Auditor-provided Tax Services and Stock Price Crash Risk

8/19/2019 Auditor-provided Tax Services and Stock Price Crash Risk

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Auditor-provided Tax Services and Stock Price Crash Risk

Ahsan HabibSchool of Accountancy

MASSEY UNIVERSITYPRIVATE BAG 102904

AUCKLAND NEW ZEALAND

Email : [email protected]

Abstract: We examine whether auditor-provided tax services affect stock price crash risk, animportant consideration for stock investors. Provision of tax services by incumbent auditorscould accentuate or attenuate crash risk depending on whether such services give rise toknowledge spillover or impair auditor independence. We investigate two channels though whichtax services might affect crash risk, earnings management in tax expenses; and tax avoidance.We also examine whether the association between tax services and crash risk is moderated bydifferent business strategies pursued by organizations. We use a two-stage model to control for

potential endogeneity inherent in the selection of auditors for tax services. Our findings revealthat auditor-provided tax services attenuate crash risk by constraining earnings management intax expenses and tax avoidance. We also find that auditor-provided tax services reduce crash riskfor firms following innovator business strategies. Taken together empirical findings reported inthis study support knowledge spillover i e insights gained from tax services can enhance audit

mailto:[email protected]:[email protected]:[email protected]:[email protected]


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this study support knowledge spillover i e insights gained from tax services can enhance audit

1. Introduction

This paper examines empirically the effect of auditor-provided non-audit tax services (NATS) on

stock price crash risk and the mechanisms through which the relationship, if any, manifests itself.

Also examined whether tax service-induced crash risk varies according to different business

strategies pursued by firms. The study is motivated by a long-standing debate in the history of

auditing as to whether external auditors should provide non-audit services (NASs) to clients and

recent regulatory ban on auditors’ providing a variety of NASs excluding the provision of

NATS.

There are competing arguments regarding the implications of NASs for audit quality.

Opponents of NASs argue that auditor independence and consequently audit quality is impaired

when auditors also provide NASs. This idea was formally developed by DeAngelo (1981) who

relates auditor independence with client- specific future quasi rents defined as “... the excess of

revenues over avoidable costs, including the opportunity cost of auditing the next-best

alternative client” (italics in original) (p.116). She develops a two-dimensional definition of

audit quality that sets the standard for addressing auditor independence issue. An auditor will be


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appearance (Lindberg and Beck, 2004). Proponents of auditor-provided NASs, however, argue

that, NASs provide considerable economies of scope. These economies of scope are broadly

categorized into knowledge spillover benefits (benefits from transferring information and

knowledge), and contractual economies (making better use of assets and/or safeguards already

developed when contracting and ensuring quality in auditing) (Simunic, 1984; Beck, Frecka and

Solomon, 1988; Arrunada, 1999).

Regulatory response to NASs, at least in the USA, takes side on the impairment of

auditor independence argument. This response can be attributed to independence concern for big

audit firm’s arising from the provision of NASs to existing clients which, among other factors,

has been identified to one of the reason for massive corporate collapses experienced by US

economy in the beginning of this century. Congress ratified the Sarbanes-Oxley Act (SOX) in

2002; Section 201 prohibits auditors from providing a variety of NASs for their audit clients.

However, the SEC determined that it would not prohibit auditor-provided NATS, a specific type

of NAS that auditors frequently provide to their audit clients. However significant restrictions

have been imposed on the provision of such services. In particular, (a) all auditor-provided

NATS are to be specifically preapproved by the client’s audit committee; (b) fees paid to


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For example, auditor-provided NATS reduce incidence of financial restatements (Kinney,

Palmrose, and Scholz, 2004) as well as tax-related restatements (Seetharaman, Sun, and Wang,

2011). Paterson and Valencia (2011) confirm Kinney et al. (2004) result but only for recurring

NATS. Nonrecurring tax engagements, on the other hand, increases the probability of future

restatements (a threat to auditor independence). Also consistent with knowledge spillover

argument, Omer, Bedard, and Falsetta (2006) and Krishnan and Visvanathan (2011) find a

negative association between auditor-provided NATS and discretionary accruals. Gleason and

Mills (2011) find that companies purchasing tax services from their auditors do not manage tax

reserves to meet/beat analyst forecasts more than other companies.

These studies, however, did not examine a more extreme form of adverse outcome

having direct economic consequences for investors, stock price crash risk. Stock price crash risk

at the firm level refers to the likelihood of observing extreme negative values in the distribution

of firm-specific returns after adjusting for the return portions that co-moves with common factors

(Jin and Myers, 2006; Kim et al., 2011a; 2011b). Conceptually, stock price crash risk is premised

on the notion that managers have a tendency to withhold bad news for an extended period of

time, allowing bad news to stockpile. When the accumulation of bad news passes a threshold, it


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averse to stocks with greater crash risk because of the undiversifiable nature of those stocks,

determining whether auditor-provided NATS mitigate (exacerbate) crash risk by deterring

(aiding) managerial bad news hoarding behavior, would help investors better allocate their

resources.

In a recent empirical study, Kim, Li, and Zhang (2011) document a positive association

between corporate tax avoidance and future crash risk. They argue that “ Tax avoidance

activities can create opportunities for managers to pursue activities that are designed to hide bad

news and mislead investors…Perhaps more importantly, managers are able to justify the opacity

of tax avoidance transactions by claiming that complexity and obfuscation are necessary to

minimize the risk of tax avoidance arrangements being detected by the [taxing authorities]… To

some extent, these avoidance activities are shielded from the investigations of audit committees

and external auditors ” (p. 640, italics added). Although Kim et al. (2011) considers external

auditors’ monitoring with respect to clients’ tax avoidance to be lacking, we argue that strength

of the relationship between tax avoidance and crash risk depends on auditor-provided NATS.

However, competing arguments exist as to whether auditor-provided NATS impairs

auditor independence or generates spillover financial reporting quality benefits; i.e., insight


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been documented before. We consider strategy-tax avoidance link as Higgins, Omer, and Philip

(2013) show that innovators engage more in tax avoidance than their defender counterparts.

We contribute to the literature in a number of ways. First, we provide evidence on the

desirability of allowing, further constraining, or banning auditor-provided NATS (PCAOB

2004). We do that by evaluating the extent to which provision of tax services increase or

decrease crash risk, a more acute form of outcomes having direct economic consequences for

investors. Unlike other studies that directly examine the effect of auditor-provided NATS on

earnings quality, we consider the joint effect of NATS and earnings quality on crash risk.

Second, we extend the growing literature on the determents of crash risk by investigating the

effect of external auditors, an essential governance mechanism, on crash risk. Since external

auditors are directly responsible for verifying the authenticity of tax-related transactions reported

in financial statements, it is logical to assume a dominant role played by auditors. One exception

is Robin and Zhang (forthcoming) who find that industry specialist auditors reduce future crash

risk arguing that industry specialist auditors have the expertise to reduce managerial propensity

to hoard bad news. Our study differs from Robin and Zhang because of our focus on auditor-

provided NATS and the effect tax expense-related earnings management moderating the


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provides sample selection procedure and descriptive statistics. Main test results are reported in

section 5. Final section concludes the paper.

2. Review of related literature and development of hypotheses

Auditor-provided NASs has and still remains a matter of serious regulatory and investor concern.

While all fees create economic bonds between the auditor and client, debate has typically alleged

that the provision of NAS provides incentives for audit firms to accept clients ’ questionable

accounting choices, thus reducing auditor independence and ultimately the quality of financial

reporting. The provision of NAS has been the focus of most recent attention, presumably

reflecting the widely accepted view that NAS typically is provided at a higher profit margin than

audit services (Ruddock and Taylor, 2005). Such concerns prompted the SEC to prohibit most

types of NASs in Section 201 of the SOX-2002 act. However, SEC allowed auditor-provided

NATS subject to the pre-approval by the audit committees. 3

Auditors evaluate the validity of accrued taxes payable and tax contingent liabilities on

the balance sheet, income tax expense on the income statement, and the related note disclosures


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auditors also have to assess whether their clients engage in potentially abusive tax transactions.

Whether auditor-provided NATS help auditors discover these risky tax transactions (knowledge

spillover) or impair independence by aiding clients develop abusive taxation strategies

(impairment of independence) has become an issue of immense importance for regulators and

investor community alike.

In a study of restatements involving GAAP violations, Kinney et al. (2004) find a

negative association between restatements of financial statements and tax services but

Seetharaman et al. (2009) fail to find any such evidence. However, they find a significant

negative association between auditor-provided tax services and tax-related restatements,

consistent with knowledge spillover. Further evidence in support of knowledge-spillover benefits

from NATS has been documented for auditors’ propensity to issue going -concern opinions

(Robinson, 2008); adequate tax reserves being maintained by firms procuring tax services from

incumbent auditors (Gleason and Mills, 2010); lower debt pricing costs for clients procuring tax

service from the same auditor (Fortin and Pitman, 2008); and lower earnings management in tax

expenses for firms with auditor providing NATS (Lisic, 2014). Krishnan and Visvanathn (2011)

also find support for auditor-provided tax services constraining earnings management but their


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services help clients to reduce 4 th quarter ETR, thereby, increasing earnings management. Harris

and Zhou (2013) find that auditor-provided NATS reduces non-tax internal control weaknesses

but not tax-related internal control weaknesses.

Since the decision to use incumbent auditor for tax services is endogenously determined,

some studies examine the rationale for procuring tax services from incumbent auditors. Albring

et al. (2009) find that corporate governance attributes, such as board independence, the audit

committee’s accounting expertise, and separation of the CEO and the chairman of the board, play

a role in a firm’s decision to switch to a nonauditor provider for tax services. Similar to Albring

et al. (2009), Lassila et al. (2010) provide evidence that firms with strong corporate governance

and relatively high levels of tax and operational complexity are more likely to retain their auditor

for tax services.

In a recent empirical work Kim et al. (2011) find convincing evidence that tax avoidance

increases future stock price crash risk. Conceptually, stock price crash risk is premised on the

notion that managers have a tendency to withhold bad news for an extended period of time,

allowing bad news to stockpile. When the accumulation of bad news passes a threshold, it is

revealed to the market at once, leading to a large negative drop in price for the stock (Jin and


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incumbent auditors providing tax services are better able to differentiate between value-

enhancing versus value-destroying tax planning stratgeies. Firms that engage in tax

aggressiveness have a higher chance of misstatements and restatements because managers can

use various accounts such as valuation allowances, tax contingency reserves, and estimates of

accrued taxes to manipulate earnings (e.g., Hanlon and Heitzman 2010; Frank and Rego 2006;

Gupta et al 2011; Dhaliwal et al. 2004). Aggressive tax positions involve complex and risky

techniques, which require additional research, specialized audit procedures, documentation, and

consultations with tax professionals to audit (Donohoe and Knechel 2013). 4

Audit firms providing tax services to their clients would package not only their

reputation, or technical expertise in the provision of such services but would also make the most

of their unique ability as auditors to leverage these benefits into higher financial reporting quality

(Seetharaman et al., 2013). .Insights gained from performing tax services will enable auditors to

be more intimate about clients’ strategic decisions regarding tax planning which benefits the

auditors in uncovering tax expense-related earnings management and tax avoidance policies.

That familiarity facilitates management’s consideration of the financial reporting implications of

various alternatives, resulting in more certainty in terms of the financial reporting aspects of the


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and defenders at the other. Prospectors rapidly change their product market mix to be innovative

market leaders, while defenders focus more on a narrow and stable product base to compete on

the basis of price, service, or quality. Firms that constitute the middle of the continuum are

analyzers possessing the attributes of both prospectors and defenders (Miles and Snow 1978,

2003). Prior research on organization theory has demonstrated that prospectors have a higher

level of outcome uncertainty, plagued with more information asymmetry, and structure executive

compensation that is primarily long-term and incentive-based. Following this Higgins et al.

(2013) document an increasing propensity for innovators to engage in more tax avoidance

compared to their defender counterparts. This finding is explained by the fact that f irm strategies

are, in part, based on firms’ willingness to deal with risk and uncertainty with prospectors being

subject to more uncertainty and hence requiring more tax planning. Habib and Hasan (2014)

provide empirical evidence that prospectors are more prone to crash. This two streams of

literature therefore suggests that tax avoidance propensities of prospectors may contribute to

future crash risk. Auditor-provided NATS may strengthen (weaken) this relationship dependingon whether independence impairment or knowledge spillover argument dominates. Our second

hypothesis is as follows:


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the market model. This ensures that our crash risk measures reflect firm-specific factors rather

than broad market movements. Specifically, we estimate the following expanded market model

regression:

)1.......(,,,,, ,2,51,4,31,22,1, jm jm jm jm jm j j j r r r r r r

Where r, j,τ is the return of firm j in week τ , and r m,τ is the return on CRSP value-weighted market

return in week τ . The lead and lag terms for the market index return is included to allow for

nonsynchronous trading (Dimson, 1979). The firm-specific weekly return for firm j in week τ (W

j,τ ) is calculated as the natural logarithm of one plus the residual return from Eq. (1) above. In

estimating equation (1), each firm-year is required to have at least 26 weekly stock returns . Our

first measure of crash risk is the negative conditional skewness of firm-specific weekly returns

over the fiscal year ( NCSKEW ). NCSKEW is calculated by taking the negative of the third

moment of firm-specific weekly returns for each year and normalizing it by the standard

deviation of firm-specific weekly returns raised to the third power. Specifically, for each firm j in

year τ , NCSKEW is calculated as:


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Up

j Down

d ju j wnwn DUVOL ,2

,2

, )1/()1(log ……………….(3)

A higher value of DUVOL indicates greater crash risk. As suggested in Chen et al. (2001),

DUVOL does not involve third moments, and hence is less likely to be overly influenced by

extreme weekly returns.

3.2 Earnings management

Earnings management for this study is proxied by earnings management in tax expense

following Dhaliwal et al. (2004). Specifically, we compare changes in 4th

quarter ETR from 3rd

quarter ETR to discern the presence of earnings management. A more negative difference

implies higher earnings management in tax expenses. ETR is defined as accumulated tax

expense (Compustat quarterly #6) divided by accumulated pre-tax income (quarterly #23). We

believe ETR-based earnings management proxy is more relevant for our study as we are

interested in auditors’ moni toring role over tax-based earnings management practices. We

include discretionary accruals as an additional control variables in our model to address omitted

variables problem.


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goods and services efficiently); (c) a measure of employee fluctuations (standard deviation of

total employees); (d) a historical growth measure (one-year percentage change in total sales)

( proxy for a firm’s historical growth) ; (e) the ratio of marketing (SG&A) to sales (a proxy for

firms’ emphasis on marketing and sales) ; and (f) a measure of capital intensity (net PPE scaled

by total assets) (designed to cap ture a firms’ focus on production) .

All variables are computed using a rolling average over the prior five years. Each of the

six individual variables is ranked by forming quintiles within each two-digit SIC industry-year.

Within each company-year, those observations with variables in the highest quintile are given a

score of 5, in the second-highest quintile are given a score of 4, and so on, and those

observations with variables in the lowest quintile are given a score of 1. Then for each company-

year, the scores across the six variables are summed such that a company could receive a

maximum score of 30 (prospector-type) and a minimum score of 6 (defender-type). 5

3.4 Empirical model

Before formally developing empirical models for testing our hypotheses we estimate firm’s

decision to procure tax services from incumbent auditors because of the endogenous nature of


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acquire tax services from incumbent auditors following McGuire et al. (2012). We estimate the

following probit model (firm and year subscripts are not reported):

)4.......(..............................4&

1615

1413121110987

6543210

SECTIER BIGCASH ROA PPE BTM LEV D R FI EQINC

NOL DAC SIZE LAF AUDIND MERGER ATSD

Where ATSD is an indicator variable set equal to 1 if the firm purchased tax services from their

incumbent auditors, and 0 otherwise; MERGER is an indicator variable set equal to 1 if the firm

participated in any merger activity during the year, and 0 otherwise; AUDIND is auditor

independence from the client, measured as nonaudit fees less tax fees divided by total audit fees

received from the client; LAF is natural log of audit fees received from the client; SIZE is natural

log of market value of equity; |DAC| is the absolute value of Modified Jones (1995) discretionary

accruals; NOL is an indicator variable set equal to 1 if there is a tax loss carryforward during

year t, and 0 otherwise; EQINC is equity income for year t scaled by total assets at the beginning

of the year; FI is pre-tax foreign income for year t scaled by total assets at the beginning of the

year; R&D is R&D expense for year t scaled by total assets at the beginning of the year; LEV is

long-term debt for year t scaled by total assets at the beginning of the year; BTM is book-to-


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expected on MERGER , LAF , SIZE , NOL , FI , LEV , PPE , ROA, and BIG4 , and negative

coefficients on AUDIND , |DAC| , EQINC , R&D , BTM , CASH , and SECTIER . We estimate IMR

from first-stage model and use this as an additional independent variable in the subsequent

regressions to address endogeneity.

To investigate the association between auditor-provided NATS and future crash risk we

develop the following regression model.

)5(........................................4||

3*

15114113112

11111019181716

15141312110,

IMR BIG ROA DAC LEV MTBSIZE SDRET RET TURN

ETR ETRTAX ETRTAX CRASH CRASH

t t t

t t t t t t

t t t t t t i

Where CRASH risk is proxied by NCSKEW and DUVOL measures following equations (2) and

(3) above. The independent variables are calculated using data from the preceding year

consistent with crash risk literature. We first control for the lag value of CRASH_RISK to

account for the potential serial correlation of NCSKEW or DUVOL for the sample firms. TAX is

dollar value of tax services in thousand. The coefficient on TAX could either be positive or

negative with respect to crash risk depending on whether auditor-provided NATS threatens

auditor independence or generates financial reporting quality spillover benefits ∆ETR is change


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Inclusion of the control variables follow prior literature on the determinants of crash risk.

TURN t-1 is the average monthly share turnover over the current fiscal year minus the average

monthly share turnover over the previous fiscal year, where monthly share turnover is calculated

as the monthly trading volume divided by the total number of shares outstanding during the

month. Chen et al. (2001) indicate that this variable is used to measure differences of opinion

among shareholders and it is significantly, positively related to crash risk proxies. Chen et al.

(2001) show that negative skewness is larger in stocks that have had positive stock returns over

the prior 36 months. To control for this possibility, we include past one-year weekly returns (

RET t-1). SDRET t-1 is the standard deviation of firm-specific weekly returns over the fiscal year

denoting stock volatility as more volatile stocks are likely to be more crash prone. Chen et al.

(2001) also demonstrate that negative return skewness is higher for larger firms. To control for

the size effect, we add SIZE t-1 measured as the natural log of total assets. The variable MTB t-1 is

the market value of equity divided by the book value of equity. Both Chen et al. (2001) and

Hutton et al. (2009) show that growth stocks are more prone to future crash risk. LEVERAGE t-1 is the total long-term debt divided by total assets, which is shown to be negatively associated

with future crash risk (Kim et al. 2011a, b). DAC t-1 is the absolute discretionary accruals


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H1 also predicts that auditor-provided NATS will affect future crash risk by deterring or

aiding clients’ abusive tax avoidance practices. The following regression specification is

developed to test this proposition.

)6.........(..............................4||

*

14113112

11111019181716

15141312110,

IMR BIG ROA DAC LEV MTBSIZE SDRET RET TURN AVOIDTAX AVOIDTAX CRASH CRASH

t t

t t t t t t

t t t t t t i

We use three proxies to operationalize tax avoidance behavior. Lisowsky, Robinson, and

Schmidt (2012) suggest that the probability of engaging in tax sheltering, discretionary

permanent book-tax difference, permanent book-tax difference, book-tax difference, and cash

effective tax rates capture the varying degree of tax aggressiveness, from most aggressive to least

aggressive. We consider tax sheltering and permanent book-tax differences as the two most

aggressive form of tax avoidance and also use ETR as the least aggressive form of tax avoidance.

We first use the tax shelter prediction score developed by Wilson (2009) as follows:

SHELTER = -4.86 + 5.20 * BTD + 4.08 * DAC - 1.41 * LEV + 0.76* SIZE + 3.51 * ROA + 1.72* FI + 2.43 * R&D …………………………………(7)

where BTD is book income less taxable income scaled by lagged total assets DAC is the


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Our second tax avoidance proxy, which is also considered to be relatively aggressive, is

discretionary permanent book-tax differences ( DTAX ), a subset of BTD [( BTD reduces the firm‟s

tax liability while increasing after-tax reported financial income (Shevlin 2002)]. Following

Frank et al. (2009) we first compute permanent book-to-tax difference ( PERMDIFF ) as total

book-tax differences ( BTD) less temporary book-tax differences ( TXDI/STR ). DTAX is defined as

the residuals from the regression of permanent differences on several determinants of

nondiscretionary permanent differences unrelated to tax planning, estimated by year and two

digit SIC code, with at least 20 firms in each industry:

PERMDIFF = α 0 + α1(1/ ATLAG) + α 2 INTANG + α 3UNCON + α 4 MI + α 5CSTE + α 6 ΔNOL +α7 LAGPERM + ε )……………………..(8)

ATLAG refers to lagged total assets ( AT ), INTANG refers to goodwill and other

intangibles ( INTAN ), UNCON refers to income/loss reported under the equity method ( ESUB ),

MI refers to income/loss attributable to minority interest ( MII ), CSTE refers to current state tax

expense ( TXS ), ΔNOL refers to the change in net operating loss carry forwards ( TLCF ) and

LAGPERM is the lagged PERMDIFF . All variables are scaled by lagged total assets.


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Finally we test tax provision by incumbent auditors on crash risk for firms following

different business strategies. The following regression equation is developed.

)9.........(..............................4||

*****

18117116115

11411311211111019

181716

15141312110,


AVOIDSTRATEGY TAX AVOIDSTRATEGY STRATEGY TAX AVOIDTAX AVOIDSTRATEGY TAX CRASH CRASH

t t t

t t t t t t

t t t

t t t t t t i

where, STRATEGY is categorised into prospector/innovator strategies ( PROSPECT ) and

defender ( DEFEND ) strategies. Details about STRATEGY score composition is explained in 3.3

above. All other variables are defined as before. Higgins et al. (2013) document an increasing

propensity for innovators to engage in more tax avoidance compared to their defender

counterparts. Habib and Hasan (2014) provide empirical evidence that prospectors are more

prone to crash. If auditor-provided NATS mitigate future crash risk then iot should be more

pronounced for innovators willing to engage in more tax avoidance. We, therefore, expect a

negative coefficient on the three-way interaction variable TAX*STRATEGY*AVOID if

knowledge spillover benefits dominated.

To control for unobservable industry and year characteristics associated with firm tax


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period. We then excluded firm-year observations from the regulated industries (two digit SIC 49)

and financial institutions (two-digit codes 60-69). This eliminates a total of 55,720 firm-year

observations. We further eliminated 15,879 non-US firm-year observations yielding a US sample

with required audit data of 88,897 firm-year observations. We then matched CIK codes from

COMPUSTAT with Audit Analytics CIK codes and found a matched sample of 45,573 firm-year

observations. Not all of these observations had the requisite crash risk measures and related

control variables further reducing the sample to 21,950 firm-year observations. Our sample size

further reduces for tax avoidance analysis because of missing data on tax avoidance.

Panel A in Table 1 provides descriptive statistics for the variables used in the regression

analyses. The mean values of the crash risk measures, NCSKEW and DUVOL , are -0.07 and -

0.37 respectively. About 67% of the firm-year observations procure tax services from their

incumbent auditors. In terms of dollar values, average firms pay about $303,000 in tax fees

although there is substantial variation among companies as is evident from a high standard

deviation. Average tax avoidance measures are 0.42, 0.02, and 0.28 for SHELTER , DTAX , and

ETR measures respectively. Average STRATEGY score is 16.91 on a scale of 6 to 30 with

prospectors and defenders constituting 5% and 11% of the sample observations respectively.


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[TABLE 1 ABOUT HERE]

5. Multivariate regression results

We begin our multivariate analysis by modeling the determinants of firms’ decision to procure

tax services from their incumbent auditors to alleviate the endogeneity concern since the choice

of incumbent auditors over other providers is not a random selection. Regression specification

(4) is a probit model whereby auditor-provided NATS is regressed on a set of variables likely to

determine firm’s decision to engage incumbent auditor as the tax service provider (McGuire et

al., 2012). The model includes a number audit-related variables, e.g., BIG4 and second tier

auditor indicator, audit fees, and a variable capturing auditor independence from client. Also

included are financial variables likely to explain firm’s decision to choose incumbent auditors as

NATS provider. We find that larger firms, firms audited by Big 4, firms paying higher audit fees,

and firms with more foreign income are more likely to procure NATS from incumbent auditors

compared to firms audited by second tier audit firms, and firms with more equity income. We

computed IMR from this regression model and used it as an additional independent variable in


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proxied by dollar amount of tax service costs is negatively related to future crash risk for both

the crash risk measures (coefficients of -0.000017 and -0.000014 for NCSKEW and DUVOL

respectively). The negative and significant coefficients suggest that auditor-provided NATS

reduce future crash risk, lending support to knowledge spillover benefits being derived from

NATS. Among the control variables, the coefficient on average returns (RET) and ROA is

positive and that on return volatility is negative suggesting that firms with better stock and

accounting performance and lower volatility are more likely to experience crashes. This suggests

that crashes are unlikely to be a manifestation of declining business conditions, continuation of

poor stock performance (i.e., negative stock momentum), and/or high stock volatility. Larger

firms and high M/B firms are more prone to crash risk. BIG4 audit appears to constrain future

crash risk but the coefficient is s ignificant in the baseline model only.

We then extend our baseline regression model by incorporating two additional

moderating variables, namely change in 4 th quarter ETR ( ∆ ETR) and the interaction between TAX

and ∆ ETR (TAX * ∆ ETR) . Since a decrease in 4 th quarter ETR compared to 3 rd quarter ETR is

used as a proxy for earnings management, the coefficient on the interaction variables is expected

to be negative (positive) if knowledge spillover (impairment of independence) argument


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Overall, test of H1 supports auditors’ constraining effect on tax expense management

courtesy of their more intimate knowledge derived from NATS which reduces subsequent crash

risk.


Next we examine the association between auditor-provided NATS and crash risk and whether

clients’ tax avoidance strategies strengthens or weakens the relationship. As argued before,

aggressive tax positions involve complex and risky techniques providing management with the

tools, and justifications for opportunistic managerial behavior, such as earnings manipulations,

related party transactions, and other resource-diverting activities (e.g., Chen et al. 2010; Desai

and Dharmapala 2006; Kim et al. 2011). Auditor provided NATS could threaten auditor

independence with auditors devising tax avoidance strategies for their clients. Since tax

avoidance allows managers to hoard bad news, the combined effect of auditor-provided NATS

and tax avoidance would increase the probability of crash risk (Kim et al., 2011). On the other

hand, knowledge spillover argument would suggest that incumbent auditors who are also

providing tax services would be better able to deter opportunistic tax avoidance strategies,


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measure. For SHELTER and ETR measures, the negative and significant coefficient is only

evident for DUVOL crash measure. Taken together we find some evidence supporting

knowledge spillover benefit emanating from auditor-provided NATS, with a deterring impact on

future crash risk.


Our final empirical analysis examines the combined effect of NATS and tax avoidance

on crash risk for firms pursuing different business strategies. This test is motivated by the

observations that many of the firms’ tax -related decisions are influenced to a certain extent by

firm’s business strategies. Following Miles and Snow (1978, 2003) strategy typology, which

places prospectors and defenders at two extremes, Higgins et al. (2013) document an increasing

propensity for innovators to engage in more tax avoidance compared to their defender

counterparts. We argue that if auditor-provided NATS provide knowledge spillover benefits then

the mitigating effect on future crash risk would be more pronounced for firms following

innovator business strategies.

Table 5 reports result for this analysis. We include auditor-provided NATS (TAX ), tax


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less aggressive nature of this tax avoidance measure. Because of the three-way interaction, all the

two-way interactions and main effects are also included. However, the signs on the two-way

interactions and main effects can no longer be easily interpreted after the inclusion of the three-

way interaction.

Additional analyses

(i) Discretionary accruals as earnings management : In our main test we have used tax-

specific earnings management technique, i.e., changes in ETR from 3 rd to 4 th quarter. To rule out

the possibility that earnings management as captured by Modified Jones model (1995) subsumes

the effect of tax-specific earnings management for future crash risk, we include an interaction

variable TAX*DAC in Table 4. Untabulated result reveals that the coefficient on the interactive

variable is insignificant.

(ii) Tax fee ratio: We also conducted additional analysis using fee ratio (total tax fees/total fees)

as an alternative NATS proxy. The coefficients on the interaction variables using FEERATIO are

reported below.


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(iii) External monitoring by financial analysts, NATS, and crash risk : As discussed earlier,

agency theory view of managerial incentives for withholding bad news through earnings

management and/or tax avoidance is based on the agency tension between managers and

shareholders. Strong external monitoring mechanisms can act as complements or substitutes to

auditor monitoring. Kim et al. (2011) find that the positive relation between tax avoidance and

stock price crash risk is diminished for firms with strong external monitoring mechanisms. We

use analyst coverage as one such external monitoring mechanism. We retrieve number of

analysts following a firm from I/B/E/S and include it as an additional independent variable in

Table 3 and 4. Our primary result of the negative effect of auditor-provided NATS and the

ineteaction variables TAX*∆ETR and TAX*AVOID on crash risk remains unchanged. We also

split the sample into high analyst coverage observations (number of analyst>median) and low

analyst coverage sub-samples. Again, our main results remain robust to this analysis.

6. Conclusion

Concern about possible impairment of independence for external auditors because of the joint

provision of audit and NASs has long been a concern for regulators and investment community.


30/42

avoidance arrangements, therefore, may increase the risk of crash risk, a direct economic

consequence for investors. Consistent with this conjecture, Kim et al. (2011) find that tax

avoidance indeed increases future crash risk. Kim et al. (2011), however, did not investigate

whether auditor-provided NATS aid or deter tax-related earnings management as well as tax

avoidance behavior . Therefore external auditors’ involvement in the tax avoidance -crash risk

link remains unexplored. We fill this void in the literature.

Our empirical findings reveal that financial reporting quality, at least from the

perspective of reporting tax transactions, improve financial reporting quality and reduces the

probability of future crash risk. We, therefore, find support for spillover benefits and concur with

regulatory decision to allow audit firms providing NATS. We also find that, auditor-provided

NATS constrain tax avoidance and hence reduces the probability of crash risk for firms

following innovator business strategies,.

We contribute to the literature providing evidence on the desirability of allowing, further

constraining, or banning auditor-provided NATS (PCAOB 2004). We also extend the growing

literature on the determents of crash risk by investigating the effect of external auditors ’

monitoring through the provision of NATS on crash risk. Finally, our study contributes to the


31/42

References

Cook, K., G. R. Huston, and T. Omer. 2008. Earnings management through effective tax rates:The effects of tax planning investment and the Sarbanes-Oxley Act of 2002.Contemporary Accounting Research 25 (2): 447 – 71.

Dhaliwal, D., C. Gleason, and L. Mills. 2004. Last-chance earnings management: Using the taxexpense to meet analysts‟ forecasts. Contemporary Accounting Research 21 (2): 431-

459.Donohoe, M. P., and W. R. Knechel. 2013. Does corporate tax aggressiveness influence audit

pricing? Contemporary Accounting Research Forthcoming.Hanlon, M., and J. Slemrod. 2009. What does tax aggressiveness signal? Evidence from stock

price reactions to news about tax shelter involvement. Journal of Public Economics 93,126-141.

Harris, D., and Zhou, J. (2013). Auditor-Provided Tax Consulting, Knowledge Spillovers, andReported Weaknesses in Internal Control. Working paper, Syracuse University andUniversity of Hawaii at Manoa

Kim, J-B., Y. Li, and L. Zhang. 2011. Corporate tax avoidance and stock price crash risk: Firm- level analysis. Journal of Financial Economics 100: 639-662.Koester, A. 2012. Investor valuation of tax avoidance through uncertain tax positions, Working

paper, Georgetown University.Lisowsky, P., Robinson, L., Schmidt, A., 2012. Do publicly disclosed tax reserves tell us about

privately disclosed tax shelter activity? Journal of Accounting Research, forthcoming.McGuire S. T., T. C. Omer, and D. Wang. 2012. Tax avoidance: Does tax-specific industry

expertise make a difference? The Accounting Review 87 (3): 975-1003.Paterson, J. S. and A. Valencia. 2011 The effects of recurring and nonrecurring tax, audit related,

and other nonaudit services on auditor independence. Contemporary Accounting

Research 28(5): 1510-1536.Rego, S., and R. Wilson, 2012. Equity risk incentives and corporate tax aggressiveness, Journalof Accounting Research , forthcoming.

Robin, A., and Zhang, H. Do Industry-Specialist Auditors Influence Stock Price Crash Risk?Forthcoming in Auditing: A Journal of Practice & Theory


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Appendix 1: Variable definitions

Variables Explanation NCSKEW Negative conditional skewness of firm-specific weekly returns over the fiscal year. NCSKEW is calculated by taking the

negative of the third moment of firm-specific weekly returns for each year and normalizing it by the standard deviation offirm-specific weekly returns raised to the third power [See text for the detailed fomula].

DUVOL Down-to-up volatility measure of the crash likelihood. For each firm j over a fiscal-year period t, firm-specific weekly returnsare separated into two groups: ‘‘down’’ weeks when the returns are below the annual mean, and ‘‘up’’ weeks when the returnsare above the annual mean. Standard deviation of firm-specific weekly returns is calculated separately for each of these twogroups, and DUVOL is the natural logarithm of the ratio of the standard deviation in the ‘‘down’’ weeks to the standarddeviation in t he ‘‘up’’ weeks. For both crash risk measures the firm -specific weekly return for firm j in week τ (W j,τ ) iscalculated as the natural logarithm of one plus the residual return from the following expanded market model regression:

)1.......(,,,,, ,2,51,4,31,22,1, jm jm jm jm jm j j j r r r r r r Where r, j,τ is the return of firm j in week τ , and r m,τ is the return on CRSP value-weighted market return in week τ . The lead andlag terms for the market index return is included to allow for nonsynchronous trading (Dimson, 1979). above. In estimatingequation (1), each firm-year is required to have at least 26 weekly stock returns.

TURN Average monthly share turnover over the current fiscal year minus the average monthly share turnover over the previous fiscalyear, where monthly share turnover is calculated as the monthly trading volume divided by the total number of sharesoutstanding during the month.

RET One-year weekly returns.SDRET Standard deviation of firm-specific weekly returns over the fiscal year.

ATSD An indicator variable set equal to 1 if the firm purchased tax services from the auditor, and 0 otherwise.TAX (‘000) Total dollar amount in thousands paid to auditors for tax services .

MERGER An indicator variable set equal to 1 if the firm participated in any merger activity during the year, and 0 otherwise. AUDIND Auditor independence from the client, measured as nonaudit fees less tax fees divided by total audit fees received from the

client. LAF Natural log of audit fees received from the client.SIZE Natural log of market value of equity;

NOL An indicator variable coded 1 if the firm reported net operating loss carryforward, and 0 otherwise. EQINC Equity income for year t scaled by total assets at the beginning of the year. FI Pre-tax foreign income for year t scaled by total assets at the beginning of the year. LEV Long-term debt for year t scaled by total assets at the beginning of the year. BTM Book-to-market ratio for the end of year t, measured as book value of equity divided by market value of equity ROA Return on assets for year t, measured as the ratio of income before extraordinary items to the average of total assets for the

year


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33 | P a g e

CASH Cash holding at the end of year t divided by total assets at the beginning of the year BIG4 An indicator variable set equal to 1if the firm is audited by one of the Big 4 auditors, and 0 otherwise.SECTIER An indicator variable set equal to 1 if the firm is audited by a second-tier accounting firm; namely, Grant Thornton LLP and

BDO Seidman LLP, and 0 otherwise. ETR ETR is defined as accumulated tax expense (Compustat quarterly #6) divided by accumulated pre-tax income (quarterly #23).SHELTER Tax shelter prediction score developed by Wilson (2009) using BTD, DAC, LEV, SIZE, ROA, FI , and R&D .

DTAX Discretionary permanent book-tax differences ( DTAX ), a subset of BTD . Following Frank et al. (2009) we first compute permanent book-to-tax difference ( PERMDIFF ) as total book-tax differences ( BTD) less temporary book-tax differences(TXDI/STR). DTAX is defined as the residuals from the regression of PERMDIFF on several determinants of nondiscretionary

permanent differences unrelated to tax planning, DAC Absolute discretionary accruals calculated using the performance-adjusted Modified Jones model (Kothari, Leone, and

Wasley, 2005). We estimate the following model for all firms in the same industry (using the SIC two-digit industry code)with at least 8 observations in an industry in a particular year, to get industry-specific parameters for calculating the non-discretionary component of total accruals ( NDAC ). DAC is then the residual from model (1), i.e., DAC = ACC-NDAC . WhereACC = Net income operating cash flows (OCF)/Lagged total assets.

t t t t t t t ROA PPE RECEIVABLE Sales Assets ACC 132110 )/1( Where, ACC is total accruals defined as the difference between net income before extraordinary items and operating cashflows (OCF), PPE is gross property, plant & equipment, ROA is return on assets. All variables are deflated by lagged assets.

R&D5 Ratio of research and development expenditures [XRD] to sales [SALE] computed over a rolling prior 5 year average EMPLOYEE5 Ratio of the number of employees [EMP] to sales [SALE] computed over a rolling prior 5 year average. REV5 One-year percentage change in total sales computed over a rolling prior five-year average SG&A5 Ratio of selling, general and administrative (SG&A) expenses to sales computed over a rolling prior five-year average. SD EMPLOYEE5 Standard deviation of the total number of employees computed over a rolling prior five-year period CAP5 Capital intensity measured as net property, plant, and equipment scaled by total assets and computed over a rolling prior five-

year average STRATEGY Each of the above six individual variables is ranked by forming quintiles within each two-digit SIC industry-year. Within each

company-year, those observations with variables in the highest quintile are given a score of 5, in the second-highest quintile

are given a score of 4, and so on ((except capital intensity which is reversed-scored so that observations in the lowest (highest)quintile are given a score of 5 (1)). Then for each company-year, the scores across the six variables are summed such that acompany could receive a maximum score of 30 (prospector-type) and a minimum score of 6 (defender-type).


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Table 1: Panel ADescriptive statistics

Variables Mean SD 25% 50% 75% ObservationsCrash risk

proxiesCRASH_NCSKEW t -0.07 1.17 -0.74 -0.07 0.61 21,950CRASH_NCSKEW t-1 -0.01 1.06 -0.68 -0.05 0.60 21,950

CRASH_DUVOL t -0.37 0.91 -0.85 -0.29 0.21 21,950CRASH_ DUVOL t-1 -0.31 0.81 -0.80 -0.27 0.21 21,950

Tax servicevariables

TAXDUM t-1 0.67 0.47 0.00 1.00 1.00 21,950TAX (in ‘000 dollar) t-1 302.65 1017.67 0.00 36.75 201.07 21,950

ETR4-ETR3 t-1 0.0007 0.09 -0.01 -0.0006 0.01 15,483 ETR3 t-1 0.32 0.12 0.29 0.35 0.38 15,483 AVOID (SHELTER) t-1 0.42 1.96 -0.71 0.51 1.69 12,149 AVOID(PERMDIFF) t-1 0.02 0.17 -0.02 0.01 0.06 11,114 AVOID (ETR) t-1 0.28 0.16 0.19 0.31 0.37 17,473

Business strategyvariables

STRATEGY t-1 16.91 3.66 14 17 19 12,149 PROSPECT t-1 0.05 0.21 - - - 12,149

DEFEND t-1 0.11 0.32 - - - 12,149Controlvariables

for crashrisk

TURN t-1 0.00 0.10 -0.03 0.00 0.03 21,950 RET t-1 0.07 0.01 0.00 0.00 0.01 21,950STDRET t-1 2.63 0.04 0.05 0.06 0.09 21,950SIZE t-1 6.04 1.99 4.60 6.02 7.42 21,950

MTB t-1 2.63 3.38 1.17 1.91 3.17 21,950 LEV t-1 0.16 0.18 0.00 0.11 0.26 21,950|DAC| t-1 0.31 0.59 0.04 0.10 0.29 21,950

ROA t-1 0.04 0.18 -0.01 0.06 0.13 21,9501 st stageregressionvariables

MERGER t 0.18 0.38 0.00 0.00 0.00 21,950 AUDIND t 0.10 0.15 0.00 0.04 0.12 21,950 LNAF t 13.47 1.55 12.55 13.55 14.41 21,950 NOL t 0.81 0.39 1.00 1.00 1.00 21,950 EQINC t 0.00 0.00 0.00 0.00 0.00 21,950 FI t 0.02 0.04 0.00 0.00 0.02 21,950


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Panel B: Industry distribution

Industry code Industry Observations % distribution

1-14 Agriculture & mining 1,158 0.0515-17 Building construction 163 0.01

20-21 Food & Kindred Products 682 0.0322-23 Textile Mill Products & apparels 404 0.0224-27 Lumber, furniture, paper, and printing 917 0.0428-30 Chemical, petroleum, and rubber & Allied Products 2,243 0.1031-34 Metal 1,063 0.0535-39 Machinery, electrical, computer equipment 7,171 0.3340-48 Railroad and other transportation 901 0.0450-51 Wholesale goods, building materials 973 0.0453-59 Store merchandise, auto dealers, home furniture stores 2,030 0.0970-79 Business services 3,165 0.1480-99 Others 1,080 0.05

Total 21,950 1.00


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Panel C: Correlation analysis

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) NCSKEW (1) 1.00 NCSKEW t-1 (2) -0.00 1.00 DUVOL (3) 0.80 -0.00 1.00 DUVOL t-1 (4) 0.04 0.78 0.12 1.00TAX t-1 (5) 0.02** 0.02** 0.08 0.08 1.00TURN t-1 (6) 0.03 0.04 0.05 0.03 0.001 1.00

RET t-1 (7) 0.03 0.07 0.009 0.11 -0.02 0.12 1.00STDRET t-1 (8) -0.05 -0.11 -0.14 -0.25 -0.15 0.11 0.16 1.00SIZE t-1 (9) 0.08 0.10 0.26 0.30 0.39 0.05 -0.07 -0.38 1.00

MTB t-1 (10) 0.04 0.06 0.04 0.06 0.06 0.05 0.14 - 0.07 0.05 1.00 LEV t-1 (11) -0.002 -0.01* 0.03 0.02** 0.06 0.05 -0.02** 0.001 0.31 -0.04 1.00|DAC| t-1 (12) -0.01 -0.001 -0.02** -0.03 0.00 -0.02** 0.001 0.06 -0 .08 0.03 - 0.05 1.00

ROA t-1

(13) 0.05 - 0.06 0.19 0.15 0.08 0.04 0.17 - 0.34 0.28 0.09 -0.04 -0.06 1.00 BIG4 t-1 (14) 0.04 0.05 0.14 0.17 0.15 0.02** -0.001 -0.17 0.51 0.05 0.16 -0.07 0.15 1.00

Italicized and bold-faced correlations are significant at p


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Table 2: Determinants of tax service purchase

)4.......(..............................4&

1615

1413121110987

6543210

SECTIER BIGCASH ROA PPE BTM LEV D R FI EQINC

NOL DAC SIZE LAF AUDIND MERGER ATSD

Variables Coefficient z-statisticsConstant -5.19*** -12.29

MERGER -0.001 -0.04 AUDIND -0.50*** -3.68 LAF 0.10*** -5.18SIZE 0.20*** 12.43

DAC -0.03 -0.87 NOL 0.06 1.25 EQINC -7.67* -1.95 FI 2.86*** 5.35 R&D -0.12 -0.45 LEV 0.09 0.80 BTM 0.003 0.65 PPE -0.07 -0.66 ROA 0.05 0.53CASH -0.09 -0.97

BIG4 0.51*** 9.57SECTIER -0.18*** -2.92

Industry FE YesYear FE Yes Pseudo R 2 0.20Observations 21,950


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Table 3: Auditor-provided non-audit tax services, financial reporting quality and crashrisk

)5(........................................4||

3*

15114113112

11111019181716

15141312110,


ETR ETRTAX ETRTAX CRASH CRASH

t t t

t t t t t t

t t t t t t i

Baseline model Earnings management modelVariables Model (1) Model (2) Model (3) Model (4)

Coefficient[t-statistics]




Constant -0.25[-1.07]

-0.67***[-3.61]

-0.93**[-2.71]

-1.16***[-4.67]

NCSKEW t-1 -0.02**[-2.44]

- -0.01*[-1.64]

-

DUVOL t-1 - 0.03***[3.51]

- 0.02**[2.38]

TAX t-1 -0.000017**[-2.16]

-0.000014***[-2.78]

-0.000019***[-2.70]

-0.000019***[-3.23]

∆ ETR t-1 -0.0040[-1.04]

-0.002[-0.97]

TAX t-1* ∆ ETR t-1 - - -0.00001**[-2.33]

-0.000013**[-2.26]

ETR3 t-1 - - -0.02***[-3.09]

-0.02***[-3.18]

TURN t-1 0.32***[3.61]

0.27***[4.12]

0.08[0.68]

0.19**[2.28]

RET t-1 1.68**[1.97]

3.31***[5.04]

1.21[1.03]

4.01***[4.47]

STDRET t-1 -0.14[-0.48] -1.50***[-6.29] 0.66[1.59] -1.55***[-4.96]SIZE t-1 0.05***

[5.30]0.10***[11.80]

0.08[8.08]

0.08***[13.96]

MTB t-1 0.0082*** 0.005*** 0.01 0.007***

Commented [AH1]: SIZE=ln SALES; LEV2; ROE version

Commented [AH2]: Only if size is proxied by Ln MVE and BTMis used instead of MTB, among others.

Commented [AH3]: Both LN MVE and LN TA provide negativeand significant coefficient. Also MTB & BTM don’t affect theresults.

Commented [AH4]: Expected result. Use LNMVE (size), ROE,BTM, and LEV2. ETR unwinsorised.


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Table 4: Auditor-provided non-audit tax services, tax avoidance, and crash risk

)6.........(..............................4||*

14113112111110

1918171615141312110,

IMR BIG ROA DAC LEV MTBSIZE SDRET RET TURN AVOIDTAX AVOIDTAX CRASH CRASH

t t t t

t t t t t t t t t t i

Avoidance= SHELTER Avoidance= DTAX Avoidance= ET R

Variables Coefficient[t-statistics]






Constant -0.50**[-2.01]

-0.44**[-2.06]

-0.65[-1.60]

-0.71**[-2.46]

-0.54*[-1.82]

-0.64***[-3.10]

NCSKEW t-1 -0.03***[-3.22]

- -0.04***[-3.75]

- -0.013[-1.56]

-

DUVOL t-1 - 0.01[1.13]

- 0.003[0.31]

- 0.03***[3.44]

TAX t-1 -0.000072*[-1.88]

-0.000016**[-2.20]

-0.01[-0.13]

-0.000020**[-2.73]

0.000035[1.53]

0.000023[1.38]

AVOID t-1 0.02**[2.19]

0.016**[2.14]

0.15*[1.87]

0.11**[2.08]

0.04[0.58]

0.01[0.22]

TAX t-1*AVOID t-1 0.000017[1.39]

-0.000013*[-1.71]

-0.000016*[-1.63]

-0.000016**[-2.13]

-0.00017[-1.54]

-0.00015**[-2.33]

TURN t-1 0.38***[3.15]

0.30***[3.25]

0.18[1.38]

0.16*[1.76]

0.23**[2.29]

0.23***[3.12]

RET t-1 2.10*[1.78]

2.56***[2.78]

1.75[1.41]

4.67***[4.94]

1.05[1.04]

3.15***[4.21]

STDRET t-1 -0.69*[-1.84]

-1.72***[-5.23]

0.02[0.04]

-2.01***[-6.12]

0.09[0.27]

-1.42***[-5.73]

SIZE t-1 0.01[0.83]

0.06***[6.29]

0.06***[5.54]

0.09***[10.22]

0.05***[4.79]

0.08***[9.62]

MTB t-1 -0.00[-0.11]

0.0076***[3.26]

-0.007[-0.59]

0.04***[2.96]

0.04***[8.46]

0.004**[2.03]

LEVERAGE t-1 0.07*[1.68]

-0.15**[-2.60]

0.04[0.88]

0.03[1.03]

-0.16***[-2.67]

-1.11**[-2.46]

|DAC| t-1 0.03[1.32]

-0.009[-0.53]

0.02[0.90]

-0.01[-0.66]

0.00[0.06]

-0.02[-1.20]

Commented [AH6]: This should have been most negative tosupport knowledge spillover for most severe form of avoidance.


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Avoidance= SHELTER Avoidance= DTAX Avoidance= ET R Variables Coefficient

[t-statistics]

Coefficient

[t-statistics]

Coefficient

[t-statistics]

Coefficient

[t-statistics]

Coefficient

[t-statistics]

Coefficient

[t-statistics] ROA t-1 0.001

[0.42]0.44***[6.29]

0.002[1.10]

0.003**[2.13]

0.18***[2.94]

0.48***[10.93]

BIG4 -0.08*[-1.79]

-0.02[-0.59]

-0.05[-1.14]

-0.05[-1.47]

-0.05**[-2.00]

0.0055[0.29]

IMR -0.17*[-1.77]

-0.25***[-3.62]

-0.02[-0.13]

-0.24**[-2.37]

-0.04[-0.46]

-0.18***[-2.70]

Industry FE Yes Yes Yes Yes Yes YesYear FE Yes Yes Yes Yes Yes Yes

Adjusted R 2 0.03 0.11 0.03 0.11 0.02 0.10Observations 12,149 12,149 11,114 11,114 17,473 17,473

***, **, and * represent statistical significance at the 1%, 5%, and 10% level respectively (two-tailed test). Variable definitions are in Appendix 1.


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Table 5: Business strategy, tax avoidance, auditor-provided non-audit tax services and crash risk

)7.........(..............................4||***

**

18117116115114

113112111110191817

1615141312110,

IMR BIG ROA DAC LEV MTBSIZE SDRET RET TURN AVOIDSTRATEGY TAX AVOIDSTRATEGY

STRATEGY TAX AVOIDTAX AVOIDSTRATEGY TAX CRASH CRASH

t t t t

t t t t t t t

t t t t t t t i

AVOIDANCE=SHELTER AVOIDANCE= DTAX AVOIDANCE=ETR

Variables Coefficient[t-statistics]






Constant 0.14[0.52]

-0.18[-0.85]

-0.68[-2.04]

-0.66[-2.31]

-0.17[-0.49]

-0.25[-0.96]

NCSKEW t-1 -0.03***[-3.00]

- -0.04[-3.77]

-0.02**[-2.19]

-

DUVOL t-1 - 0.02[1.34]

0.004[0.36]

- 0.03***[3.38]

TAX t-1 -0.0002[-1.25]

-0.00025**[-2.09]

-0.000039[-0.67]

-0.000079[-2.18]

-0.000089[-0.84]

-0.0000[-0.20]

STRATEGY t-1 0.001*[2.90]

-0.0001[-0.38]

0.0042[1.20]

-0.007[-2.44]

0.009*[1.87]

0.006[1.43]

AVOID t-1 0.07**[2.26]

0.11***[4.53]

0.08[1.13]

0.11[2.01]

0.29[1.10]

0.28[1.28]

TAX*AVOID t-1 0.000078[1.51]

0.000062*[1.76]

0.00044[1.99]

0.00077[2.42]

0.0004[1.15]

0.00005[0.20]

TAX*STRATEGY t-1 -0.00009[-0.90]

0.000014**[2.33]

0.0000012[1.10]

0.0000032[1.68]

0.000006[1.17]

-0.00001[-0.76]

STRATEGY*AVOID t-1 -0.002[-1.38]

-0.003**[-2.52]

0.05[2.95]

0.04[2.65]

-0.02[-1.19]

-0.02[-1.44]

TAX*STRATEGY*AVOID t-1 -0.00004*

[-1.60]

-0.000038**

[-2.09]

-0.000022**

[-2.33]

-0.000043**

[-2.43]

-0.000035

[-1.58]

-0.000019

[-0.83]TURN t-1 0.33**[2.59]

0.35***[3.74]

0.20[1.50]

0.16[1.67]

0.24**[2.35]

0.23***[3.01]

MEANRET t-1 3.23**[2.51]

5.32***[5.38]

1.92[1.55]

4.49[4.67]

1.99*[1.95]

3.46***[4.47]

STDRET t-1 -0.83**[-2.03]

-2.09***[-6.62]

-0.06[-0.14]

-1.83[-5.49]

-0.05[-0.16]

-1.64***[-5.84]


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AVOIDANCE=SHELTER AVOIDANCE= DTAX AVOIDANCE=ETRCoefficient

[t-statistics]

Coefficient

[t-statistics]

Coefficient

[t-statistics]

Coefficient

[t-statistics]

Coefficient

[t-statistics]

Coefficient

[t-statistics]SIZE t-1 -0.003

[-0.25]0.04***[4.05]

0.06[5.30]

0.09[10.38]

0.04***[3.54]

0.07***[8.52]

MTB t-1 0.00[0.03]

-0.04***[-2.90]

-0.006[-0.34]

0.04[2.62]

-0.02[-1.44]

0.01[1.27]

LEVERAGE t-1 -0.06[-1.04]

-0.23***[-3.80]

0.04[0.90]

0.03[0.93]

-0.008[-0.18]

-0.02[-0.47]

|DAC| t-1 0.03[1.16]

-0.00[-0.02]

0.02[0.83]

-0.01[-0.71]

0.005[0.28]

-0.02[-1.05]

ROA t-1 0.003[0.78]

0.005[1.30]

0.002[1.13]

0.003[2.08]

0.22***[3.22]

0.44***[7.86]

BIG4 -0.07[-1.51]

-0.01[-0.38]

-0.05[-1.14]

-0.04[-1.21]

-0.10**[-2.50]

-0.06*[-1.94]

IMR -0.19**[-2.11]

-0.24***[-3.25]

-0.01[-0.09]

-0.21[-2.10]

-0.18[-1.42]

-0.34***[-3.89]

Industry FE Yes Yes Yes Yes Yes YesYear FE Yes Yes Yes Yes Yes Yes

Adjusted R 2 0.03 0.11 0.03 0.10 0.03 0.10Observations 12,149 12,149 11,114 11,114 17,118 17,118

***, **, and * represent statistical significance at the 1%, 5%, and 10% level respectively (two-tailed test). Variable definitions are in Appendix 1.

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