FINANCIAL INSTRUMENTS USAGE AND STRATEGIC EARNINGS
REPORTING – THE COMPLEMENT HYPOTHESIS EXAMINATION
Hung-Shu Fan, Fu Jen Catholic University, Taiwan
acct1008@mails.fju.edu.tw, *corresponding author
Ching-Lung Chen, National Yunlin University of Science and Technology, Taiwan
clchen@yuntech.edu.tw
Chia-Ying Ma, Soochow University, Taiwan
cyma@scu.edu.tw
Yan-Ting Lin, Fu Jen Catholic University, Taiwan
067670@mail.fju.edu.tw
ABSTRACT
The purpose of this study is to examine whether listed firms in Taiwan use discretionary
accruals and financial instruments regulated under Statement of Financial Accounting
Standards No.34 as complement tools in reporting earnings. We estimate a set of
simultaneous equations that captures managers' incentives to enhance earnings performance
through financial instruments usage and accrual management. The empirical results of
two-stage least squares regressions reveal that, as conjectured, the financial instruments
usage is positive associated with the magnitude of discretionary accruals and support the
complement hypothesis. It suggests that managers jointly use financial instruments and
discretionary accruals to increase earnings and, besides the risk hedging purpose, provides
alternative explanation for the multi-goals of financial instruments usage. These results
remain robust to various specification tests.
Keywords: Complement hypothesis, Discretionary accruals, Earnings management,
Financial instruments usage,
INTRODUCTION
The participants in financial derivatives market include hedgers, speculators and arbitrageurs.
Hedgers use financial instruments to lock-in prices in order to avoid exposure to adverse
movements in the price of an asset. Speculators take short-term positions in the market and
consider derivatives as instruments of profit making, instead of as risk hedging tools. The
arbitrageurs enter simultaneously into contracts in two or more markets to lock-in riskless
profit by exploiting price differentials or imbalances.
Nonetheless, how financial
instruments are actually used by non-financial firms is still largely unknown in the literatures.
Although firms can use financial instruments to speculate and/or to arbitrage on private
information, extant empirical studies highlight the risk hedging motive and ignore the
important roles of the other two participants.
The purpose of this study is to examine the possible complement relationship between the
financial instruments usage and aggressive discretionary accruals in enhancing earnings
(hereafter, the complement hypothesis) for listed firms who recognized the usage of financial
instruments regulated under Statement of Financial Accounting Standards (SFAS) No.34
(Financial Accounting Standards Board 2004) in Taiwan. Before the enactment of SFAS
No.34, firms only disclosed some of the financial instruments information in the footnotes
and these disclosures were not uniform. Therefore, there was a lack of visibility or
transparency for financial instruments usage. Because SFAS No. 34 requires firms to
recognize all financial instruments as assets or liabilities in the balance sheet and also
requires them to adopt mark to market approach to recognize the resulting gains or losses
from the changes of financial instruments’ fair value at the end of fiscal year in their income
statements. Thus, the magnitude of financial instruments usage to some extent would have
an effect on listed firms’ reported earnings. Rather, the active usage of financial instruments
is thought to add value by alleviating a variety of market imperfections through hedging
(Adam & Fernando, 2006). Recently, Gèczy et al. (2007) suggest that financial derivatives
involve real action which plays an important role in strategically boosting firm’s earnings and
view speculation as a positive profitability activity. Inspired by the study of Gèczy et al.
(2007), the present study is motivated to examine whether listed firms jointly use financial
instruments and discretionary accruals to manipulate their earnings.
Since earnings have two components, cash flows from real operations and accruals, the
earnings reporting choices can result from cash flows and/or accruals. Facing increasing
pressure to meet benchmark earnings, managers have incentives to use both discretionary
accounting choices and real operation activities, such as using financial instruments, to
manipulate earnings. In other words, except using the discretionary accrual, managers also
can manipulate earnings through purchase and/or sell financial instruments. Recently,
Gèczy et al. (2007) suggest that financial derivatives can be used to hedge market risk
exposures or to speculate on movements in the value of the underlying asset. The authors
argue that the speculation hypothesis generally implies that the derivative transactions are
undertaken with the primary intention of making a profit. Adam & Fernando (2006) also
document that gold mining firms have consistently realized economically significant cash
flow gains from their derivatives transactions. Thus, if managers rather use financial
instruments to increase earnings (and/or cash flow) but to smooth earnings, the substitution
relationship between financial instruments usage and discretionary accruals is called for
further examinations.
Recent studies document that managers’ market views influence their attempting to time
commodity markets with derivatives usage decisions (Stulz, 1996; Faulkender, 2005; Brown
et al., 2006; Adam & Fernando, 2006). Stulz (1996) calls this practice “selective hedging”.
He claims that selective hedging will increase shareholder value if managers have an
informational advantage relative to other market participants. Thus, it is possible that
managers believe that they can create shareholder value by incorporating speculative
elements into their financial instruments usage decisions. Hitherto, many studies view
discretionary accruals as the primary tools to manipulate earnings. However, managers also
can increase earnings by using financial instruments that boost their firms’ cash flows.
Once managers believe they have a comparative information advantage relative to the market
and, hence, to view speculation as a positive profitability activity, the magnitude of financial
instruments usage will be enlarged. In such case, financial instruments usage will play an
important role in strategically boosting firm’s earnings. It is expected that both the financial
instruments usage and aggressive discretionary accruals play important roles in elucidating
firms’ income-increase reporting behaviors. Inspired by the studies of Adam & Fernando
(2006), Gèczy et al. (2007) and among others which view speculation as a positive
profitability activity, the present study examine the possible complement relationship
between the financial instruments usage and aggressive discretionary accruals in enhancing
earnings for listed firms with active financial instruments usage under SFAS No.34 in
Taiwan.
The empirical results of the two-stage least squares regression indicate that, as conjectured,
financial instruments usage is positive association with the magnitude of discretionary
accruals and supports the complement hypothesis. It suggests that managers use financial
instruments and discretionary accruals jointly to increase earnings, which is inconsistent with
the studies of Barton (2001) and Wang & Kao (2005). Reporting requirements on financial
instruments usage may potentially affect firms’ behavior with respect to production and risk
management, and thus the derivative instruments usage. Sapra (2002) demonstrates that it is
possible for mandatory financial instruments disclosures to result in excessive speculation in
the financial instruments market. Our empirical results to some extent evidence the possible
potential multi-purposes of financial instruments usage and support the findings of Sapra
(2002). These results remain robust to various specification tests.
Overall, the present study documents that financial instrument has cash flows effect, then
influence reporting earnings, and provides feedback for regulators with respect to the
subsequent reporting requirement in capital market. The present study enriches the financial
instruments related research from two angles. First, although Barton (2001) finds that
derivatives serve as a partial substitute role to discretionary accruals subject to managers’
income smoothing assumption, the potential cash flows effect of financial instruments can be
a potentially important motive for firms to use financial instruments. We examine whether
financial instruments usage is a complement tool for discretionary accruals to manipulate
earnings. The second angle, this study contributes to the literature by examining, besides the
prevailing hedging risk hypothesis, financial instruments can be used to speculate on
movements in the value of the underlying asset. If managers can boost earnings by using
multiple tools, i.e., financial instruments and aggressive discretionary accruals, the question
naturally arises regarding how managers combine these instruments to gain benefits. This
examination, to some extent, provides evidence to understand the substitute or complement
nature of financial instruments usage strategy in earnings increase and assists regulators to
evaluate the possible policy effect of SFAS No. 34 in Taiwan.
This paper is organized as follows: Section 2 describes the related prior studies and develops
the empirical hypothesis. Section 3 presents the empirical design. Section 4 presents and
discusses the empirical results. Section 5 discusses the robustness test and Section 6
concludes the study.
BACKGROUND AND LITERATURE REVIEWS
Accounting for Financial Instruments in Taiwan
The ongoing growth in the use of financial instruments accompanying with the financial
instruments abuses have motivated accounting regulators to develop and stretch financial
instrument-related disclosure requirements in Taiwan.
First, The Financial Accounting
Standards Committee of the Accounting Research and Development Foundation in Taiwan
issued SFAS No.14 regulating the accounting treatments of Foreign Currency Translation
transactions in December 1998. Subsequently, SFAS No.27 (Disclosure of Financial
Instruments) was issued in 1997 and aimed to improve the transparency of listed firms’
financial instruments usage. Namely, it required the disclosure of the extent (i.e., the
contractual or notional amount), nature, terms, and the concentrations of credit risk for all
financial instruments. Since the requirements of disclosing of financial instruments were
not clear and uniform, the disclosures about financial instruments in financial statement are
obscure and discretionary. SFAS No.34, Accounting for Financial Instruments, was issued
in December 2004 and was effective for fiscal years beginning after 2006. This new
standard requires all financial instruments owned by the listed companies to be initially
recognized in the balance sheet as either liabilities or assets at their fair values. It also
requires them to adopt mark to market approach to recognize the resulting gains or losses
from financial instruments’ fair value changes at the end of fiscal year in their income
statements or in equity section of the balance sheet, depending on the intentions of financial
instruments usage and the types of hedging. This standard setting provides the present study
with a unique opportunity to identify a distinct financial instruments users sample that
recognized financial instruments in their annual reports during 2006~2007 to examine the
alternative motivation of financial instruments usage, besides the risk hedging motivation.
Related Research
Firms normally make plans based on expectations of what foreign exchange rates, interest
rate, and commodity prices will be over the near time. If prices or rates change, the result of
operations and cash flows will also differ from expectations. When firms have incentives to
reduce the volatility of cash component of earnings through real risk-management activities,
one possible way is to use derivatives to hedge the risks inherent in commodity prices,
foreign currencies, and interest rate (Nance et al., 1993; Tufano, 1996; Gèczy et al., 1997).
That is, firms can use properly structured hedging derivative forms, whose rate or price
moves in the opposite direction of the rate or price of the underlying item being hedged, to
reduce the magnitude of differences. Breeden & Viswanathan (1998) find that hedging
reduces noise related to exogenous factors and hence decreases the level of asymmetric
information regarding a firm’s earnings and its quality. This finding again is supported by the
study of Dadalt et al. (2002). There are substantial studies examining the using of
derivatives to smooth earnings/cash flows volatilities. For examples, Smith & Stulz (1985)
show that the use of derivatives to hedge can maximize shareholder value because hedging
may be reduce expected tax and expected costs of financial capital by reducing the
probability that the firm encounters financial distress. In advance, there are rich bodies of
studies exploring the various channels through which financial derivatives hedging can
contribute to increase firm value (e.g., Froot et al., 1993; DeMarzo & Duffie, 1995;
Allayannis & Ofek, 2001; Graham & Rogers, 2002). It seems the usage of financial
derivatives is associated not only with smoothing earnings but also with enhancing firm’s
value.
Another line of research examines the tradeoffs managers make between derivatives and
other risk management tools. For example, Schrand & Unal (1998) document that managers
of thrift institutions integrate derivatives and the composition of loan portfolios to manage
overall risk. Petersen & Thiagarjan (2000) using gold mining companies and Pincus &
Rajgopal (2002) using oil & gas firms as sample examine the interaction of accounting choice
and derivatives hedging and evidence similar tradeoffs pattern. Barton (2001) uses sample
firms of Fortune 500 non-financial companies and estimates a set of simultaneous equations
that captures managers’ incentives to maintain a desired level of earnings volatility through
hedging and accrual management. The author concludes that managers use derivatives and
discretionary accruals as partial substitutes for smoothing earnings. Following Barton
(2001), Wang & Kao (2005) based on Taiwanese listed companies sample also find the
substitution relationship between discretionary accruals and derivatives in earnings
smoothing. However, the finding of Wang & Kao (2005) is based on SFAS No.27
(Disclosure of Financial Instruments) and on relative small hand-collected sample size (109
sample firms with 327 firm-year observations). Their underlying premise is also based on
the comparative cost and benefit of discretionary accruals and derivatives as Barton’ (2001)
U.S. capital market setting. Recently, Kim et al. (2006) examine whether operational
hedging (firms reporting foreign sales) can be viewed as either a substitute or a complement
role to financial derivatives hedging when a firm intends to reduce the volatility of future
cash flows and, thus, to possibly increase firm value. The authors find that non-operationally
hedged firms use more financial hedging relative to their level of foreign currency exposure
and suggest operational hedging is complement to financial derivatives hedging. According
to above, to some extent, managers have incentives to smooth and/or enhance earnings and
financial instruments can be an effective tool to meet their earnings management goals.
Hypothesis Developments
Prior studies document that financial instruments can be treated as a mean to strategically
manipulate firm’s earnings. Barton (2001) and Wang & Kao (2005) propose the income
smoothing hypothesis and suggest both derivatives and discretionary accruals can be used for
reducing earnings volatility. Recently, Adam & Fernando (2006) suggest that the active
usage of derivatives is thought to create firm value by alleviating a variety of market
imperfections through hedging. Gèczy et al. (2007) also point out that derivatives can be
undertaken with the intention of making a profit. Based on the above arguments, financial
instruments usage can be viewed as either a substitute or a complement tool to a firm’s
discretionary accruals when it intends to reduce a firm’s volatility of future cash flows or to
possibly increase a firm’s earnings. In the income smoothing hypothesis, it is expected that
financial instruments usage can be partially substituted by the use of discretionary accruals.
As for the earnings increasing hypothesis, it is expected that financial instruments usage is a
complement tool to the use of discretionary accruals. Thus, that the goal of strategically
uses of financial instruments is earnings smoothing or earnings increasing, to some extent, is
an empirical issue in differential capital market setting.
Although Ewert & Wagenhofer (2005) develop an analytical model and demonstrate that real
activities manipulation increases when tightening accounting standards make accruals
management more difficult. However, accruals management and real activities manipulation
(e.g., financial instruments usage) are not mutually exclusive strategies for managers and
multiple tools can achieve the same earnings objective. Firms can use both accruals
management and real activities manipulation to boost or constrain earnings and obtain the
greatest effect through a coordinated approach (Mizik & Jacobson 2007, 2008). In other
words, both accruals management and financial instruments manipulation play important
roles in concurrently strategically boosting and/or suppressing firm’s earnings.
As for the relative cost determination of alternative earnings management tools, prior studies
show that the reporting and/or litigation costs in U.S. are relatively higher than that in other
developed economies (e.g., Antle et al., 1997). The higher reporting and/or litigation costs
bring about the cost-benefit consideration of accruals management and financial instruments
usage manipulation an important issue in managers’ strategic earnings reporting decisions.
However, the relative cost-benefit of accruals management and financial instruments usage
manipulation bonded by the lower reporting and/or litigation costs may make the earnings
management incentives effect dominate the relative cost-benefit effect, and then, weaken the
effect of cost determination in the selection decisions of alternative earnings management
tools. Yet, the institutional consideration for managers to incorporate the relative cost-benefit
of alternative manipulating tools into strategic earnings reporting decisions are likely to be
less in Taiwan, where reporting and litigation costs are relatively lower.1 Thus, without
higher reporting and litigation costs to trigger the relative cost-benefit advantage
consideration between accruals management and financial instruments usage manipulation,
we expect the earnings management incentives effect will dominate the relative cost-benefit
effect in managers’ accruals management and financial instruments usage manipulation
decisions.
Stulz (1996) documents that managers’ market views influence their attempting to time
commodity markets with financial derivatives usage decisions. The author claims that
selective hedging will increase shareholder value if managers have an informational
advantage relative to other market participants. Thus, it is possible that managers believe
that they can create shareholder value by incorporating speculative elements into their
financial instruments decisions. Sapra (2002) suggests that greater transparency about a
firm's derivative activities is not necessarily a panacea for imprudent risk management
strategies. The author shows that transparency actually induces the firm to take excessive
speculative positions in the derivative market. In other words, after SFAS No.34 is enforced
in Taiwan, once managers believe they have a comparative information advantage relative to
the market and, hence, view speculation as a positive profitability activity, the magnitude of
financial instruments usage will enlarge. In such situation, besides the aggressive
discretionary accruals, financial instruments usage, involving real actions which have cash
flows effects, play an important role in strategically boosting firm’s earnings. It is expected
that both the financial instruments usage and aggressive discretionary accruals play important
roles in elucidating firms’ income-increase reporting behaviors. Inspired by the studies of
1
Taiwan is characterized as a smaller stock markets, weaker investor protections, and lower disclosure
requirements (Chin et al., 2009). Chin et al. (2007) and Duh et al. (2009) also documented that the auditor litigation
costs in Taiwan are lower than those in other developed economies. Thus, both the lower disclosure requirements and
auditor litigation costs form a somewhat relative weaker financial reporting costs environment.
Sapra (2002), Adam & Fernando (2006), Gèczy et al. (2007) that view financial instruments
usage as positive profitability activities, the present study conjectures that firms with active
financial instruments positions will simultaneously use discretionary accruals to boost their
earnings. Namely, this study examines the complement relationship between the financial
instruments usage and aggressive accruals management in enhancing earnings. From the
above discussions, we therefore establish our hypothesis as follows:
H1: The financial instruments usage regulated by SFAS No.34 is positive association with the
magnitude of discretionary accruals.
RESEARCH DESIGN
Data and Sample Selection
Years 2006 and 2007 are chosen as the observation years because SFAS No.34 was enforced
on 2006 and later annual reports in Taiwan. The sample firms used in this study are composed
of publicly traded companies listed on the Taiwan Stock Exchange (hereafter, TSE) and Gre
Tai Securities Market (hereafter, OTC) in Taiwan. That only TSE-listed and OTC-listed firms
are considered is due to the feasibility of collecting the necessary reliable data. The empirical
data are retrieved from both the Taiwan Economic Journal Database (TEJ) and the Open
Market Observation Post System (MOPS) of the TSE and OTC in Taiwan.
Because there are insufficient sample firms in some industries for estimating discretionary
accruals, except food (code 12), textile (code 14) electronic (codes 23, 24, 30) industries, we
follow the study of Chang et al. (2003) and group some resembling industries into one
integrated industry to obtain larger firm sample and avoid the inefficiency problem of
regression coefficients estimation. Electric machinery (code 15) and electric wire (code 16)
are combined as “electric” industry; plastics (code 13), chemical (code 17), and rubbery
(code 21) are combined as “plastic and chemical” industry; cement (code 11), iron (code 20),
and construction (code 25) are combined as “construction and building materials” industry;
tourism (code 27) and general merchandise (code 29) are combined as “service and sales”
industry. Table 1 reports the resulted seven industries in the study.
Table 2 presents the sample selection process of the present study. Consistent with extant
literature, finance-related institutions (codes 2801 to 2888) are excluded as they are subject to
different disclosure requirements and regulations that make using industry cross-sectional
Jones model to estimate discretionary accruals problematic. The total observations of firms
traded in TSE and OTC during 2006~2007 are 2,459 firm/years. The present study follows
the study of Chang et al. (2003) and deletes glass-ceramic, paper, automobile industries,
shipping and transport industry, oil industry, and other industries because of too few listed
firms causing trouble in estimating discretionary accruals. We also deleted 394 observations
for data deficient or unavailable. These selection procedures yield a final sample of 1,834
firm/years, which include 837 firms in year 2006 and 997 firms in year 2007, respectively.
Table 1: Integrated Industry Analysis.
Integrated or Individual Industry
Industry Composition
Construction and Building Materials
Construction, Steel, and Building
(11,20,25)
Plastic and Chemical (13,17,21)
Plastic, Chemical, Rubber and Tire
Electric (15,16)
Electric Machinery, Electric Wire and Cable
Service and Sales (27,29)
Tourism and General Merchandise
Electronic (23, 24, 30)
Electronic
Foods (12)
Foods
Textile (14)
Textile and Fiber
Table 2: Sample Selection.
Firms listed on TSE and OTC during 2006~2007
Less:
Too few listed firms industry:
Shipping and Transport industry
Oil industry
Paper industry
Glass and Ceramic industry
Automobile industry
Other industries
Firms’ data unavailability:
Final samples
2006
1,229
2007
1,230
Total
2,459
(22)
(12)
(7)
(7)
(5)
(64)
(275)
837
(22)
(12)
(7)
(5)
(5)
(63)
(119)
997
(44)
(24)
(14)
(12)
(10)
(127)
(394)
1,834
Variables Measurement and Empirical Model
This section presents the methodology implementing to examine the hypothesis. Since the
usage of financial instruments regulated by SFAS No.34 and discretionary accruals may be
endogenous, the simultaneous equations are developed to avoid bias and the inconsistent
coefficients estimation of ordinary least squares (OLS). Following Barton (2001) and Wang
& Kao (2005), the relationship between financial instruments usage and discretionary
accruals is tested by the following simultaneous equations estimated by two-stage least
squares (2SLS) regression. To test our hypothesis, the present study uses the following
simultaneous equations:
| DIC |it   0  1DER it   2COMPit  3STOCK it   4 DEBTit  5 RDit   6FS it
  7 IDR it  8INDit   9 DPR it  10FLEX it  11 | OCF |it 12ROA it
 13SIZE it  14MILLS it   it
and
................................................................(1)
DER it  0  1 | DIC |it 2COMPit  3STOCK it  4 DEBTit  5 RDit  6FS it
 7 IDR it  8INDit  9QR it  10CYCLE it  11DIV _ YIELD it
 12ST _ DEBTit  13MILLS it  it
......................................................(2)
where:
| DIC | and DER are the proxies for discretionary accruals and financial instruments usage
regulated by SFAS No.34, respectively. In simultaneous equations, | DIC | and DER are
endogenous variables, concurrently, the other variables are control variables and will be
defined in section 3.3. Furthermore, the possible self-selection problem is corrected by
adding the inverse Mills ratio (MILLS) variable, which suggested by Heckman (1979), into
the simultaneous equations.
Because the sample firms do not have sufficient time series data to estimate the parameters of
the Jones (1991) model when determining | DIC | , we measure discretionary accruals (DA)
via the cross-sectional version of the Jones (1991) model as reported in DeFond & Jiambalvo
(1994)2 . Subramanyam (1996) and Bartov et al. (2001) indicate that this approach is
generally better specified than the time-series model. Recently, Kothari et al. (2005) suggest
that incorporating a performance measure into Jones model to estimate the parameters of the
nondiscretionary accruals can effectively enhance the model specification. Therefore, we
estimate the discretionary accruals (DA) by the prediction errors from the cross-sectional
Jones model of Kothari et al. (2005). As discussed above, we follow the study of Chang et
al. (2003), grouping some resembling industries into one integrated industry to come up with
a larger sample, thus to avoid the inefficiency of regression coefficients estimation when
getting the estimates of nondiscretionary accruals.
The financial instruments regulated by SFAS No.34 include: financial assets/liabilities at fair
value through profit or loss, held-to-maturity investments, available-for-sale financial assets,
hedging derivative assets/liabilities, financial assets/liabilities carried at amortized cost,
financial assets carried at cost. Thus, in order to catch the earnings management intention of
the financial instruments usage, our second pivotal variable DER is measured as the change
in sum of notional amount of financial assets and financial liabilities regulated by SFAS
No.34 scaled by lagged total assets. It is described as follows:
DER  TFA it  TFA it 1  / Ait 1
where:
TFA is defined as the sum of notional amount of total financial assets plus total financial
liabilities regulated by SFAS No.34. According to the definition, the DER may be positive
or negative. Positive DER indicates that there is an increasing usage of financial instruments
regulated by SFAS No.34. On the other coin, negative DER reveals that there is a
decreasing usage of financial instruments regulated by SFAS No.34.
2
The modified Jones model proposed by Dechow et al. (1995) is also used to enhance the robustness of our
study. The result is discussed in the robustness check.
Definitions of Control Variables
Inverse Mills Ratios
To measure the inverse Mills ratio, we first estimate the following equation to explain
the decision to use financial instruments:
USERit   0   1COMPit   2 STOCK it   3 DEBTit   4 RDit   5 FSit   6 IDRit
  7 INDit   8QRit   9CYCLEit   10 DIV _ YIELDit   11ST _ DEBTit   it ....(3)
where:
USER is a binary variable and coded 1 if the firm uses financial instruments, 0 otherwise.
We use probit regression on the full sample of 1,834 firm-years to estimate equation (3).
Following Greene (2004),  and W are denoted as the coefficient vector and the
explanatory variable vector in the financial instruments user model respectively. And let 


be the estimate of  . Then, the inverse Mills ratio is defined as: MILLS  ( W) / ( W) if


USER variable equals 1 and MILLS  ( W) /[( W)  1] if USER variable equals 0, where: ()
and () are denoted as the standard normal probability density function and the standard
normal cumulative distribution function, respectively. We include MILLS as an additional
control variable to correct for potential self-selection bias in the empirical equation.
Common Explanatory Variables for both | DIC | and DER Equations
Cash compensations for managers are increased with the firm’s performance such as profit.
The interest conflicts exist between managers and shareholders conditional on maximizing
managers’ self-interests. Managers might want to increase their cash compensations by
increasing earnings myopically. It is expected that cash compensations (COMP) will be
positively associated with the pivotal variables, i.e., | DIC | and DER . COMP is measured
as the directors’, supervisors’, and CEOs’ salaries and bonuses, scaled by lagged total assets.
It is also found that managers have incentives to increase earnings to raise their values
brought from ownership. Thus, we expect managers’ stock holdings to be associated with
| DIC | and DER . Stock holdings (STOCK) are measured as the sum of percentages of the
stock ownerships owned by directors, supervisors, and CEO. In addition, managers may
avoid violating the debt covenants based on accounting performance. DeFond & Jiambalvo
(1994) provide evidence that managers tend to increase earnings when the leverage is raised.
We use debt-to-assets ratio (DEBT), defined as total liabilities divided by total assets, to
proxy for leverage risk. It is expected that DEBT will be associated with our two pivotal
variables, i.e., | DIC | and DER .
Baber et al. (1991) point out when pre-R&D earnings are low, managers may strategically
prune away investment in R&D expenditures. It suggests that R&D intensity will be
positively associated with | DIC | and DER . Thus, we incorporate R&D intensity into
empirical models to capture the relationship between R&D intensity and two explained
variables. R&D intensity is defined as the ratio of R&D expense to equity market value.
Moreover, Jorion (1990) documents that foreign sale is positively associated with the
absolute value of exposure to foreign exchange rate risk. Since exports sale is an important
revenue for listed firms in Taiwan, we expect firms with international diversification have
more opportunity to manage earnings by way of | DIC | and DER . We thus use ratio of
foreign sales to total sales (FS) to measure firms’ business diversification to capture the
possible influences of diversification on | DIC | and DER . Wang & Kao (2005) reveal that
interest-rate risk is positive associated with | DIC | and DER . Following Wang & Kao
(2005), we use the absolute value of the difference between interest revenues and interest
expenses scaled by net sales (IDR) to measure interest-rate risk and expect IDR will be
positive with | DIC | and DER . Naturally, the percentage of firms belonging to electronic
industries is 64.89% in our empirical data, thus, an electronic industry dummy variable (IND)
is used to control for the industry characteristics. IND is coded 1 if the sample firm belongs to
electronic industry and 0 otherwise.
Control Variables for the Discretionary Accruals Model
Previous studies document that when pre-management earnings are low, managers might
myopically increase earnings to maintain the common dividend payout levels. Dividend
payout ratio (DPR) is defined as the ratio of cash dividends to earnings per share before
discretionary accruals. In addition, managers in industries with more accounting flexibility
are likely to manage accruals to a greater extent (Barton, 2001). The present study follows
Barton (2001) and uses the root mean squared error of equation (4) as a proxy for industry
accounting flexibility (FLEX). It is expected that FLEX will be positively associated with the
magnitude of discretionary accruals.
The literature related to discretionary accruals are based on the maintained hypothesis;
however, Dechow et al. (1995) point out that the modified Jones model will overstate the
magnitude of discretionary accruals for firms with extreme operating cash flows. The
absolute value of operating cash flows scaled by lagged total assets (|OCF|) is used to control
for the potentially measurement error. If ROA of a firm is high, the manager is less likely to
myopically increase earnings (Bowen et al., 1995). ROA in this paper is measured as the ratio
of net income before interest and depreciation net of tax to average total assets. The relation
between ROA and | DIC | is predicted to be negative. In the view of economics of
regulation, large firms tend to manage earnings to avoid the monitors from capital market
regulator and the government. DeFond & Park (1997) provides evidence on the positive
relation between firm size and the magnitude of discretionary accruals. The natural logarithm
of net sales, denoted as SIZE, is the proxy of firm size in this paper.
Control Variables to Financial Instruments Usage Regulated by SFAS No.34
Firms with large current assets face less financial distress (Nance et al. 1993). Froot et al.
(1993) also indicate that the liquidation of assets is negatively associated with the use of
derivatives. Therefore, quick ratio (QR), defined as the ratio of quick assets to current
liabilities, is used to be a proxy for a firm’s liquidation. Firms with long cash conversion
cycles are more likely to benefit from hedging because their cash flows are exposed to
volatility in market prices (Barton 2001). Cash cycle (CYCLE) is measured as the average
collection days of accounts receivables, net of the average payment days of accounts payables,
plus average days of inventory sell-out. CYCLE variable is predicted to be positively related
with DER . In addition, Barton (2001) documents that large expected dividend yield increases
the firm’s cash need, managers tend to hedge against the volatility in cash flows. Hence, we
expect that dividend yield (DIV_YIELD), measured as the ratio of cash dividends to market
value of common stocks, will be positively correlated with DER . Finally, firms with shorter
debt maturity are more likely to use interest rate swaps to hedge risk exposure, thus, a
positive correlation between short maturity debts and DER is expected (Barton 2001). Short
maturity debts (ST_DEBT) are measured as current liabilities divided by total liabilities.
EMPIRICAL RESULTS
We first present the summary statistics and correlation analyses of related variables in this
study. Second, two-stage least squares regression results for the DER and |DIC| simultaneous
equations are presented.
Descriptive Statistics
Table 3 reports the descriptive statistics for the related variables in this study. The average
absolute discretionary accruals (|DIC|) is 0.073, which is statistically significant at 1% level.
The average financial instruments usage (DER) is 0.019. The average ratio of cash
compensations (COMP) is 0.9%. It indicates that managers receive relative moderate cash
compensations in the sample. The average ownership of managers (STOCK) is 23.8%
(median is 20.9%) and show the ownership structures of firms in Taiwan are characterized by
a higher degree of concentration. The average ratio of leverage and average R&D intensity
are approximately 0.37 and 0.024, respectively. More than half of revenues come from
foreign sales documents that international business activities play an important role in the
revenues structure in our sample. The average ratio of interest difference is 0.014. The mean
of FLEX is 0.017 and documents that there is little difference in the flexibility of GAAP in
different industries. The average ROA of the sample is approximate 6%. The average cash
conversion cycle is about 101 days. The average dividend yield is 3%. Finally, the average
SH_DEBT is 0.758 which reveals half of the total liabilities are short maturity debts in the
sample of this study.
Table 3: Descriptive Statistics of Related Variables (n=1,834).
Standard
First
Variables
Mean
Median
Deviation
Quartile
|DIC|
0.073
0.094
0.023
0.049
DER
0.019
0.092
-0.005
0.000
COMP
0.009
0.008
0.003
0.007
STOCK
0.238
0.135
0.139
0.209
DEBT
0.371
0.175
0.236
0.364
RD
0.024
0.035
0.003
0.013
FS
0.530
0.441
0.164
0.571
IDR
0.014
0.086
0.002
0.005
IND
0.650
0.477
0.000
1.000
DPR
0.399
0.420
0.000
0.410
FLEX
0.017
0.008
0.017
0.017
|OCF|
0.108
0.110
0.039
0.080
ROA
0.059
0.103
0.017
0.064
SIZE
14.819
1.496
13.868
14.682
QR
1.824
2.142
0.822
1.261
CYCLE
101.101
135.138
45.063
79.659
DIV_YIELD
0.030
0.028
0.000
0.025
Third
Quartile
0.093
0.020
0.012
0.303
0.486
0.031
0.822
0.010
1.000
0.663
0.020
0.152
0.115
15.645
2.083
122.678
0.051
ST_DEBT
0.754
0.210
0.619
0.801
0.934
Legends:
|DIC|=discretionary accruals. DER=financial instruments usage regulated by SFAS No.34. COMP=cash
compensations for managers scaled by lagged total assets. STOCK=the ownership owned by managers.
DEBT=the ratio of total liabilities to total assets. RD=the ratio of R&D expenditures to market value of
common equity. FS=the ratio of net foreign sales to net sales. IDR=the absolute value of difference between
interest revenues and interest expenses scaled by net sales. IND=1 if the sample firm belongs to electronic
industry, and 0 otherwise. DPR=the ratio of cash dividends to earnings net of discretionary accruals per share.
FLEX=the root mean squared error of equation (4). |OCF|=the absolute value of operating cash flows scaled by
lagged assets. ROA=the ratio of net income before interest and depreciation net of tax to average total assets.
SIZE=nature log of net sales. QR=the ratio of quick assets to current liabilities. CYCLE=the average collection
days of accounts receivables, net of the payment days of accounts payables, plus average days of inventory
sell-out. DIV_YIELD=the ratio of cash dividends for common stock to market value of common equity.
ST_DEBT=current liabilities scaled by total liabilities.
Correlation Analysis
According to the study of Barton (2001), earnings is the sum of cash flows and accruals, thus,
2
2
the variance of earnings (  E ) is a function of the variance of cash flows (  C ), the variance
of accruals (  A ), and the correlation coefficient of cash flows and accruals (  CA ). In other
2
words, the equation  E   C   A  2CA C A will be hold. Therefore, it is reasonable
to observe that managers smooth earnings volatility by adjusting cash flows volatility,
accruals volatility, and/or the correlation between cash flows and accruals. However, when
the myopic earnings increase motivation replaces the possible earnings smoothing behavior
by managers, it is expected to find distinct relationship between financial instruments usage
and discretionary accruals, which is different with those observed in Barton (2001) and Wang
& Kao (2005). The present study first follows the procedure suggested by Barton (2001) and
correlates the earnings volatility with | DIC | and DER variables to provide preliminary
comparison evidences. We use sample firm’s quarterly data to calculate the coefficient of
variation (CV) to measure volatility. Panel A in Table 4 presents the descriptive statistics for
the coefficient of variation for earnings, operating cash flow, and total accruals. The average
coefficients of variation of earnings, operating cash flow, and total accruals are 2.660, 4.926,
and 8.948, respectively. It is found that total accruals are more volatile than operating cash
flows and earnings.
2
2
2
Panel B in Table 4 presents the correlations between DER ( | DIC | ) and the coefficient of
variation (CV) of related variables.
It documents that DER is insignificantly
negative-associated with the volatilities of cash flows and accruals. But the rank
correlations between DER and the volatilities of earnings is 0.058 which is statistically
significant at 5% level. This result is inconsistent with the finding of Barton (2001) and
does not support the DER smoothing earnings volatility conjectures. The correlation
between | DIC | and volatilities of earnings is 0.005 which is positive but not statistically
significant. Operating cash flows and total accruals are both negatively associated with
| DIC | and again are inconsistent with the finding of Barton (2001). Overall, the fact that
financial instruments usage regulated by SFAS No.34 (i.e., DER) and discretionary accruals
(i.e., |DIC|) are positively associated with the volatility of earnings suggests that managers do
not use both financial instruments and discretionary accruals to smooth earnings volatility.
Thus, the conclusion that the usage of financial instruments can smooth earnings volatility,
which is suggested by Barton (2001), is called for further examinations.
Although the untabulated Pearson/Spearman correlation matrix for the related variables in
equations reveal most of the independent variables are correlated with others, the variance
inflation factors (VIF) of the explanatory variables (unreported) in the regressions are less
than 10 and do not suggest severe multi-collinearity problems (Neter et al., 1989).
Table 4: Rank Correlations between |DIC| and DER and the Coefficient of Variation.
Panel A. Descriptive Statistics for the Coefficient of Variation(n=1,834)
Standard
First
Third
Variables
Mean
Median
deviation
quartile
quartile
Coefficient of variation for the variable: a
Earnings
2.660
18.774
0.305
0.570
1.196
Operating cash flows
4.926
23.029
0.803
1.704
3.704
Total accruals
8.948
46.213
1.239
2.431
5.375
Correlation between operating cash
-0.857
0.292
-0.993
-0.970
-0.875
flows and total accruals
Panel B. Correlations between |DIC|, DER and the Coefficient of Variation (n=1,834) b
CV of variables
|DIC|
DER
CV of earnings
0.005
0.058**
CV of operating cash flows
-0.025
-0.002
CV of total accruals
-0.053**
-0.005
Correlation between operating cash
-0.031
-0.007
flows and total accruals
Legend:
a. The coefficient of variation is the standard deviation of the variable divided by the absolute value of its mean,
based on quarterly data for 2006 and 2007.
b. *, **, *** denote significance at the 10%, 5%, and 1% levels respectively, based on two-tailed tests.
Simultaneous Regression Results
From Table 5, the coefficients of DER in Equation (1) and |DIC| in Equation (2) are 3.067
(t=29.28) and 0.256 (t=7.00), respectively, and both positively statistically significant at 1%
level. It documents that managers use both financial instruments and discretionary accruals to
increase earnings, yet, not to smooth earnings. It is reasonable to conclude that managers use
financial instruments and discretionary accruals as complement tools rather than substitute
tools in create firms’ net income. Our hypothesis is empirically supported.
The empirical results from common control variables are discussed as follows. The
coefficients of COMP variable are -0.501(t=-2.43) and -0.193 (t=-0.67) in the DER and |DIC|
models, respectively. It is unexpected to find that COMP is negatively associated with DER
and |DIC|. This may be due to the fact that cash compensation in many Taiwanese firms is
replaced with stocks or stock options in recent years. The coefficients of STOCK variable are
-0.046(t=-4.27) and 0.003 (t=0.21) in the |DIC| and DER models, respectively. The
coefficient of STOCK is negative statistically significant associated with |DIC| at 1% level.
It implies that the interest conflict between shareholders and the managers holding large
shareholdings is not severe and consistent with the argument of Jensen and Meckling (1976).
However, in the DER model, the coefficient of STOCK is positive but not statistically
significant. The coefficient of DEBT is 0.281(t=24.07), which proxy of leverage risk, is
positive statistically significant associated with |DIC|. Consistent with the finding of DeFond
& Jiambalvo (1994), this result reveals that the firms with higher leverage are more inclined
to manipulate earnings.
Table 5: Regression Results for Simultaneous Equations (n=1,834).
| DIC |it   0  1DER it   2 COMPit   3STOCK it   4 DEBTit   5 RDit   6 FS it   7 IDR it
  8 INDit   9 DPR it  10 FLEX it  11 | OCF |it 12 ROA it  13SIZE it  14 MILLS it   it (1)
DER it   0  1 | DIC |it  2 COMPit   3STOCK it   4 DEBTit   5 RDit   6 FS it   7 IDR it
 8 INDit   9 QR it  10CYCLE it  11DIV _ YIELD it  12ST _ DEBTit  13MILLS it   it (2)
|DIC|
Variables
Expected
sign
Coefficients
+
DER
COMP
STOCK
DEBT
+
+
+
+
3.067
-0.501
-0.046
0.281
***
RD
FS
IDR
IND
DPR
FLEX
+
+
+
+
+
+
0.383
0.001
-0.148
-0.050
-0.009
1.631
***
|OCF|
ROA
SIZE
QR
CYCLE
DIV_YIELD
ST_DEBT
MILLS
+
+
+
+
+
?
0.362
-0.045
-0.003
***
Adjusted R2
F-statistic
-0.059
***
Constant
|DIC|
-0.004
0.61
206.22
**
***
***
***
***
***
**
***
***
**
*
***
DER
t-stat.
Coefficients
-3.132
0.015
0.256
29.284
-2.426
-4.269
24.069
8.353
5.449
-5.509
-14.150
-2.442
8.382
-0.193
0.003
-0.054
-0.116
-0.0004
0.067
0.006
t-stat.
***
***
1.043
7.000
-0.667
0.206
-3.579
*
-1.821
-1.345
1.512
1.170
***
4.358
-2.368
0.071
0.130
-0.275
23.844
-2.776
-2.544
-1.675
0.005
-0.00004
0.006
0.001
-0.001
0.06
9.85
**
***
Legends:
1. MILLS is the inverse Mills ratio obtained from Equation (4). Other variables are defined in Table 3.
2. *, **, *** denote the significance on 10%, 5%, and 1% levels respectively, based on two-tailed tests.
The coefficients of RD variable are 0.383(t=8.35) and -0.116(t=-1.82), which statistically
significant at 1% and 10% level respectively, in the |DIC| and DER models. The statistically
significant positive relation between R&D intensity and discretionary accruals reveals that
managers with more growth opportunities tend to manage earnings to prevent
underinvestment problems. However, the negative relationship between RD and DER
suggests that the usage of financial instruments is decreased for managers with more growth
opportunities. It is interesting to find that FS is statistically significant positively related
with |DIC|, yet, statistically insignificant negatively related with DER. It implies that
managers with higher exchange rate risk increase the use of discretionary accruals.
However, a distinct pattern is revealed by the IDR variable empirical findings. The
coefficients of IND variable are -0.050(t=-14.150) and 0.006(t=1.170) in the |DIC| and DER
models, respectively. The empirical results reveal that the use of discretionary accruals is
decreased for firms belonging to electronic industry when comparing with firms in other
industries. Nevertheless, managers in electronic industry use slightly more financial
instruments. The reason for this result may be attributed to the fact that there is strong
demand for cash flow hedge for firms in electronic industry and managers use financial
instruments to smooth cash flows volatility, then, decrease the usage of discretionary
accruals.
As to the exclusive variables in the |DIC| model, the coefficients of FLEX and |OCF| are
1.631(t=8.38) and 0.362(t=23.84), respectively, which are both positive and statistically
significant at 1% level. It reveals that firms with more FLEX and larger magnitude of
operation cash flows tend to use discretionary accruals to manipulate earnings. The
coefficients of DPR, ROA and SIZE are -0.009(t=-2.44), -0.045 (t=-2.78) and -0.003(t=-2.54),
both negative and statistically significant at 5%, 1% and 5% level, respectively. As to the
exclusive variables in the DER model, the coefficient of QR variable is 0.005(t=4.36),
positive and statistically significant at 1% level. The coefficient of CYCLE variable is
-0.00004(t=-2.37), negative and statistically significant at 5% level. The coefficients of
DIV_YIELD and ST_DEBT are statistically insignificant.
ROBUSTNESS TEST
The present study implements some diagnostic checks (untabulated) to confirm our initial
findings. First, we know that three-stage least squares (3SLS) extend 2SLS and include all
equations estimation at the same time. In order to gain confirmatory evidence to support our
empirical results, the 3SLS regression is implemented in this study to estimate more efficient
coefficients. The result of 3SLS regression reveals that the coefficients of DER in Equation
(1) and |DIC| in Equation (2) are 0.689(t=2.74) and 0.079(t=2.15), both positive and
statistically significant at 1% and 5% level, respectively. This additional evidence suggests
our empirical result is robust to the 3SLS regression examination.
Second, the present study further examines DER and |DIC| models independently by
traditional ordinary least squares (OLS) to gain comparing evidence. These further
examination results reveal that the coefficients of DER in Equation (1) and |DIC| in Equation
(2) are 0.060(t=3.27) and 0.101(t=4.33), again, both positive and statistically significant at
1% level, respectively. The significantly positive relation between DER and |DIC| indicates
that the primary results do not topple by separated OLS estimations of Equation (1) and (2).
Third, the present study replaces the discretionary accruals measured from cross-sectional
Jones (1991) model by the discretionary accruals measured from cross-sectional modified
Jones model. In the 2SLS regressions, the coefficients of DER in Equation (1) and |DIC| in
Equation (2) are 3.146(t=30.38) and 0.257(t=7.25), respectively, both positive and
statistically significant at 1% level. In the 3SLS regressions, the coefficients of DER in
Equation (1) and |DIC| in Equation (2) are 0.796(t=3.55) and 0.085(t=2.18), which are
positive and statistically significant at 1% and 5% level in the |DIC| and DER models,
respectively. The additional evidence suggests that the positive correlation between DER
and |DIC| unlikely results from the misspecification of discretionary accruals.
Finally, in order to gain comparative results with Barton (2001), we further use the stock
concept to measure the position of financial instruments held by sample firms. Accordingly,
DER variable is redefined as the sum of notional amount of financial assets and financial
liabilities regulated by SFAS No.34 scaled by lagged total assets. The 2SLS regression
empirical results show that the coefficients of DER and |DIC| are 0.988 (t=13.32) and
0.220(t=4.73), respectively, when discretionary is measured by Jones model. Alternative,
the 2SLS regression empirical results report the coefficients of DER and |DIC| are 1.039
(t=13.95) and 0.224(t=4.97), respectively, when discretionary is measured by modified Jones
model.
In summary, we obtain supportive evidence on our hypothesis that managers view derivatives
and discretionary accruals as partial complement tools in enhancing earnings. The above
additional diagnostic checks demonstrate that our empirical results are robust to different
estimating methods and specifications of variables.
CONCLUSION
Prior studies document that firms can use derivative instruments to speculate and/or to
arbitrage on private information, yet, few studies highlight the risk hedging motive and
ignore the important roles of the other two key participants. Adam & Fernando (2006)
argue that the active usage of derivatives is thought to add value by alleviating a variety of
market imperfections through hedging. Gèczy et al. (2007) also suggest that financial
derivatives involve real action which plays an important role in strategically boosting firm’s
earnings and view speculation as a positive profitability activity. Although Barton (2001)
and Wang & Kao (2005) evidence managers appear to trade-off derivatives and discretionary
accruals for incentives to smooth earnings. The present study alternatively use the financial
instruments usage regulated under SFAS No.34 in Taiwan to examine the hypothetical
complement relationship between financial instruments usage and discretionary accruals in
enhancing earnings.
The empirical result indicates that financial instruments usage is positive associated with the
magnitude of discretionary accruals and supports our complement hypothesis. It suggests
that managers use financial instruments and discretionary accruals jointly to increase earnings,
contrary to the conclusions of Barton (2001) and Wang & Kao (2005). Our empirical results
to some extent evidence the possible potential multi-purposes of financial instruments usage
and support the findings of Sapra (2002). These results remain robust to various
specification tests.
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