%

5 17 10 17

0 (0) 14 (2) 0 (0) 2 (2)


PY_Tax_Surprise -0.006 -1.440 *** -0.491 *** -1.077 ***


PI_Surprise 0.955 *** 1.337 *** 1.018 *** 1.161 ***

Adj R2

6.18% 8.93% 6.56% 8.00%

0 16 8 16

0 (2) 8 (9) 0 (4) 1 (8)


PY_Tax_Surprise -0.869 *** -2.499 *** -1.141 *** -2.302 ***

Other_Tax_Surprise -3.465 *** -1.008 -3.231 ***

PI_Surprise 1.804 *** 2.447 *** 1.855 *** 1.995 ***

Adj R2

8.51% 11.29% 8.62% 9.97%

2 18 3 13

0 (2) 9 (8) 0 (4) 1 (8)





IndAvg_ETR

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



Ret it =β 0 + β 1 PY_Tax_Surprise it + β 2 Other_Tax_Surprise it + β 3 PI_Surprise it + β k Controls it + ε t

Panel B: Firms with high tax planning identified using industry-size-adjusted ETR, sample trimmed at 5% based on PI_Surprise

(15,538 firm-year observations)

Panel C: Firms with high tax planning identified by R&D expense and foreign operations, sample trimmed at 5% based on

PI_Surprise (16,127 firm-year observations)

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

Panel A: Sample trimmed at 5% based on PI_Surprise (77,698 firm-year observations)


Stat_Rate

Stat_Rate FirmAvg_ETR

FirmAvg_ETR

PY_ETR

(1) (2) (3)


PY_ETR Stat_Rate FirmAvg_ETR IndAvg_ETR

(4)

IndAvg_ETR

PY_ETR


Table 5 (cont.): Results from estimating the effect of different tax surprise measures on Ret

industry is defined using the Fama-French 30 industry classifications. PI_Surprise is the change in pre-tax

income (PI) from year t-1 to t. All Surprise variables are scaled by MVE, which is the market value of

equity three months after the end of year t, from CRSP (PRC*SHROUT). In all specifications, we include

three untabulated control variables: LogMVEt-1 is the lagged natural log of MVE; Rett-1 is the lagged value

of Ret, and BTMt-1 is (CEQt-1/ (PRCC_Ft-1*CSHOt-1)). Number of years the coefficient is significant is the

number of years in which the coefficient on PY_Tax_Surprise (Other_Tax_Surprise) is significant

according to the Z2 statistic (Barth 1994). Number of years highest (number of years tied for highest) is the

number of years the Adj. R2 is statistically higher than all other models (statistically equivalent to the model

with the highest Adj. R2) according to a Vuong (1989) test. ***, ** and * represent two-tailed significance

at 1%, 5% and 10% respectively.


Table 6 presents results from estimating annual cross-sectional regressions from 1996-2013 for subsamples

of firms with lower information processing costs. Panel A reproduces Panel B of Table 4 for comparison

purposes. Panel B presents results for the subsample of firms with at least one analyst forecast in the 30

days preceding the earnings announcement, whereas Panel C presents results for the subsample of firms

with institutional ownership in the top quintile of observations in year t. Average coefficients and average

Adj. R2 are presented in all panels. Ret is the 12-month buy and hold return from the end of the third month

of year t to the end of the third month of year t+1. PY_Tax_Surprise is the difference between total tax

expense (TXT) in year t and year t-1. Other_Tax_Surprise is the difference between expected tax expense

using a random walk (TXT in t-1) and expected tax expense in year t-1 calculated as PIt-1 multiplied by one

of the other tax rate heuristics: Stat_Rate is the top U.S. corporate statutory rate of 35 percent;

FirmAvg_ETR is the firm’s average ETR from year t-3 through year t-1; and IndAvg_ETR is the firm’s

industry average ETR in year t-1, where industry is defined using the Fama-French 30 industry

classifications. PI_Surprise is the change in pre-tax income (PI) from year t-1 to t. All


PY_Tax_Surprise -0.568 *** -1.537 *** -0.741 *** -1.397 ***


PI_Surprise 1.608 *** 1.933 *** 1.637 *** 1.724 ***

Adj R2

7.73% 10.44% 7.82% 9.10%

5 17 10 17

0 (0) 14 (2) 0 (0) 2 (2)


PY_Tax_Surprise -0.818 ** -1.081 *** -1.027 *** -1.552 ***


PI_Surprise 2.184 *** 2.254 *** 2.235 *** 2.282 ***

Adj R2

9.92% 10.50% 9.99% 10.71%

5 9 4 11

0 (9) 2 (11) 0 (10) 5 (11)


PY_Tax_Surprise -0.319 -0.596 -0.519 -1.002 **

Other_Tax_Surprise -0.726 -0.874 *** -1.705 ***

PI_Surprise 1.694 *** 1.807 *** 1.729 *** 1.811 ***

Adj R2

8.62% 9.14% 8.69% 9.25%

1 7 2 9

0 (12) 1 (15) 0 (14) 1 (16)






Panel C: Firms with lower information processing costs, identified by high institutional ownership, sample trimmed at 5% based on

PI_Surprise (11,541 firm-year observations)


Panel B: Firms with lower information processing costs, identified by analyst following, sample trimmed at 5% based on PI_Surprise

(24,471 firm-year observations)

(1)

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


IndAvg_ETR

(2) (3) (4)

PY_ETR Stat_Rate FirmAvg_ETR

Ret it = β 0 + β 1 PY_Tax_Surprise it + β 2 Other_Tax_Surprise it + β 3 PI_Surprise it + β k Controls it + ε t


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

Panel A: Sample trimmed at 5% based on PI_Surprise (77,698 firm-year observations)




Table 6 (cont.): Results from estimating the effect of different tax surprise measures on Ret Surprise variables are scaled by MVE, which is the market value of equity three months after the end of

year t, from CRSP (PRC*SHROUT). In all specifications, we include three untabulated control variables:

LogMVEt-1 is the lagged natural log of MVE; Rett-1 is the lagged value of Ret, and BTMt-1 is (CEQt-1/

(PRCC_Ft-1*CSHOt-1)). Number of years the coefficient is significant is the number of years in which the

coefficient on PY_Tax_Surprise (Other_Tax_Surprise) is significant according to the Z2 statistic (Barth

1994). Number of years highest (number of years tied for highest) is the number of years the Adj. R2 is

statistically higher than all other models (statistically equivalent to the model with the highest Adj. R2)

according to a Vuong (1989) test. ***, ** and * represent two-tailed significance at 1%, 5% and 10%

respectively.


Table 7 presents results from estimating annual cross-sectional regressions from 1996-2013 for a subsample of firms with a ValueLine analyst ETR

forecast available. Average coefficients and average Adj. R2 are presented in all panels. Ret is the 12-month buy and hold return from the end of the

third month of year t to the end of the third month of year t+1. PY_Tax_Surprise is the difference between total tax expense (TXT) in year t and year

t-1. Other_Tax_Surprise is the difference between expected tax expense using a random walk (TXT in t-1) and expected tax expense in year t-1

calculated as PIt-1 multiplied by one of the other tax rate heuristics: Stat_Rate is the top U.S. corporate statutory rate of 35 percent; FirmAvg_ETR is

the firm’s average ETR from year t-3 through year t-1; IndAvg_ETR is the firm’s industry average ETR in year t-1, where industry is defined using

the Fama-French 30 industry classifications; and VL_ETR is the median GAAP ETR estimated by the ValueLine Analyst in year t-1. PI_Surprise is

the change in pre-tax income (PI) from year t-1 to t. All Surprise variables are scaled by MVE, which is the market value of equity three months

after the end of year t, from CRSP (PRC*SHROUT). In all specifications, we include three untabulated control variables: LogMVEt-1 is the lagged

natural log of MVE; Rett-1 is the lagged value of Ret, and BTMt-1 is (CEQt-1/ (PRCC_Ft-1*CSHOt-1)). Number of years the coefficient is significant is

the number of years in which the coefficient on PY_Tax_Surprise (Other_Tax_Surprise) is significant according to the Z2 statistic (Barth 1994).

Number of years highest (number of years tied for highest) is the number of years the Adj. R2 is statistically higher than all other models (statistically

equivalent to the model with the highest Adj. R2) according to a Vuong (1989) test. ***, ** and * represent two-tailed significance at 1%, 5% and

10% respectively.

Firms with a ValueLine forecast available, sample trimmed at 5% based on PI_Surprise (14,884 firm-year observations)


PY_Tax_Surprise 0.021 -0.247 0.044 -0.579 0.405

Other_Tax_Surprise -1.464 -0.693 -1.488 *** 0.277

PI_Surprise 2.017 *** 2.114 *** 2.010 *** 2.105 *** 1.915 ***

Adj R2

10.74% 11.02% 10.86% 11.30% 11.04%

4 5 3 8 3

0 (8) 0 (9) 0 (9) 3 (10) 0 (9)

Ret it = β 0 + β 1 PY_Tax_Surprise it + β 2 Other_Tax_Surprise it + β 3 PI_Surprise it + β k Controls it + ε t

PY_ETR Stat_Rate FirmAvg_ETR IndAvg_ETR VL_Surprise

(5)


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



Table 8: Results from estimating the effect of different Tax_Surprise measures on Ret

Table 8 presents results from estimating annual cross-sectional regressions from 1996-2013. Average

coefficients and average Adj. R2 are presented in all panels. Ret is the 12-month buy and hold return from

the end of the third month of year t to the end of the third month of year t+1. Tax_Surprise is the difference

between total tax expense (TXT) in year t and expected tax expense in year t-1 where expected tax expense

is calculated as PIt-1 multiplied by each tax rate heuristic: PY_ETR is ETR in year t-1; Stat_Rate is the top

U.S. corporate statutory rate of 35 percent; FirmAvg_ETR is the firm’s average ETR from year t-3 through

year t-1; and IndAvg_ETR is the firm’s industry average ETR in year t-1, where industry is defined using

the Fama-French 30 industry classifications. Income_Surprise is the change in net income (NI) from year

t-1 to t in column (1) and the change in pre-tax income (PI) from year t-1 to t in columns (2) - (5). All

Surprise variables are scaled by MVE, which is the market value of equity three months after the end of

year t, from CRSP (PRC*SHROUT). In all specifications, we include three untabulated control variables:

LogMVEt-1 is the lagged natural log of MVE; Rett-1 is the lagged value of Ret, and BTMt-1 is (CEQt-1/

(PRCC_Ft-1*CSHOt-1)). Number of years the coefficient is significant is the number of years in which the

coefficient on Tax_Surprise is significant according to the Z2 statistic (Barth 1994). Number of years

highest (number of years tied for highest) is the number of years the Adj. R2 is statistically higher than all

other models (statistically equivalent to the model with the highest Adj. R2) according to a Vuong (1989)

test. ***, ** and * represent two-tailed significance at 1%, 5% and 10% respectively.


Tax_Surprise 0.082 -1.293 *** -0.302 *** -0.916 ***

Income_Surprise 0.424 *** 0.408 *** 0.681 *** 0.447 *** 0.515 ***

Adj R2

5.95% 6.01% 9.64% 6.13% 7.58%

7 18 12 17

0 (3) 12 (6) 0 (3) 0 (4)


Tax_Surprise -0.568 *** -2.268 *** -0.737 *** -1.672 ***

Income_Surprise 1.529 *** 1.608 *** 2.047 *** 1.639 *** 1.756 ***

Adj R2

7.34% 7.73% 10.16% 7.81% 8.89%

5 18 13 17

0 (0) 13 (3) 0 (0) 2 (3)


Tax_Surprise -1.006 *** -2.394 *** -1.076 *** -1.952 ***

Income_Surprise 2.208 *** 2.517 *** 2.905 *** 2.533 *** 2.638 ***

Adj R2

6.98% 7.62% 9.39% 7.70% 8.60%

6 17 12 17

0 (1) 11 (4) 0 (2) 3 (4)



Panel C: Sample trimmed at 10% based on PI_Surprise (69,068 firm-year observations)

Panel B: Sample trimmed at 5% based on PI_Surprise (77,698 firm-year observations)

Panel A: Full-Sample, Winsorized at 1% (86,310 firm-year observations)






FirmAvg_ETR IndAvg_ETR

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

Stat_RatePY_ETR

(1)

(4) (5)(1)

Ret it = β0 + β1Tax_Surprise it + β2PI_Surprise it + βkControls it + εt

(2) (4) (5)

NI_Surprise

(1) (3)


Stat_Rate

Stat_RatePY_ETR FirmAvg_ETR IndAvg_ETR

PY_ETR FirmAvg_ETR IndAvg_ETRNI_Surprise


NI_Surprise

(3)(2)

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