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: [email protected]), 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.
1
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).
3
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
6
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
8
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
9
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
10
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
11
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
12
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
13
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
14
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
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