Tax Aggressiveness and Idiosyncratic Volatility

Neeru Chaudhry1

September 15, 2016

Abstract

Does idiosyncratic volatility increase as corporate tax rates drop? Two otherwise

similar firms will have different effective tax rates (ETR) if one of them is taking risky tax

positions while other does not. Different investors will assess risk associated with firm’s tax

strategies differently and the valuation of stock among investors will vary, causing stock prices

to become volatile. In this study, I examine whether ETRs are useful in predicting idiosyncratic

volatility, and find results consistent with this conjecture. This result is obtained after

controlling for systematic risk and other factors that may affect the relation between ETRs and

idiosyncratic volatility. Results are robust to alternative variable definitions and model

specifications. Results from 2SLS regression and quasi-experiment (implementation of FASB

Interpretation No. 48) establish a direct causal effect of tax aggressiveness on idiosyncratic

volatility. The negative relation between idiosyncratic volatility and ETRs is weaker when

CEO ownership is high, and stronger when CEOs are compensated with options or when

institutional ownership is high. Furthermore, this relation is more pronounced for financially-

constrained firms, younger firms, non-dividend paying firms, and technology firms.

Keywords: Idiosyncratic risk, idiosyncratic volatility, firm-specific risk, tax aggressiveness,

tax avoidance, tax planning, effective tax rates

1 Corresponding author: Neeru Chaudhry (Email: neeru.chaudhry@monash.edu), Department of Banking and

Finance, Monash University Clayton Campus, Clayton, Wellington Road, Victoria 3800, Australia. Phone: +61 3

99052153

I am grateful to Hue Hwa Au Yong, Jonathan Batten, Michael Skully, Chris Veld, and other participants at the

Australasian Finance and Banking Conference (AFBC) 2015 and seminar at Monash University for helpful

comments and suggestions.

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1. Introduction

Investing in tax strategies could be considered a value-maximizing activity, which

transfers wealth from the government to the shareholders. Even though there are several

opportunities available to reduce taxes and do not involve too much risk, some firms do not

engage in tax-reducing activities (Weisbach, 2002). Graham, Hanlon, Shevlin, and Shroff

(2014) note in their survey that reputational concerns constrains firms’ incentives to engage in

tax planning strategies. On the other extreme, some firms actively employ risky tax strategies,

which have very high chances that they will be challenged by the tax authorities and firm will

not be able to claim the tax benefits under those strategies. Consistent with prior literature

(Hanlon and Heitzman, 2010), I view tax strategies as encompassing a spectrum of tax planning

activities with outcomes that range from certain to uncertain. Uncertain (i.e., aggressive or

risky) tax positions are supported by a weak set of facts and are thus less likely to be sustained

upon audit. Moreover, tax rules may change and firms might not be able to claim certain tax

benefits in future. In this study, I argue that aggressive tax strategies help firms to reduce their

effective tax rates but increase firm-specific risk.

Aggressive tax strategies could increase firm-specific risk by increasing the risk of

being audited by tax authorities (Mills, 1998), paying more taxes, penalties, and fines in future

(Wilson, 2009), increasing reputational and political costs (Chen, Chen, Cheng, and Shevlin,

2010; Chyz, Leung, Li, and Rui, 2013; Mills, Nutter, and Schwab, 2013), increasing

information asymmetry and facilitating managerial opportunism (Desai and Dharmapala,

2006), increasing risk of stock price crash (Kim, Li, and Zhang, 2011), and increasing cost of

debt (Hasan, Hoi, Wu, and Zhang, 2014). In addition, the outcome of any given aggressive tax

strategy is uncertain because tax laws can be changed and courts do not always have the same

ruling on similar transactions (Graham and Tucker, 2006).

2

Here, I discuss an example of Pfizer as an anecdotal evidence of why tax strategies may

affect firm-specific risk (Bergin, 2016).2 In 2015, Pfizer has unrepatriated income of $193

billion and effective tax rate (ETR) of 25 percent, which is much lower than the U.S. statutory

tax rate of 35 percent. Motivated to reduce taxes even further, in November 2015, Pfizer

announced that it would merge with Allergan and move its headquarters to Ireland. This deal

was expected to reduce Pfizer’s ETR from 25 to 17-18 percent while making it easier for Pfizer

to gain access to its overseas profits. In April 2016, the U.S. government made changes to the

tax rules that did not allow U.S. subsidiaries of foreign companies to deduct the interest they

pay on loans from their parent firms from their taxable income. As a result of this new rule,

Pfizer-Allergan merger deal was called off and therefore anticipated tax savings from this deal

were not realized. Investors who had anticipated such changes in tax rules would have used

higher discount rate while valuing Pfizer’s stock whereas investors who did not perceive

Pfizer’s tax strategies to be risky would have used lower discount rate and thus valued stock

higher. Therefore investors’ valuation of Pfizer stock will be affected by their assessment of

the benefits and risk associated with Pfizer’s tax strategies. Uncertainty about Pfizer’s ability

to successfully defer taxes on unrepatriated foreign profits and completion of Pfizer-Allergan

merger will increase firm-specific risk and introduce volatility in stock prices. The Pfizer

example demonstrates why risky tax activities make it difficult for investors to predict future

profitability, causing stock prices to become more volatile.

This study contributes to the literature that examine consequences of tax avoidance.

Kim et al. (2011) and Hasan et al. (2014), respectively, document that the risk induced by

aggressive tax strategies increases the probability of future stock crash risk and loan spreads.

These studies suggest that tax avoidance increases riskiness of a firm. In contrast, Guenther,

2 Pfizer is a pharmaceutical company with its headquarters in New York (USA). It is listed on NYSE, London,

Euronext, and Swiss stock exchanges (Source: Pfizer website).

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Matsunaga, and Williams (2016) examine the relation between tax risk (five-year effective tax

rate) and firm risk (total stock return volatility), and do not find any significant relation between

these two variables and conclude that tax avoidance does not increase firm-specific risk. Their

study does not differentiate between idiosyncratic and systematic risk. It is important to

distinguish between idiosyncratic and systematic risk. Firms may have low tax rates because

of an economic downturn (systematic risk) and not necessarily because of a particular tax

strategy (idiosyncratic risk). Furthermore, Guenther et al. (2016) use five-year effective tax

rate to capture the effect of tax avoidance on firm-specific risk. The Internal Revenue Service

(IRS) takes, on average, four years to audit tax returns filed by large firms and another two-

three years to resolve any appeals before final tax assessment could be determined (White,

2013). Taking average of the effective tax rates over a five-year period will dampen the effect

of any event that occurs, for instance in the fourth or fifth year, and results in higher tax

payments during a year. Moreover, data requirements over a five-year period will lead to a

sample that is biased towards the heaviest taxpayers (Dyreng, Hanlon, and Maydew, 2008). By

using a more accurate measure of firm-specific risk and a proxy for tax aggressiveness that

captures year-on-year variation in taxes paid by a firm, this study shows that firm-specific risk

increases because of tax avoidance activities of a firm. Tax aggressiveness has an incremental

ability to predict firm-specific risk over and above other variables identified in the prior

literature (such as size, age, dividend-payment, competition, and industry affiliation). This

finding will have important implications for portfolio diversification, pricing of stock options

and designing of executive compensation, as well as for other corporate decisions.

In contrast to prior studies that relate firm characteristics with idiosyncratic risk, this

research identifies investing in risky tax strategies, which represents a deliberate action taken

by managers, as causing firm-specific risk. In supplementary analyses, I document that the

effect of aggressive tax strategies on idiosyncratic volatility lasts for long periods (as long as

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seven years), partially explaining time-varying nature of the idiosyncratic risk. This evidence

complements finding of Fu (2009) who documents that idiosyncratic risk is not persistent and

varies substantially over time.

To test whether tax aggressiveness leads to higher firm-specific risk, I regress

idiosyncratic volatility, proxy for firm-specific risk, on ETR. Idiosyncratic volatility is

estimated by taking standard deviation of the residuals obtained by regressing daily excess

stock returns on the daily Fama and French (1993) three-factors for each stock and for each

year, and multiplying that by the square root of the number of trading days in a year. ETR is

defined as taxes paid divided by the pretax income (adjusted for special items). ETR

incorporates various tax benefits and deductions that reduce the taxable income relative to

financial income, and indicates the tax rate firms actually incur.

Using a U.S. sample of 31,744 firm-year observations from 4,463 firms between 1990

and 2013, I find a negative and significant relation between ETR and idiosyncratic volatility.

A one percentage increase in ETR causes approximately 11 percent increase in idiosyncratic

volatility relative to the mean. This result is obtained after controlling for factors that may affect

the relation between idiosyncratic volatility and tax rates. This finding is robust to using

alternative measures of key variables, model specifications, different sample periods, and

inclusion of additional control variables in the regression model.

I exploit the unique setting offered by the implementation of FASB Interpretation No.

48 (FIN48), which represents an exogenous event that affected the approach used by firms to

measure and report uncertainty in income taxes. Ciconte, Donohoe, Lisowsky, and Mayberry

(2016), and Drake, Lusch, and Stekelverg (2016) provide evidence that disclosures related to

unrecognized tax benefits (UTB) are informative and help investors in predicting firm’s future

profitability more accurately compared to pre-FIN48 period. Hasan et al. (2014) argue that

FIN48 affected only those firms that take risky tax positions (treatment firms) but did not affect

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firms that do not take risky tax positions (control firms). Therefore it can be expected that UTB

disclosures will reduce uncertainty about future tax payments for treatment firms but would

not have any significant effect on control firms. Results obtained from difference-in-difference

analysis show that firms taking risky tax positions have lower idiosyncratic volatility relative

to firms that do not take risky tax positions. This finding indicates that investors use the

information disclosed in the financial statements pursuant to the implementation of FIN48 as

informative about firm’s tax strategies in terms of uncertain tax positions and likelihood that a

firm will not be able to retain benefits claimed under those strategies. This results in less tax

uncertainty post-FIN48 relative to the pre-FIN48 period. Results obtained from this quasi-

experiment provide a robust evidence of causal effect of tax aggressiveness on idiosyncratic

volatility.

To identify channels that drive the relation between tax strategies and firm-specific risk,

I investigate the effect of CEO ownership, convexity of CEO compensation, institutional

ownership and a number of firm characteristics on the relation between tax aggressiveness and

idiosyncratic volatility. Observing a stronger (insignificant) relation between idiosyncratic

volatility and tax rates for firms with low (high) CEO ownership is consistent with the prior

studies, which find that when managers own a substantial stake in their firms they are reluctant

to invest in projects that increase idiosyncratic risk. This is because managers can hedge

exposure to systematic risk but not to idiosyncratic risk (Panousi and Papanikolaou, 2012).

Since decisions about firm’s tax strategies are primarily taken by CEOs (Dyreng, Hanlon, and

Maydew, 2010) poorly diversified CEOs may not invest in risky tax strategies because it

increases uncertainty about firm’s future prospects. Showing that managerial risk incentives

and institutional ownership strengthens the positive relation between tax aggressiveness and

idiosyncratic volatility is consistent with the view that risk averse managers invest less in risky

activities and compensating them with options and monitoring by institutional investors has

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negative effect on managerial risk aversion. By documenting this evidence, my research

contributes to the literature on the design of executive compensation that aims to provide risk

incentives to managers. This study provides an explanation for the “undersheltering puzzle”

why some firms do not invest in tax strategies while others engage in them frequently as

documented by Weisbach (2002). Evidence documented in this study suggests that adopting

risky tax strategies increases firm-specific risk, which discourages managers to engage in tax-

reducing activities, providing a potential explanation for the under-sheltering puzzle.

While the focus of this study is to examine the effect of tax aggressiveness on

idiosyncratic volatility, I extend previous research on the link between firm characteristics and

idiosyncratic volatility. In particular, examining the effect of firm characteristics on the relation

between idiosyncratic volatility and tax aggressiveness improves our understanding of which

specific firm characteristics play a role in shaping firms’ tax strategies. Additional tests show

that the positive relation between tax aggressiveness and idiosyncratic volatility is particularly

pronounced for financially constrained firms, firms with unrated debt, younger firms, firms

that do not pay dividends, and firms that belong to technology industries (Computers,

Biotechnology, and Telecommunications). There is some evidence that firms belonging to

competitive industries, firms with less number of antitakeover provisions, and multinational

firms have stronger relation between ETRs and idiosyncratic volatility, however, results are

not statistically significant.

The remainder of the paper is organized as follows. Section 2 discusses related literature

and develops hypothesis. Section 3 explains data and research method. Section 4 presents

descriptive statistics, main empirical findings, and robustness tests. Section 5 contains

additional tests and Section 6 concludes.

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2. Related Literature and Hypothesis Development

An aggressive tax strategy increases the probability that a firm is identified by the tax

authority as involved in tax avoidance. Wilson (2009) estimates that firms, which are detected

by the IRS as involved in tax sheltering, pay approximately 50 percent of the tax savings

originally generated by the tax sheltering activities in interest and penalties to the IRS, and pay

around 8 percent of the tax savings as fees to tax shelter purveyors. The risk of paying increased

taxes in future as well as penalties and fines imposed by the IRS, will increase uncertainty

associated with future profitability of a firm, causing stock returns to become volatile.

Firms suffer reputational costs if news about firms’ involvement in tax avoidance

negatively affects investors’ assessment of firm value (Hanlon and Slemrod, 2009). The impact

of such news could be significant. For example, revelation of the tax shelters employed by

Dynegy from September 2000 to April 2002 resulted in a loss of 97 percent of its market value

(Desai and Dharamapala, 2006). Hasan et al. (2014) find that banks increase loan spread after

news about firm’s involvement in tax-sheltering become public. Evidence from these studies

reflects that investors perceive tax aggressive firms to be risky. On the other hand, several firms

do not engage in risky tax avoidance to avoid such reputational costs (Chen et al,. 2010;

Graham et al., 2014). Family firms and firms with higher labor unionization rate have lower

levels of tax avoidance (Chen et al., 2010; Chyz et al., 2013).

Another factor that suggests a positive relation between tax aggressiveness and

idiosyncratic volatility is the information asymmetry associated with tax strategies, which are

often complex and opaque. Accounting disclosure rules do not provide sufficient information

to determine a firm’s tax position (McGill and Outslay, 2004). Kim et al. (2011) argue that tax

avoidance activities allow managers to hoard negative information for long periods and when

this information is suddenly released to the stock market, it results in a stock price crash. In

order to avoid exposing their confidential tax strategies to their competitors, firms may not

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want to (and are not required to) disclose information about their tax strategies publicly (McGill

and Outslay, 2004). Complex tax strategies lead to poor information environment for a firm

thereby affecting investors’ perception about the future profitability of a firm. Providing

support to this conjecture, Beck, Lin, and Ma (2014) find that financial market development

(that is, better credit information sharing and higher branch penetration) is negatively

associated with the incidence and extent of tax evasion. They argue that in an economy with

developed financial market, information related to corporate misconduct can be more easily

observed and shared among potential lenders, making it difficult and expensive to receive

future loans, consequently increasing the opportunity costs of engaging in tax evasion.

Therefore better information sharing among lenders discourages firms to engage in tax

avoidance activities.

Based on the discussion in this section I hypothesize that aggressive tax strategies cause

firm-specific risk to increase.

3. Data and Methodology

3.1 Data

Data to estimate effective tax rate and control variables is obtained from Compustat.

Compustat started collecting tax-related data from 1987. Daily stock returns data is taken from

CRSP. Data used to estimate CEO ownership and sensitivity of CEO wealth to stock price and

stock price volatility is taken from Execucomp. Institutional holdings data come from Thomson

Reuter’s 13f filings and data on antitakeover provisions to estimate Gompers, Ishii, and

Metrick’s (2003) Governance-index (G-Index) come from Institutional Shareholder Services

(ISS).

The sample includes only those firms that report positive pretax income, firms that have

an incentive to reduce taxes. Observations with negative ETR or ETR greater than one are

removed from the sample. All financial services, real estate, utilities and government regulated

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firms are excluded (i.e., firms with SIC codes 4900-4949, 6000-6999, >9000). Only those

stocks that are listed on the NYSE, American Stock Exchange or NASDAQ are included in the

sample. After removing observations with missing data, final sample includes 31,744 firm-year

observations from 4,463 unique firms for the period 1990-2013.

3.2 Methodology

To examine the effect of a broad spectrum of tax strategies on firm-specific risk, I use

effective tax rate (ETR) as the main explanatory variable. ETR is computed as taxes paid

divided by pretax income (adjusted for special items). This variable is also referred to as cash

ETR in the extant literature. Lower values of ETR indicate that firms are taking risky tax

positions.

Following Fu (2009), I estimate idiosyncratic volatility by taking standard deviation of

residuals obtained by regressing daily excess stock returns on the three Fama and French (1993)

factors, for each stock i and for each year t, as shown in Model (1) below, and multiply that by

the square root of the number of trading days in a year for a stock.

𝑅𝑖𝜏 − 𝑟𝑓 = 𝛼𝑖𝑡 + 𝑏𝑖𝑡(𝑅𝑚𝜏 − 𝑟𝑓) + 𝑠𝑖𝑡𝑆𝑀𝐵𝜏 + ℎ𝑖𝑡𝐻𝑀𝐿𝜏 + 𝜀𝑖𝜏 … (1)

where, i represents a stock, 𝜏 is the subscript for a day, 𝑡 is the subscript for a year (𝜏 ∈ 𝑡), 𝑟𝑓

is the one-month Treasury-bill rate, and 𝑏𝑖𝑡, 𝑠𝑖𝑡 and ℎ𝑖𝑡 are the factor sensitivities. Firms with

at least 120 days of trading data during a year are included in the sample.

To test whether idiosyncratic volatility is related to tax aggressiveness, idiosyncratic

volatility is regressed on ETR and control variables as shown in Model (2). The regression is

performed using ordinary least squares (OLS) with t-statistics computed using standard errors

robust to heteroscedasticity and clustered by firm and year.

𝐼𝑉𝑂𝐿𝑖,𝑡+1 = 𝛽0 + 𝛽1𝐸𝑇𝑅𝑖,𝑡 + 𝛽2𝐿𝐸𝑉𝑖,𝑡 + 𝛽3𝑆𝐼𝑍𝐸𝑖,𝑡 + 𝛽4𝑀𝐵𝑖,𝑡 + 𝛽5𝐷𝐼𝑉𝑖,𝑡 + 𝛽6𝐴𝐺𝐸𝑖,𝑡 + 𝛽7𝐶𝐹𝑖,𝑡 + 𝛽8𝐶𝐹𝜎𝑖,𝑡 +

𝛽9𝐵𝐸𝑇𝐴𝑖,𝑡 + 𝛽10𝑅𝐸𝑇𝑖,𝑡 + 𝛽11𝑇𝑅𝑁𝑂𝑉𝑅𝑖,𝑡 + 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦𝐷𝑢𝑚𝑚𝑖𝑒𝑠 + 𝑌𝑒𝑎𝑟𝐷𝑢𝑚𝑚𝑖𝑒𝑠 + 𝜀𝑖𝑡 … (2)

where, IVOL is idiosyncratic volatility, ETR is tax paid divided by pretax income adjusted for

special items, LEV is long term debt divided by total assets, SIZE is the natural logarithm of

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the total assets, MB is the ratio of market value of equity to book value of equity, DIV is an

indicator variable that is equal to one if a firm pays dividend and zero otherwise, AGE is the

number of years for which firm’s data is available in Compustat, CF is the cash flow from

operations scaled by total assets, CFσ measures cash flow volatility, BETA is a measure of

systematic risk, RET is annual stock returns, and TRNOVR represents stock turnover. Appendix

A provides detailed construction of all variables used in this study. Industry and year dummies

control for industry (at the two-digit SIC level) and year fixed-effects. To mitigate the effect

of outliers, all continuous variables are winsorized at the 1st and 99th percentiles. The coefficient,

𝛽1, is expected to be negative, suggesting that for lower values of ETR, firm-specific risk is

higher.

4. Results

4.1 Descriptive Statistics

Table 1 presents sample distribution by Fama and French (1997) 48-industry

classifications. The number of observations reported in Table 1 are different from those used

in the multivariate regression. Since in Model (2), idiosyncratic volatility is measured in year

(t + 1) while ETR and other control variables are measured in year t, some observations are lost.

Columns (1) and (2) report the number and percentage of observations from a particular

industry in the full sample, respectively. There are 28 (14) industries each with more (less) than

one percent observations in the full sample. Industries with the maximum number of

observations are Business Services, Retail, Electronic Equipment, Machinery, and Petroleum

and Natural Gas.

[Please Insert Table 1 here]

Electronic Equipment, Fabricated Products, Computers, and Recreation are the

industries with the highest idiosyncratic volatility whereas Aircraft, Business Supplies,

Shipping Containers, and Tobacco Products have lowest idiosyncratic volatility. Industries

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with low ETRs are those that usually make large investments. For example, Petroleum and

Natural Gas has the lowest ETR (18.88 percent) followed by Transportation (20.82 percent),

Electronic Equipment (21.51 percent), and Shipping Containers (22.06 percent). Industries

with high mean ETRs include Apparel, Wholesale, Retail, Printing and Publishing, and

Tobacco Products (greater than 30 percent). Mean tax rate across industries varies between 18

to 33 percent.

Figure 1 presents the time trend in mean idiosyncratic volatility and ETR for the sample

period (1990-2014). Average idiosyncratic volatility was in the range of 41-47 during period

1990-1997, increased during period 1997-2000 and then dropped in 2001. High idiosyncratic

volatility observed during the late 1990s and early 2000s is due to the technology bubble.

Between 2002 and 2006, idiosyncratic volatility dropped and then increased for the next two

years before falling again in 2009. The sharp increase in idiosyncratic volatility in year 2008

could be attributed to the occurrence of the financial crisis. For the period 2010-2014,

idiosyncratic volatility was low and varied between 27 and 33.

[Please Insert Figure 1 here]

Overall trend in tax rates during the sample period from 1990 to 2014 is that average

ETR is declining. During 1990-1995 period average ETR varied between 29 to 32 percent while

for 1996-2001 period, the range was 26-29 percent. Compared to 1990s, tax rates were

generally low in 2000s. Between 2002 and 2014, the lowest average ETR was for year 2004

(21 percent) and highest for 2008 (26 percent).

Table 2 presents descriptive statistics and correlation coefficients for all variables used

in this study. For the full sample mean (median) ETR and idiosyncratic volatility are 26.1 (26.4)

percent 38.5 (34.2) percent, respectively. The Pearson (Spearman) correlation coefficient

between ETR and idiosyncratic volatility is -0.016 (-0.018), statistically significant at the five-

percent level. The univariate results indicate a negative relation between effective tax rates and

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idiosyncratic volatility, which is consistent with the main hypothesis. Idiosyncratic volatility is

positively correlated with cash flow volatility, and negatively correlated with leverage, firm

size, growth opportunities, dividend-payment, firm age and operating cash flow. ETR is

positively correlated with dividend-payment, and negatively correlated with leverage, firm size,

growth opportunities, cash flow from operations, market beta, stock performance and stock

turnover.

[Please Insert Table 2 here]

4.2 Multivariate Analysis

Table 3 presents results obtained by estimating Model (2). Column (1) shows that the

coefficient of ETR is negative (significant at the one-percent level), indicating that firms

adopting risky tax strategies have high idiosyncratic volatility. Specifically, a unit drop in ETR

increases idiosyncratic volatility by 0.043 units, which represents an 11.17 percentage increase

in idiosyncratic volatility relative to the mean.

[Please Insert Table 3 here]

To check whether main results are robust to alternative measures of tax aggressiveness,

I use GAAP ETR (GAAPETR) and long-run cash ETR (LCETR) in place of ETR in Model (2).

GAAPETR is measured as tax expense reported in the financial statement divided by the pretax

income (adjusted for special items). LCETR is estimated as the sum of tax paid in leading three

years divided by the sum of pretax income (adjusted for special items) over the same period.

Columns (2)-(3) of Table 3 show that the coefficients of GAAPETR and LCETR are negative

(significant at the one-percent level), confirming that results are not sensitive to alternative

measures of tax aggressiveness. Finally, to capture the possibility that the relation between

idiosyncratic volatility and tax aggressiveness is nonlinear, I regress natural log of idiosyncratic

volatility on natural log of ETR, while controlling for other variables as in Model (2). Column

(4) of Table 3 suggests that a one percentage drop in ETR is associated with a 2.3 percentage

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increase in idiosyncratic volatility. As an additional check, I use the standard deviation of

annual cash ETRs (over a five-year period) to measure firm’s tax riskiness. Results reported in

Column (5) show that idiosyncratic volatility increases as ETR volatility increases and is

consistent with results discussed earlier.

Taken together, there is a strong evidence in support of the hypothesis that effective tax

rates are negatively related to idiosyncratic volatility. The results are robust to alternative

definitions of ETRs.

4.3 Robustness Tests

4.3.1 Fixed-Effect and Random-Effect Models

To mitigate potential omitted variable bias, I re-estimate Model (2) using fixed-effect

and random-effect model, which control for the unobserved time-invariant factors. Results

reported in Columns (1)-(2) of Table 4 show that the negative relation between ETR and

idiosyncratic volatility remains highly significant, suggesting that the main results reported in

Table 3 are unlikely to be driven by omitted variables.

[Please Insert Table 4 here]

4.3.2 Fama-MacBeth Regression

In the presence of cross-sectional correlation, Fama and Macbeth (1973) method gives

unbiased standard errors and is especially suitable for asset pricing applications (Petersen,

2009). As an additional check, I perform analysis using Fama-Macbeth regression and report

results in Column (3) of Table 4. The results show that the coefficient of ETR is negative and

significant at the one-percent level. These results are consistent with the main results.

4.3.3 Two-Stage Least Squares Method (2SLS)

In this subsection I take a more formal approach to address concerns related to omitted

variable bias and reverse causality. If the degree of tax aggressiveness varies endogenously

with some omitted variables that are actually responsible for idiosyncratic volatility, then it

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would make it difficult to draw a causal inference from a positive association between tax

aggressiveness and idiosyncratic volatility. Also, there could be a potential reverse causality

from idiosyncratic volatility to tax aggressiveness. For example, high idiosyncratic volatility

may incentivize managers to undertake aggressive tax strategies to build financial slack in order

to absorb unexpected cash flow shocks. To address these concerns, I perform 2SLS regression.

In the first-stage, ETR is regressed on an instrument variable and all control variables as in

Model (2). In the second stage, Model (2) is estimated after replacing ETR with its fitted value

from the first-stage regression.

The instrument variable should be highly correlated with the endogenous variable (ETR)

and not correlated with idiosyncratic volatility. I use average ETR of local other-industry firms

as the instrument variable, that is, average ETR of firms from the same two-digit ZIP code area

but not from the same two-digit SIC industry. Economic intuition behind selecting this

instrument variable is that the tax environment will affect tax practices of firms in the same

region. Same-industry firms are excluded as firms in the same industry may have similar

idiosyncratic volatility. For example, idiosyncratic volatility of a biotechnology firm is similar

to other firms in that sector but different from that of a manufacturing or construction firm.

Therefore local other-industry average ETR (IV) is expected to be closely related to the firm’s

tax policies but does not have any significant effect on its idiosyncratic volatility.

Columns (4)-(5) of Table 4 report results for the 2SLS regression. The coefficient of IV

in Column (4) is significant (at the one-percent level), indicating that the instrument is not weak.

In Column (5), the coefficient of the fitted ETR is negative and significant (at the one-percent

level), confirming that ETR remains a statistically significant predictor of idiosyncratic

volatility even after addressing endogeneity issues. These results suggest a causal relation that

runs from tax aggressiveness to idiosyncratic volatility.

4.3.4 Quasi-Experiment

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To provide additional evidence of a direct effect of tax aggressiveness on idiosyncratic

volatility, I observe change in idiosyncratic volatility around the changes in financial reporting

and disclosure introduced by FASB Financial Interpretation No. 48 (FIN48). Prior to FIN48

there was no standard approach that was followed by firms to address uncertainty in accounting

for income taxes. This caused inconsistencies in disclosures related to unrecognized tax

benefits across firms and even for the same firm across different years. FIN48 requires all tax

positions to be evaluated for recognition and measurement using consistent criteria. Firms have

to determine whether it is “more-likely-than-not” (likelihood of more than 50 percent) that a

tax position will be sustained upon examination including resolution of any related appeals or

litigation processes based on the technical merits of the position.3 This approach requires

consideration of the facts, circumstances, and information available at the reporting date. If a

tax position does not meet the “more-likely-than-not” recognition threshold, the benefit of that

tax position is not recognized (FIN48, 2006). Firms with open uncertain tax positions are

required to provide annual disclosure of the levels and the changes in uncertain tax benefits.

FIN48 became effective for fiscal years beginning after December 15, 2006.

FIN48 does not affect all firms equally because some firms do not undertake risky tax

strategies and therefore do not have uncertain tax positions (Chen et al., 2010; Graham et al.,

2014). Such firms may not have any unrecognized tax benefits (UTB) to disclose either before

or after FIN48 is implemented while firms undertaking risky tax strategies are required to

provide full disclosure about their uncertain tax positions post-FIN48 (Hasan et al., 2014).

FIN48 provides a quasi-experiment setting to examine the change in idiosyncratic volatility

across treatment firms (firms undertaking uncertain tax positions and likely to report a UTB

post-FIN48) and control firms (not affected by FIN48). The disclosure provisions of FIN48

3 Technical merits of a tax position derive from sources of authorities in the tax law (legislation and statutes,

legislative intent, regulations, rulings, and case law) and their applicability to the facts and circumstances of the

tax position (FIN48, 2006).

16

regarding uncertain tax positions (resulting from risky tax strategies) will help investors to

estimate firms’ future tax payments/liabilities with more confidence and thus help to reduce

uncertainty about firm’s future cash flows (Ciconte et al., 2016; Drake et al., 2016). Therefore

after the implementation of FIN48 the idiosyncratic volatility that arises because of aggressive

tax strategies will reduce more for treatment firms relative to control firms. I test this conjecture

by conducting a difference-in-differences analysis.

Hasan et al. (2014) note that several firms with positive UTB are sometimes coded as

having missing UTB or a zero UTB in Compustat. For their study, the authors hand-collected

data for the period 2007-2009 from annual reports to confirm whether a firm has reported UTB

or not. The sample period for my study is longer, allowing me to observe whether UTB

reporting has improved over time. From 2007 to 2013, the number of firms (in my sample) in

Compustat having nonmissing/nonzero UTB is 621, 628, 853, 1025, 1045, 1015, and 1013,

respectively. This trend indicates that UTB reporting has improved over the last few years in

Compustat. Following Hasan et al. (2014), I define treatment firm as one for which Compustat

reports positive UTB in any of the year between 2007 and 2013. There are 1602 unique firms

that have nonmissing/nonzero UTB in my sample for at least one year during 2007-2013.

Control firms are defined as those which do not report UTB in any given year during the period

2007-2013. UTB is not reported prior to 2006.

To conduct difference-in-differences analyses, I define two dummy variables -

UTB_FIRM and POST_FIN48. UTB_FIRM equals one for treatment firms and zero otherwise,

and POST_FIN48 is one for years 2007-2013 and zero otherwise. I replace ETR in Model (2)

with UTB_FIRM, POST_FIN48, and an interaction term between these two variables. The

coefficient of the interaction term captures the difference-in-differences estimate in

idiosyncratic volatility between treatment and control firms across the two periods (before and

after implementation of FIN48). Results reported in Column (6) of Table 4 show that the

17

coefficient of the interaction term is -0.013 (significant at the five-percent level), suggesting

that idiosyncratic volatility decreases more for firms that are affected by FIN48 (treatment

firms) relative to control firms. These results are consistent with the conjecture that investors

use UTB disclosures made by treatment firms to infer about firm’s uncertain tax positions, and

their impact on future tax liabilities of a firm. The results obtained from the quasi-experiment

setting could be viewed as providing robust evidence of a causal and direct effect of tax

aggressiveness on idiosyncratic volatility.

4.3.5 Alternative Models Used to Estimate Idiosyncratic Volatility

I examine whether results are robust to alternative models used to estimate idiosyncratic

volatility, namely Carhart’s (1997) four-factor model, and value-weighted and equal-weighted

market model. The regression results obtained using these alternate estimation of idiosyncratic

volatility are reported in Columns (1)-(3) of Table 5. In all the three regressions, the coefficients

of ETR are negative and significant (at the one-percent level), confirming that the main findings

are robust to alternative models used to estimate idiosyncratic volatility.

[Please Insert Table 5 here]

4.3.6 Reduced Sample Period (1993-2013)

FASB Statement 96 (Accounting for Income Taxes), issued in 1987, provides guidelines

for reporting taxes that result from firm’s activities during the year and preceding years. In

1993, significant changes were made to the rules for accounting and reporting of income taxes.

To avoid any difference in data arising from changes in financial reporting and disclosure, I

perform analysis for a reduced sample period (1993-2013). Results for this reduced sample,

presented in Column (4) of Table 5, show that the coefficient of ETR is -0.043 (significant at

the one-percent level). These results are similar to those reported for the full sample in Column

(1) of Table 3, and confirm that the main findings are not driven by changes in the reporting of

income taxes in the post-1993 period.

18

4.3.7 Excluding Recessionary Years

The time trend discussed earlier in this section illustrates that during recessionary years

such as during late-1990s and mid-2000s, very high idiosyncratic volatility is observed. To

remove influence of recessionary years on the main findings, I modify my sample by removing

all observations belonging to years that have been identified as recessionary periods by the

National Bureau of Economic Research.4 Specifically, observations belonging to years 1990-

1991, 2001, and 2007-2009 are excluded from the sample. Results for this modified sample,

presented in Column (5) of Table 5, show that the coefficient of ETR is -0.047 (significant at

the one-percent level), comparable to that for the full sample (-0.043) (Column (1) Table 3).

These results confirm that the positive relation between tax aggressiveness and idiosyncratic

volatility is not driven by the recessionary years when idiosyncratic volatility is high and tax

rates are low.

4.3.8 Additional Control Variables

The full sample includes firms that have positive pretax income. There is a possibility

that there are some firms, which have several years of past losses but has positive pretax income

in a particular year. Thus to demonstrate that the negative relation between effective tax rates

and idiosyncratic volatility is not driven by loss making or less profitable firms, I include

additional control variables in the Model (2) to control for the underlying profitability of a firm.

This additional control variables include return-on-assets (ROA), dummy variable to indicate

if there is any tax loss carryforward (TLCF), change in tax loss carryforward (ΔTLCF), and

Whited and Wu’s (2006) measure of financial constraints (WW).5 The results presented in

Column (6) of Table 5 show that the coefficient of ROA is significant, while the coefficients of

TLCF, ΔTLCF, and WW are not significant. Even after including additional variables to control

4 This information is obtained from www.nber.org/cycles.html 5 I get similar results if Cleary’s (1999) index is used as a measure of financial constraints (results available on

request).

19

for the profitability of a firm, the coefficient of ETR is negative and significant at the one

percent level. These results are consistent with findings discussed earlier and provide robust

evidence that the observed negative relation between effective tax rates and idiosyncratic

volatility is not driven by loss making or less profitable firms.

In conclusion, the empirical results discussed in this section provide strong and

consistent evidence in support of the hypothesis that aggressive tax strategies cause firm-

specific risk. The findings are robust to alternative variable measurement and estimation

methods.

5. Additional Tests

5.1 Long-Lasting Effect

The IRS takes, on average, four years to audit tax returns filed by large corporations

and additional two-three years to resolve any appeals before final tax assessment could be

determined (White 2013). This lengthy audit process leads to years of uncertainty about firms’

tax liabilities. Hence, it can be expected that the effect of an aggressive tax strategy on firm

idiosyncratic volatility will last for several years. I investigate how far out in future aggressive

tax strategies affect idiosyncratic volatility by measuring idiosyncratic volatility (dependent

variable in Model (2)) in years (t + 3), (t + 5), (t + 7) and (t + 10). The results are presented in

Table 6.

[Please Insert Table 6 here]

Columns (1)-(3) show that the coefficients of ETR are -0.039, -0.046, and -0.034

(significant at the five-percent level or better), when idiosyncratic volatility is measured in

years (t + 3), (t + 5), and (t + 7), respectively. When idiosyncratic volatility is measured in year

(t + 10), the coefficient of ETR is not significant as shown in Column (4).6 These findings

6 Results (not reported for all years) show that the coefficients of ETR are negative and significant when

idiosyncratic volatility is measured in years (t + 1) to (t + 7) and insignificant when measured in years (t + 8) to

(t + 12).

20

indicate that tax strategies affect firms’ idiosyncratic volatility for at least seven years in the

future, and this effect diminishes after that. This evidence is consistent with the prediction that

most of the uncertainty about the tax claims are resolved by the end of approximately seventh

year. Based on this evidence, it can be concluded that tax aggressiveness has a long-lasting

effect on firm’s idiosyncratic volatility.

In the remaining section, I follow similar analysis procedure to study the effect of a

number of variables on the relation between tax aggressiveness and idiosyncratic volatility. For

each year, I divide the sample into quintiles based on variable, say X, and estimate Model (2)

for the first and fifth quintiles separately. I conduct Chow-test to examine whether the

coefficients of ETR are significantly different across sample of firms belonging to the first and

fifth quintiles. Using this approach allows me to group firms with similar characteristics and

have different intercept and slope in Model (2) for these two groups. For example, young firms

generally have more opportunities to make tax-deductible investments and therefore may have

low tax rates. Also, these firms have high idiosyncratic volatility because of uncertainty about

their future profitability (Pástor and Veronesi, 2003). On the other hand, large firms may have

less opportunities to reduce their taxes by making new investments. Therefore, observing

relation between tax aggressiveness and idiosyncratic volatility separately for firms belonging

to different quintiles based on firm size allows me to take into account the inherent differential

nature of small and large firms. I use this approach to examine the effect of different variables

on the relation between tax rates and idiosyncratic volatility.

5.2 CEO Compensation and External Monitoring

CEO Ownership

Compensating business unit managers on an after-tax basis lowers firm’s tax rate

(Phillips, 2003). However, risk averse managers will underinvest in risky tax strategies if that

causes idiosyncratic volatility to increase. Panousi and Papanikolaou (2012) find that when

21

managers own a large fraction of the firm they reduce investment in projects that increase firm-

specific risk. This is because managers can hedge exposure to systematic risk but cannot reduce

their exposure to idiosyncratic risk. Chen et al. (2010) find that family firms are less tax

aggressive than their non-family counterparts. The authors argue that family owners forego tax

benefits to avoid the non-tax cost of a potential price discount arising from minority

shareholders’ concern about family rent-seeking masked by tax avoidance activities.

Badertscher, Katz, and Rego (2013) provide similar evidence and find that firms with greater

concentration of ownership and control engage less in tax avoidance activities compared to

firms with less concentrated ownership and control. Evidence from these studies suggests that

managerial risk aversion is an important factor that may weaken the relation between tax

aggressiveness and idiosyncratic volatility.

A typical CEO is unlikely to be an expert on tax strategies but likely to understand the

competitive nature of the industry in which a firm operates and can affect firm’s tax strategies

by setting the “tone at the top” with regard to the firm’s tax activities (Dyreng et al., 2010). To

examine the effect of CEO ownership on the relation between tax aggressiveness and

idiosyncratic risk, for each year, I divide my sample into quintiles based on CEO ownership.

The results for the first and fifth quintiles are reported in Columns (1) and (2) of Table 7,

respectively. For the firms in the lowest quintile (least CEO ownership), a unit drop in ETR is

associated with 0.049 unit increase in idiosyncratic volatility whereas the coefficient of ETR is

not significant for the fifth quintile (firms with high CEO ownership). Results from the Chow-

test show that the difference between the coefficients of ETR obtained for the first and fifth

quintile firms is statistically significant (at the one-percent level). These results are consistent

with the conjecture that the relation between tax aggressiveness and idiosyncratic volatility is

stronger when CEO ownership is low.

[Please Insert Table 7 here]

22

Convexity of CEO Compensation

High sensitivity of CEO’s wealth to stock price (CEO delta) and stock price volatility

(CEO vega) encourages managers to take risky tax positions (Rego and Wilson, 2012).

Therefore idiosyncratic volatility will be more sensitive to effective tax rates at higher values

of CEO delta and CEO vega. To test this prediction, I divide my sample into quintiles based

on CEO delta and vega and follow Guay (1999) to estimate these two variables. Results

reported in Columns (3)-(6) of Table 7 show that the coefficients of ETR are -0.053 and -0.065

for firms with high CEO delta and vega (fifth quintiles), respectively and insignificant for firms

with low CEO delta and vega (first quintiles). The difference between the coefficients of ETR

for the first and fifth quintile firms (for both CEO delta and CEO vega) is statistically

significant. These results confirm that higher sensitivity of CEO wealth to stock price and stock

price volatility induces CEOs to invest in risky tax strategies, which causes idiosyncratic

volatility.

Institutional Ownership

If tax strategies help firms in increasing firm value by reducing their tax liabilities, then

shareholders would want managers to invest more in risky tax strategies. Institutional investors

being more effective monitors, compared to dispersed individual shareholders, can cause

managers to adopt specific tax strategies to reduce corporate taxes. For example, Cheng, Huang,

Li, and Stanfield (2012) find that after hedge fund interventions, firms’ effective tax rates

reduce. Thus institutional ownership will affect the relation between idiosyncratic volatility

and tax aggressiveness by reducing managerial risk aversion.

Institutional ownership data is available quarterly. For each quarter, I sum the number

of shares held by institutional investors and divide that sum by the total number of shares

outstanding. Average of these quarterly estimates for a year is used as a measure of institutional

ownership. Results for the first and fifth quintile firms based on average institutional ownership,

23

are reported in Columns (7)-(8) of Table 7. The coefficient of ETR is not significant for the

first quintile firms (low institutional ownership) while it is negative and significant (at the five-

percent level) for the fifth quintile firms (high institutional ownership). The difference between

these coefficients is statistically significant. These results are consistent with the prediction that

institutional ownership plays an important role in reducing managerial risk aversion and

induces managers to invest in risky tax strategies. These results are also in line with Chen et al.

(2010) who document that family firms, which are less tax aggressive, have higher tax rates in

the absence of institutional investors.

Antitakeover Provisions

Antitakeover provisions insulate managers from takeover threats from the corporate

control market, indicating lower shareholder rights and thus weaker external monitoring

(Gompers et al., 2003). The positive association between tax aggressiveness and idiosyncratic

volatility is expected to attenuate for firms with more antitakeover provisions.

Antitakeover provisions data is taken from ISS (formerly RiskMetrics). Before 2007,

complete data to estimate Governance-index (G-index) as used by Gompers et al. (2003) is

available. However, in 2007 ISS changed the methodology to collect data and now it collects

data only for 13 variables (out of 21 variables used by Gompers et al. (2003) to estimate G-

index).7 Therefore for 2007-2013, I use these 13 variables to construct G-index. For each year,

based on the G-index I divide my sample into two groups - firms that have G-index less than

and greater than the sample median. The results reported in Columns (9)-(10) of Table 7 show

that firms with less number of antitakeover provisions have stronger relation between ETR and

idiosyncratic volatility. However, the difference between the coefficients of ETR for firms with

less or more number of antitakeover provisions is not statistically significant. Based on these

7 See Gompers et al. (2003) for construction of G-index in detail.

24

results, it cannot be concluded that the relation between tax aggressiveness and idiosyncratic

volatility differs systematically with the number of antitakeover provisions.

5.3 Firm Characteristics

5.3.1 Financial Constraints

Financially constrained firms engage more in tax avoidance activities (Edwards,

Schwab, and Shevlin, 2016) and also have more volatile stock returns (Campbell, Hilscher, and

Szilagyi, 2008). Relative to financially sound firms, firms with financial constraints are

expected to have stronger relation between tax aggressiveness and idiosyncratic volatility.

Financially constrained firms are likely to face difficulty in adjusting their investment and other

business activities if any of their tax strategies fails to generate expected tax benefits and firms

have to pay increased taxes in future. On the other hand, financially sound firms can use internal

funds or raise capital from external capital market to deal with cash flow shock that may arise

from aggressive tax position.

For each year, full sample is divided into quintiles, separately, based on Cleary’s (1999),

and Whited and Wu’s (2006) measures of financial constraints. Results reported in Columns

(1)-(4) of Table 8 Panel A show that the coefficients of ETR are negative and significant for

the most financially constrained and insignificant for the least financially constrained firms.

The difference between the coefficients of ETR for financially constrained and unconstrained

firms is statistically significant. From this evidence, it can be inferred that the effect of tax

strategies on idiosyncratic volatility is stronger for financially constrained firms.

[Please Insert Table 8 here]

5.3.2 Competition

A competitive product market could exert a disciplining effect on managers and serve

as a substitute for high-powered incentives (Panousi and Papanikolaou, 2012). Tax

aggressiveness will increase firm-specific risk more for those firms that belong to competitive

25

industries relative to firms that belong to less competitive industries. Cash flow shock because

of penalties/fines and increased tax payments resulting from an aggressive tax strategy will

restrict firms that operate in competitive environment to invest in new projects or to come up

with new and better products than their competitors thus affecting their profitability even more.

This situation is further aggravated if news about firm’s involvement in tax avoidance creates

negative publicity about a firm. Consumers may boycott these firms and shift their demand to

other firms within an industry (The Week, 2012).

I analyze Model (2) for the first and fifth quintile firms based on the level of competition,

where competition is estimated by taking reciprocal of the Herfindahl-Hirschman index (HHI).

HHI is the sum of squares of the market share of each firm in a three-digit SIC code. The results

reported in Columns (5)-(6) of Table 8 Panel A show that the coefficient of ETR is greater in

magnitude for the fifth quintile (most competitive) than that for the first quintile firms (least

competitive) but the difference between these coefficients is not statistically significant.

Therefore it cannot be concluded that competition affects the relation between tax

aggressiveness and idiosyncratic volatility significantly.

5.3.3 Firm Age

Idiosyncratic volatility of a given firm decreases over time as a firm gets larger and

matures, and uncertainty about its profitability diminishes (Pástor and Veronesi, 2003). I divide

my full sample into quintiles based on firm age and estimate Model (2) separately for the first

(youngest firms) and fifth (oldest firms) quintiles. The results, reported in Columns (7)-(8) of

Table 8 Panel A, indicate that the effect of tax aggressiveness on idiosyncratic volatility is

stronger for younger firms and not significant for older firms. The difference in the coefficients

of ETR for the young and old firms is significant at the one-percent level. These results suggest

that the relation between tax aggressiveness and idiosyncratic volatility are more pronounced

for younger firms relative to older firms.

26

5.3.4 Credit Ratings

Firms with rated debt have greater access to the public debt market and hence are less

likely to be financially constrained relative to firms with no debt ratings. I divide firms into

two groups – those with public rated debt and those with unrated debt. The results, reported in

Columns (1)-(2) of Table 8 Panel B, show that the coefficient of ETR is significant for firms

with unrated debt and insignificant for firms with rated debt. Results from the Chow-test

suggest that the coefficients of ETR for firms with rated and unrated debt are statistically

different. From this evidence, it can be inferred that the effect of tax aggressiveness on

idiosyncratic volatility is stronger for firms with unrated debt, relative to firms with rated debt.

5.3.5 Multinational Firms versus Domestic-Only Firms

Multinationals corporations (MNC) have more opportunities to avoid taxes,

opportunities that are not available to firms with only domestic operations (domestic-only

firms). For example, MNCs can avoid taxes by locating operations/income in countries with

low tax rates. I re-estimate Model (2) separately for MNCs (firms that report foreign income)

and domestic-only firms (firms that do not report any foreign income), and present results in

Columns (3)-(4) of Table 8 Panel B. Results suggest that idiosyncratic volatility is more

sensitive to ETR for domestic-only firms relative to multinational firms. However, the results

from the Chow-test are not significant therefore it cannot be inferred that domestic-only firms

systematically have high idiosyncratic volatility in response to low effective tax rates.

5.3.6 Dividend versus Non-Dividend Paying Firms

Compared to a non-dividend paying firm, dividend-paying firm can counteract the

effect of an unexpected cash flow shock (such as from an aggressive tax position) on their

business activities by reducing dividend payments. I estimate Model (2) separately for

dividend-paying and non-dividend paying firms. The results, reported in Columns (5)-(6) of

Table 8 Panel B, show that the coefficient of ETR for non-dividend paying firms is negative

27

and significant while for dividend-paying firms it is insignificant. The results from the Chow-

test suggest that the coefficients of ETR for the dividend and non-dividend paying firms are

statistically different (at the one-percent level). Furthermore, compared to the full sample,

idiosyncratic volatility is more sensitive to tax rates for non-dividend paying firms.

5.3.7 Technology versus Non-Technology Firms

Schwert (2002) shows that the stocks listed on the Nasdaq exchange are more volatile

(compared to S&P 500 firms) and this high volatility is driven by firms belonging to technology

sector (Computer, Biotechnology, and Telecommunications).8 The author argues that in the

recent years economic boom is concentrated in the technology sector therefore any negative

news about future growth has much stronger effect on these stocks. Negative news about higher

tax payments is likely to affect technology firms more than other firms. Therefore relative to

non-technology firms, a stronger relation between tax aggressiveness and idiosyncratic

volatility is predicted for firms belonging to technology sector. Regression results obtained

from estimation of Model (2) separately for technology firms and non-technology firms

(reported in Columns (7)-(8) of Table 8 Panel B) show that a unit drop in ETR increases

idiosyncratic volatility by 7.5 percent for technology firms and 3.4 percent for non-technology

firms. The difference in the coefficients for these two subsamples is statistically significant (at

the one-percent level). These results are consistent with the expectation that idiosyncratic

volatility will increase more with tax aggressiveness for technology firms relative to non-

technology firms.

6. Conclusion

This paper demonstrates a positive causal relation between tax aggressiveness and

idiosyncratic volatility for publicly traded U.S. firms. These results are robust to various

sensitivity checks. Results from the 2SLS regression and evidence from a quasi-experiment

8 Schwert (2002) examines total volatility (and not idiosyncratic volatility).

28

setting establish a direct effect of tax aggressiveness on idiosyncratic volatility. Further analysis

suggest that the effect of aggressive tax strategies on idiosyncratic volatility lasts for long

periods extending up to seven years after which the effect diminishes. The strong results linking

idiosyncratic volatility and tax aggressiveness will be useful to the investors in understanding

cross-sectional variation in idiosyncratic volatility across firms, and help them in designing

their portfolios. Additional analyses show that the positive effect of tax aggressiveness on

idiosyncratic volatility is weaker when CEO ownership is high and stronger when CEO’s

wealth is sensitive to stock price and stock price volatility resulting from option-based

compensation. These results have implications for the design of executive compensation and

suggest that compensating managers with options encourages managers to undertake projects

that increase firm-specific risk such as investing in risky tax strategies. Evidence related to the

effect of CEO compensation on risk-taking incentives will be useful to the investors and board

in evaluating CEO performance as decisions regarding investment in tax planning are primarily

taken by managers. Institutional ownership has similar effect in reducing managerial risk

aversion and results show that the relation between tax aggressiveness and idiosyncratic

volatility is stronger when institutional ownership is high. Empirical analyses also demonstrate

that idiosyncratic volatility is more sensitive to effective tax rates for financially constrained

firms, younger firms, firms with unrated debt, non-dividend paying firms, and technology firms.

Evidence documented in this study is crucial in the current uncertain political

environment and likely tax reforms that may take place in future. Firms get tax credits and

deductions from the government, and further exploit various loopholes in the rules to avoid

taxes. In the last few years, several proposals have been made recommending changes in the

existing tax rules (Keightley and Sherlock 2014). In such a changing environment, it will

become even more difficult for investors to estimate future profitability of firms that engage

aggressively in tax avoidance activities.

29

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31

Time-Series Trend: Mean Idiosyncratic Volatility and Effective Tax Rate

Figure 1: This figure plots the mean of idiosyncratic volatility and effective tax rate over the 1990-2014 period.

Idiosyncratic volatility is estimated as the standard deviation of the residuals obtained by regressing excess stock

returns, for each stock and for each year, on the three Fama and French (1993) factors and effective tax rate is the

tax paid divided by pretax income (adjusted for special items).

32

Appendix A: Variable Definitions

Compustat variables are shown in BOLD and estimated variables are shown in ITALICS

Variable Definition

IVOL Standard deviation of the residuals obtained from the Fama and French (1993) model multiplied

by the square root of the number of trading days in a year

ETR Tax paid (TXPD) divided by pretax income (PI) adjusted for special items (SPI)

GAAPETR Reported tax expense (TXT) divided by pretax income (PI) adjusted for special items (SPI)

LCETR Tax paid (TXPD) over leading three years divided by pretax income (PI) adjusted for special

items (SPI) over the same period

LEV Long term debt (DLTT) divided by total assets (AT)

SIZE Natural log of the book value of total assets (AT)

MB Market value of equity (CSHO × PRCC_F) divided by book value of equity (CEQ)

DIV Indicator variable that is equal to one if a firm pays dividend (DVC > 0) and zero otherwise (DVC

= 0 or DVC is missing)

AGE Number of years for which a firm exists in the Compustat database

CF Cashflow from operating activities (OANCF) scaled by total assets (AT)

CFσ Standard deviation of cash flow (CF), using data for the trailing five years

BETA Market beta obtained from the Fama and French (1993) model using past 60 months of monthly

returns data

RET Annual stock returns

TRNOVR Average of monthly trading volume to shares outstanding over a year

IV Mean ETR for two-digit SIC industries (excluding firm’s industry) and two-digit ZIP code area

UTB_FIRM Indicator variable that is equal to one for firms that report unrecognized tax benefits for any year

during the period 2007-2013 and zero otherwise

POST_FIN48 One for years 2007-2013 and zero otherwise

ROA Net income (NI) divided by total assets (AT)

TLCF Indicator variable if tax-loss-carryforward is positive (TLCF > 0) and zero otherwise

ΔTLCF Change in tax loss carryforward (TLCF) divided by total assets (AT)

CEO Ownership Percentage of shares held by CEO

CEO Delta Sensitivity of CEO wealth to stock price estimated following Guay (1999)

CEO Vega Sensitivity of CEO wealth to stock price volatility estimated following Guay (1999)

Institutional

Ownership

Shares held by institutional investors divided by shares outstanding at the end of each quarter. I

take average of this quarterly estimates

G-Index

ISS provides Governance-index (G-index) as calculated by Gompers et al. (2003) for the period

1990-2006. For years 2007-2014, I estimate G-index based on 13 antitakeover provisions for

which data is provided by ISS

Cleary (1999) Index

-0.119ACT/LCT – 1.904DLTT/AT + 0.001COVERAGE + 1.456PROFIT_MARGIN +

2.036SGRTH – 0.048SLACK

where, COVERAGE = ((OIBDP – DP)/(XINT + DVP))/(1 - TAX_RATE)

TAX_RATE = TXT/(OIBDP – DP - XINT)

PROFIT_MARGIN = IB/REVT

SLACK = (CHE + 0.5INVT + 0.7RECT - DPACT)/PPENT

SGRTH is the annual sales growth rate of a firm

Whited and Wu

(2006) Index

(WW)

-0.091OANCF/AT – 0.062DIV + 0.021DLTT/AT – 0.044ln(AT) + 0.102INDGROWTH –

0.025SGRTH

where, INDGROWTH is the industry sales growth rate, SGRTH is the firm’s annual sales growth

rate, and DIV as described earlier in this table

Competition Reciprocal of Herfindahl-Hirschman index, which is estimated by summing squares of the market

shares of firms in the industry (at the 3-digit SIC level)

Credit Rating Indicator variable that is equal to one for firms with public rated debt and zero otherwise

Multinational Firms Firms that report foreign-income (PIFO > 0)

Technology Firms Firms belonging to industries - Computers, Biotechnology and Telecommunications

33

Table 1: Distribution of Sample Firms by Industry Columns (1) and (2) present, respectively, the number and percentage of observations from a particular industry in the full

sample. Columns (3) and (4) report mean idiosyncratic volatility (IVOL) and mean effective tax rate (ETR), respectively.

Idiosyncratic volatility is estimated as the standard deviation of the residuals obtained by regressing excess stock returns

on the three Fama and French (1993) factors multiplied by the number of trading days in a year, for each stock and for

each year, and ETR is the tax paid divided by the pretax income adjusted for special items. The sample period for this

study is from 1990 to 2014.

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

Industry Name N Full Sample (%)

Mean

Idiosyncratic Volatility (%) Mean ETR (%)

Business Services 5134 12.42 45.26 25.14

Retail 2967 7.18 40.76 31.13

Electronic Equipment 2910 7.04 48.99 21.51

Machinery 2093 5.06 39.61 27.21

Petroleum and Natural Gas 1987 4.81 38.54 18.88

Wholesale 1951 4.72 42.84 30.41

Computers 1634 3.95 47.46 23.54

Medical Equipment 1562 3.78 44.85 24.76

Transportation 1413 3.42 36.93 20.82

Pharmaceutical Products 1381 3.34 40.19 23.82

Measuring and Control Equipment 1267 3.07 44.32 25.76

Chemicals 1207 2.92 32.87 26.48

Communication 1143 2.77 32.68 26.00

Food Products 1110 2.69 34.25 27.78

Construction Materials 1065 2.58 35.66 28.80

Restaurants, Hotels, Motels 982 2.38 38.27 26.10

Consumer Goods 941 2.28 39.10 29.49

Healthcare 935 2.26 46.14 26.52

Automobiles and Trucks 889 2.15 36.69 29.35

Electrical Equipment 804 1.95 40.64 28.00

Apparel 792 1.92 41.61 30.38

Business Supplies 752 1.82 30.70 26.66

Steel Works 724 1.75 39.86 28.18

Personal Services 621 1.50 40.07 26.50

Entertainment 603 1.46 41.99 26.70

Construction 597 1.44 41.04 29.38

Rubber and Plastic Products 463 1.12 43.68 27.95

Printing and Publishing 437 1.06 32.16 32.46

Recreation 332 0.80 47.13 28.34

Aircraft 313 0.76 31.85 24.51

Non-Metallic and Metal Mining 253 0.61 35.65 22.95

Beer & Liquor 242 0.59 32.29 26.57

Textiles 199 0.48 39.63 29.25

Shipping Containers 182 0.44 30.16 22.06

Candy & Soda 175 0.42 33.58 26.43

Precious Metals 156 0.38 47.27 22.59

Agriculture 150 0.36 36.67 23.78

Defense 146 0.35 35.77 23.94

Fabricated Products 145 0.35 47.67 22.72

Shipbuilding, Railroad Equipment 113 0.27 34.38 29.14

Tobacco Products 86 0.21 23.00 32.85

Other 470 1.14 44.55 23.64

34

Table 2: Descriptive Statistics and Correlations

Panels A and B present descriptive statistics and correlation matrix, respectively. IVOL is the standard deviation of the

residuals obtained from the Fama and French (1993) model multiplied by the number of trading days in a year, ETR is tax

paid divided by pretax income adjusted for special items, LEV is long term debt divided by total assets, SIZE is the natural

log of the book value of total assets, MB is market value of equity divided by book value of equity, DIV is an indicator

variable that is equal to one if a firm pays dividend and zero otherwise, AGE is the number of years a firm exists in the

Compustat database, CF is cash flow from operating activities scaled by total assets, CFσ is standard deviation of CF,

using data for the trailing five years, BETA is market beta, RET is annual stock returns, and TRNOVR is average monthly

trading volume to shares outstanding over a year. All continuous variables are winsorized at the 1st and 99th

percentile. In Panel B, Pearson (Spearman) correlations are presented above (below) the diagonal. Significant

coefficients (at the 5% level) are shown in bold.

Panel A: Descriptive Statistics

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

Variables Mean 25th Percentile Median 75th Percentile Std Dev

IVOL 0.385 0.248 0.342 0.479 0.188

ETR 0.261 0.150 0.264 0.356 0.152

LEV 0.158 0.006 0.126 0.256 0.158

SIZE 6.361 4.972 6.274 7.636 1.924

MB 3.053 1.512 2.291 3.602 2.697

DIV 0.502 0 1 1 0.500

AGE 9.603 5 8 13 5.410

CF 0.119 0.071 0.113 0.161 0.076

CFσ 0.048 0.022 0.037 0.062 0.038

BETA 1.022 0.605 0.978 1.388 0.723

RET 0.258 -0.074 0.159 0.446 0.551

TRNOVR 1.385 0.455 0.969 1.861 1.321

Panel B: Correlation Matrix

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

IVOL (1) -0.018 -0.173 -0.597 -0.120 -0.418 -0.389 -0.07 0.33 0.035 -0.007 0.000

ETR (2) -0.016 -0.073 -0.079 -0.049 0.151 0.008 -0.07 0.02 -0.084 -0.097 -0.159

LEV (3) -0.123 -0.089 0.354 -0.097 0.115 0.021 -0.20 -0.24 0.022 -0.023 -0.065

SIZE (4) -0.575 -0.064 0.284 0.174 0.337 0.360 0.02 -0.36 0.133 -0.024 0.310

MB (5) -0.047 -0.069 -0.018 0.118 0.047 0.028 0.36 0.01 -0.002 0.272 0.257

DIV (6) -0.393 0.138 0.070 0.342 0.019 0.251 0.07 -0.21 -0.095 -0.037 -0.211

AGE (7) -0.380 0.005 -0.003 0.360 0.009 0.266 0.07 -0.13 -0.065 -0.026 0.171

CF (8) -0.077 -0.092 -0.164 0.018 0.335 0.059 0.057 0.06 -0.040 0.082 0.119

CFσ (9) 0.312 0.001 -0.199 -0.341 0.066 -0.216 -0.180 0.058 0.037 0.026 0.064

BETA (10) 0.010 -0.070 0.015 0.104 -0.008 -0.092 -0.067 -0.027 0.037 0.035 0.233

RET (11) 0.109 -0.117 -0.037 -0.091 0.221 -0.099 -0.075 0.076 0.084 0.042 0.049

TRNOVR (12) 0.038 -0.137 -0.078 0.209 0.180 -0.206 0.102 0.150 0.091 0.201 0.095

35

Table 3: Relation between Effective Tax Rates and Idiosyncratic Volatility This table presents OLS results obtained by regressing idiosyncratic volatility (IVOL), on ETR and other control variables.

IVOL is the standard deviation of the residuals obtained from the Fama and French (1993) model multiplied by the number

of trading days in a year, ETR (GAAPETR) is tax paid (reported tax expense) divided by pretax income adjusted for special

items, LCETR is long-run ETR, LEV is long term debt divided by total assets, SIZE is the natural log of the book value of

total assets, MB is market value of equity divided by book value of equity, DIV is an indicator variable that is equal to one

if a firm pays dividend and zero otherwise, AGE is the number of years a firm exists in the Compustat database, CF is cash

flow from operating activities scaled by total assets, CFσ is standard deviation of CF, using data for the trailing five years,

BETA is market beta, RET is annual stock returns, and TRNOVR is average monthly trading volume to shares outstanding

over a year. *, **, *** Indicate statistical significance at the 0.10, 0.05, and 0.01 levels, respectively (two-tailed tests). t-

Statistics (in parentheses) are calculated based on heteroscedasticity-robust standard errors clustered by firm and year. All

continuous variables are winsorized at the 1st and 99th percentile.

Dependent

Variable →

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

IVOL IVOL IVOL Ln (IVOL) IVOL

ETR -0.043

(-4.389)***

CETRσ 0.057

(3.893)***

GAAPETR -0.064

(-6.141)***

LCETR -0.084

(-5.269)***

Ln(ETR) -0.023

(-5.943)***

LEV 0.030 0.033 0.022 0.114 0.034

(2.461)** (2.663)*** (1.669)* (3.408)*** (2.847)***

SIZE -0.046 -0.047 -0.046 -0.127 -0.046

(-18.336)*** (-18.590)*** (-16.852)*** (-19.193)*** (-18.416)***

MB -0.002 -0.001 -0.002 -0.011 -0.001

(-1.813)* (-1.755)* (-2.370)** (-4.585)*** (-1.555)

DIV -0.047 -0.048 -0.046 -0.114 -0.048

(-8.232)*** (-8.245)*** (-7.540)*** (-10.002)*** (-8.250)***

AGE -0.003 -0.003 -0.003 -0.011 -0.003

(-6.499)*** (-6.683)*** (-5.717)*** (-9.536)*** (-6.878)***

CF -0.208 -0.197 -0.181 -0.334 -0.196

(-10.488)*** (-10.008)*** (-8.957)*** (-7.367)*** (-9.792)***

CFσ 0.386 0.382 0.391 0.815 0.370

(8.007)*** (7.942)*** (7.208)*** (8.215)*** (7.501)***

BETA 0.004 0.004 0.003 0.029 0.004

(1.978)** (1.918)* (1.612) (4.728)*** (1.976)**

RET 0.020 0.021 0.020 0.059 0.021

(3.109)*** (3.331)*** (3.206)*** (3.396)*** (3.264)***

TRNOVR 0.018 0.018 0.016 0.080 0.018

(7.336)*** (7.532)*** (6.287)*** (12.164)*** (7.387)***

Constant 0.666 0.673 0.672 -0.367 0.646

(24.375)*** (25.004)*** (24.181)*** (-6.212)*** (23.693)***

Year Effects Yes Yes Yes Yes Yes

Industry Effects Yes Yes Yes Yes Yes

N 31,744 31,744 25,281 31,261 31,669

R2 0.564 0.565 0.558 0.649 0.564

36

Table 4: Alternative Model Specifications This table presents results obtained by regressing idiosyncratic volatility (IVOL), on ETR and other control variables, using

different model specifications. IVOL is the standard deviation of the residuals obtained from the Fama and French (1993) model

multiplied by the number of trading days in a year, ETR is tax paid divided by pretax income adjusted for special items, LEV is

long term debt divided by total assets, SIZE is the natural log of the book value of total assets, MB is market value of equity

divided by book value of equity, DIV is an indicator variable that is equal to one if a firm pays dividend and zero otherwise,

AGE is the number of years a firm exists in the Compustat database, CF is cash flow from operating activities scaled by total

assets, CFσ is standard deviation of CF, using data for the trailing five years, BETA is market beta, RET is annual stock returns,

and TRNOVR is average monthly trading volume to shares outstanding over a year. UTB_FIRM is equal to one for firms that

report unrecognized tax benefits and zero otherwise, POST_FIN48 is equal to one for years 2006-2013 and zero otherwise, and

IV is the mean ETR for two-digit SIC industries (excluding firm’s industry) and two-digit ZIP code area. *, **, *** Indicate

statistical significance at the 0.10, 0.05, and 0.01 levels, respectively (two-tailed tests). t-Statistics (in parentheses) are calculated

based on heteroscedasticity-robust standard errors clustered by firm and year. All continuous variables are winsorized at the 1st

and 99th percentile. (1) (2) (3) (4) (5) (6)

Fixed-

Effects Random-Effects Fama-MacBeth

2SLS Regression Quasi-

Experiment 1st Stage 2nd Stage

ETR -0.031 -0.034 -0.031 -0.704 (-4.391)*** (-5.144)*** (-4.669)*** (-2.931)***

UTB_FIRM -0.024

(-6.586)***

UTB_FIRM*

POST_FIN48

-0.013

(2.116)**

POST_FIN48 -0.039

(-6.243)***

LEV 0.080 0.055 0.020 -0.093 -0.031 0.033

(6.908)*** (6.071)*** (2.023)* (-8.590)*** (-1.248) (2.693)***

SIZE -0.044 -0.051 -0.045 -0.003 -0.048 -0.045

(-

16.180)*** (-45.918)*** (-18.627)*** (-2.954)*** (-32.893)*** (-17.399)***

MB 0.001 0.000 -0.002 -0.001 -0.002 -0.001

(2.156)** (0.562) (-3.729)*** (-1.997)** (-3.729)*** (-1.485)

DIV -0.031 -0.040 -0.046 0.032 -0.026 -0.050

(-7.473)*** (-12.392)*** (-7.513)*** (10.363)*** (-2.940)*** (-8.644)***

AGE 0.002 -0.002 -0.006 0.004 -0.001 -0.003

(1.467) (-7.149)*** (-10.176)*** (11.124)*** (-0.603) (-6.097)***

CF -0.143 -0.170 -0.150 -0.159 -0.315 -0.198

(-8.748)*** (-11.527)*** (-12.088)*** (-8.891)*** (-7.355)*** (-10.041)***

CFσ 0.112 0.197 0.361 0.060 0.426 0.369

(3.122)*** (6.395)*** (9.399)*** (1.780)* (9.912)*** (7.852)***

BETA -0.004 -0.002 0.006 -0.005 0.001 0.004

(-2.256)** (-1.044) (3.667)*** (-3.071)*** (0.417) (1.933)*

RET 0.011 0.012 0.009 -0.028 0.002 0.022

(7.210)*** (8.367)*** (1.173) (-16.449)*** (0.352) (3.440)***

TRNOVR 0.011 0.015 0.019 -0.003 0.016 0.018

(9.705)*** (16.091)*** (8.225)*** (-2.914)*** (11.585)*** (7.707)***

IV 0.112 (4.661)***

Constant 0.694 0.741 0.719 0.265 0.856 0.646 (41.049)*** (82.693)*** (26.998)*** (9.730)*** (11.203)*** (22.318)***

Firm Effects Yes Yes - - - -

Year Effects Yes Yes - Yes Yes Yes

Industry Effects - - - Yes Yes Yes

N 31,744 31,744 31,744 31,400 31,400 31,744

R2 0.433 0.485 0.157 0.324 0.567

37

Table 5: Other Robustness Tests This table presents OLS results obtained by regressing idiosyncratic volatility (IVOL), on ETR and other control variables.

In Columns (1)-(3), IVOL is estimated using Carhart (1997) four factor model, value-weighted market model and equal-

weighted market model, respectively, and in Columns (4)-(6) using Fama and French (1993) three-factor model. ETR is tax

paid divided by pretax income adjusted for special items, LEV is long term debt divided by total assets, SIZE is the natural

log of the book value of total assets, MB is market value of equity divided by book value of equity, DIV is an indicator

variable that is equal to one if a firm pays dividend and zero otherwise, AGE is the number of years a firm exists in the

Compustat database, CF is cash flow from operating activities scaled by total assets, CFσ is standard deviation of CF, using

data for the trailing five years, BETA is market beta, RET is annual stock returns, TRNOVR is average monthly trading

volume to shares outstanding over a year. ROA is return-on-assets, TLCF is equal to one if a firm reports tax loss

carryforward, ΔTLCF is the change in tax loss carryforward, and WW is the Whited and Wu’s (2006) measure of financial

constraints. *, **, *** Indicate statistical significance at the 0.10, 0.05, and 0.01 levels, respectively (two-tailed tests). t-

Statistics (in parentheses) are calculated based on heteroscedasticity-robust standard errors clustered by firm and year.

(1) (2) (3) (4) (5) (6)

Alternative Estimates of Idiosyncratic Volatility

Carhart

(1997) Four-

Factor Model

Value-

Weighted

Market Model

Equal-

Weighted

Market model

Sample Period

1993-2014

Excluding

Recessionary

Years

Additional

Control

Variables

ETR -0.043 -0.043 -0.043 -0.043 -0.047 -0.032

(-4.422)*** (-4.208)*** (-4.235)*** (-3.864)*** (-4.268)*** (-3.274)***

LEV 0.030 0.029 0.023 0.021 0.013 0.020

(2.480)** (2.259)** (1.872)* (1.759)* (1.179) (1.457)

SIZE -0.047 -0.047 -0.045 -0.044 -0.045 -0.048

(-18.466)*** (-18.526)*** (-17.797)*** (-17.691)*** (-15.966)*** (-11.314)***

MB -0.002 -0.001 -0.001 -0.001 -0.001 -0.000

(-1.885)* (-1.489) (-1.008) (-1.487) (-1.534) (-0.390)

DIV -0.047 -0.049 -0.048 -0.043 -0.049 -0.047

(-8.226)*** (-8.386)*** (-8.224)*** (-7.041)*** (-8.142)*** (-6.661)***

AGE -0.003 -0.003 -0.003 -0.004 -0.003 -0.003

(-6.426)*** (-6.369)*** (-6.281)*** (-7.627)*** (-6.194)*** (-4.744)***

CF -0.209 -0.214 -0.206 -0.213 -0.201 -0.155

(-10.540)*** (-10.397)*** (-10.256)*** (-9.932)*** (-10.550)*** (-7.245)***

CFσ 0.385 0.382 0.378 0.398 0.368 0.446

(8.057)*** (8.102)*** (8.070)*** (7.620)*** (7.341)*** (7.969)***

BETA 0.004 0.004 0.004 0.004 0.004 0.005

(2.014)** (2.155)** (1.975)** (1.941)* (1.646)* (1.757)*

RET 0.020 0.022 0.022 0.025 0.024 0.024

(3.095)*** (3.202)*** (3.252)*** (3.764)*** (3.245)*** (3.436)***

TRNOVR 0.018 0.019 0.019 0.017 0.020 0.019

(7.199)*** (7.262)*** (7.129)*** (6.468)*** (7.260)*** (6.765)***

ROA -0.164

(-3.950)***

TLCF 0.000

(0.056)

ΔTLCF -0.005

(-0.197)

WW -0.106

(-1.343)

Constant 0.666 0.671 0.662 0.625 0.652 0.629

(24.422)*** (25.084)*** (24.734)*** (21.999)*** (26.294)*** (21.953)***

Year FE Yes Yes Yes Yes Yes Yes

Industry FE Yes Yes Yes Yes Yes Yes

N 31,744 31,744 31,744 27,656 25,165 24,742

R2 0.565 0.566 0.558 0.580 0.581 0.567

38

Table 6: Long-Lasting Effect of Effective Tax Rates on Idiosyncratic Volatility This table presents OLS results obtained by regressing idiosyncratic volatility (IVOL), on ETR and other control variables.

IVOL is the standard deviation of the residuals obtained from the Fama and French (1993) model multiplied by the number

of trading days in a year. In Columns (1)-(4), IVOL is measured in year (t + 3), (t + 5), (t + 7), and (t + 10), and all independent

variables are measured in year t. ETR is tax paid divided by pretax income adjusted for special items, LEV is long term debt

divided by total assets, SIZE is the natural log of the book value of total assets, MB is market value of equity divided by

book value of equity, DIV is an indicator variable that is equal to one if a firm pays dividend and zero otherwise, AGE is the

number of years a firm exists in the Compustat database, CF is cash flow from operating activities scaled by total assets,

CFσ is standard deviation of CF, using data for the trailing five years, BETA is market beta, RET is annual stock returns,

and TRNOVR is average monthly trading volume to shares outstanding over a year. *, **, *** Indicate statistical significance

at the 0.10, 0.05, and 0.01 levels, respectively (two-tailed tests). t-Statistics (in parentheses) are calculated based on

heteroscedasticity-robust standard errors clustered by firm and year. All continuous variables are winsorized at the 1st and

99th percentile. *, **, *** Indicate statistical significance at the 0.10, 0.05, and 0.01 levels, respectively (two-tailed tests). t-

Statistics (in parentheses) are calculated based on heteroscedasticity-robust standard errors clustered by firm and year. (1) (2) (3) (4)

IVOL (t + 3) IVOL (t + 5) IVOL (t + 7) IVOL (t + 10)

ETR -0.039 -0.046 -0.034 -0.023

(-3.061)*** (-3.248)*** (-2.376)** (-1.340)

LEV 0.026 0.033 0.041 0.045

(1.984)** (2.763)*** (3.491)*** (3.211)***

SIZE -0.043 -0.040 -0.037 -0.036

(-18.419)*** (-17.929)*** (-20.412)*** (-15.133)***

MB -0.001 -0.001 -0.002 -0.004

(-1.377) (-2.139)** (-3.564)*** (-5.298)***

DIV -0.042 -0.034 -0.028 -0.017

(-7.005)*** (-5.264)*** (-4.027)*** (-2.719)***

AGE -0.002 -0.002 -0.002 -0.002

(-4.450)*** (-2.912)*** (-2.342)** (-2.024)**

CF -0.194 -0.177 -0.181 -0.114

(-8.907)*** (-6.840)*** (-6.026)*** (-4.475)***

CFσ 0.359 0.330 0.278 0.192

(6.275)*** (6.734)*** (5.332)*** (3.341)***

BETA 0.002 0.002 -0.002 0.002

(0.699) (0.929) (-0.684) (0.533)

RET 0.006 0.003 0.005 0.000

(1.237) (0.631) (1.405) (0.112)

TRNOVR 0.012 0.008 0.005 0.004

(4.528)*** (2.593)*** (1.526) (1.884)*

Constant 0.616 0.547 0.541 0.673

(23.916)*** (19.702)*** (20.812)*** (40.278)***

Year Effects Yes Yes Yes Yes

Industry Effects Yes Yes Yes Yes

N 21,690 17,114 14,013 9,881

R2 0.506 0.480 0.481 0.423

39

Table 7: Effect of CEO Compensation and External Monitoring on the Relation between Idiosyncratic Volatility and Effective Tax Rates

This table presents OLS results obtained by regressing idiosyncratic volatility (IVOL), on ETR and other control variables. IVOL is the standard deviation of the residuals obtained from the

Fama and French (1993) model, ETR is tax paid divided by pretax income adjusted for special items. All control variables are same as used in the main analysis and are not shown here for

brevity. *, **, *** Indicate statistical significance at the 0.10, 0.05, and 0.01 levels, respectively (two-tailed tests). t-Statistics (in parentheses) are calculated based on heteroscedasticity-robust

standard errors clustered by firm and year. Full sample is divided into quintiles based on CEO ownership, CEO delta, CEO vega, and institutional ownership, and results are reported for the

first and fifth quintile firms. In the last two columns, full sample is divided into two groups based on Governance-index - greater than (less than) sample median. Results obtained from the

Chow-test indicate whether the difference between the coefficients of ETR (DIFFERENCE) is statistically significant across the two subsamples (first and fifth quintiles) or across two groups.

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)

CEO Ownership CEO Delta CEO Vega Institutional Ownership

Number of Antitakeover

Provisions

Q1 Q5

Q1 Q5

Q1 Q5

Q1 Q5

Below

Median

Above

Median

ETR -0.049 0.004 0.009 -0.053 -0.010 -0.065 0.011 -0.031 -0.037 -0.026

(-2.770)*** (0.220) (0.425) (-2.998)*** (-0.484) (-3.521)*** (0.577) (-2.457)** (-2.981)*** (-2.022)**

Obs 2,487 2,469 2,553 2,532 2,552 2,534 5,767 5,748 8,887 5,387

R2 0.721 0.611 0.601 0.741 0.620 0.727 0.511 0.615 0.627 0.589

DIFFERENCE 0.053*** 0.062** 0.055* 0.042* 0.063

F-Statistics 9.99 6.02 3.13 3.08 0.07

p-value 0.0016 0.0142 0.0770 0.0794 0.7906

Control

Variables

Yes Yes

Yes Yes Yes Yes

Yes Yes Yes Yes

Constant Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Year FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Industry FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

40

Table 8: Effect of Firm Characteristics on the Relation between Idiosyncratic Volatility and Effective Tax Rates This table presents OLS results obtained by regressing idiosyncratic volatility (IVOL), on ETR and other control variables. IVOL is the standard deviation of the residuals obtained from the Fama

and French (1993) model and ETR is the tax paid divided by pretax income adjusted for special items. All control variables are same as used in the main analysis and are not shown here for brevity.

*, **, *** Indicate statistical significance at the 0.10, 0.05, and 0.01 levels, respectively (two-tailed tests). t-Statistics (in parentheses) are calculated based on heteroscedasticity-robust standard

errors clustered by firm and year. The full sample is divided into quintiles based on variable X and Panel A reports results for the first and fifth quintile. In Panel B, full sample is divided into two

groups based on variable Y. Results obtained from the Chow-test indicate whether the difference between the coefficients of ETR (DIFFERENCE) is statistically significant across the two subsamples

(first and fifth quintiles) or across two groups.

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

Panel A: Effect of Variable X on the Relation between Idiosyncratic Volatility and Effective Tax Rates

Variable X → Cleary (1999) Index Whited and Wu (2006) Index Competition Firm Age

Q1 Q5 Q1 Q5 Q1 Q5 Q1 Q5

ETR -0.018 -0.042 -0.015 -0.030 -0.042 -0.074 -0.035 -0.026

(-0.994) (-1.826)* (-1.191) (-1.789)* (-2.589)*** (-4.100)*** (-2.185)** (-1.499)

N 4,223 4,205 4,969 4,949 6,452 5,531 8,092 4,414

R2 0.535 0.593 0.625 0.462 0.542 0.571 0.520 0.589

DIFFERENCE 0.024*** 0.015*** 0.032 0.009***

F-Statistics 13.23 9.70 0.52 8.86

p-value 0.0003 0.0018 0.4693 0.0029

Panel A: Effect of Variable Y on the Relation between Idiosyncratic Volatility and Effective Tax Rates

Variable Y → Firms with

Rated Debt

Firms with

Unrated Debt

Multinational

Firms

Domestic-Only

Firms

Dividend Paying

Firms

Non-Dividend

Paying Firms

Technology

Firms

Non-Technology

Firms

ETR -0.008 -0.051 -0.033 -0.044 -0.008 -0.053 -0.075 -0.034

(-0.873) (-4.542)*** (-3.254)*** (-3.229)*** (-0.941) (-3.512)*** (-3.242)*** (-3.559)***

N 10,376 21,368 14,452 17,292 15,944 15,800 5,965 25,779

R2 0.607 0.516 0.591 0.527 0.492 0.529 0.590 0.561

DIFFERENCE 0.043*** 0.011 0.045*** 0.041***

F-Statistics 9.61 0.69 11.03 6.84

p-value 0.0019 0.4066 0.0009 0.0089

Control

Variables Yes Yes Yes Yes Yes Yes Yes Yes

Constant Yes Yes Yes Yes Yes Yes Yes Yes

Year FE Yes Yes Yes Yes Yes Yes Yes Yes

Industry FE Yes Yes Yes Yes Yes Yes Yes Yes