Modern Economy, 2013, 4, 513-519
http://dx.doi.org/10.4236/me.2013.48055 Published Online August 2013 (http://www.scirp.org/journal/me)
Volume of Derivative Trading, Enterprise Value,
and the Return on Assets
Jin-Yong Yang
Department of International Business, Hankuk University of Foreign Studies, Seoul, Korea
Email: jyang0112@gmail.com
Received April 24, 2013; revised May 24, 2013; accepted June 24, 2013
Copyright © 2013 Jin-Yong Yang. This is an open access article distributed under the Creative Commons Attribution License, which
permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
ABSTRACT
We study how the volume of derivatives trading is associated with the return on assets (ROA), as well as the enterprise
value proxied by abnormal return (AR), before and after the US Financial Crisis. Results suggest that before the crisis,
the volume of over-the-counter trading, which tends to be less strictly regulated and thus can be more flexibly applied,
is positively associated with AR and ROA, while exchange trading is not. After the financial crisis, exchange trading,
which is more heavily regulated and thus has lower credit risks, is positively associated with AR and ROA. This implies
that the kinds of derivatives products having a positive or negative effect on the enterprise value of financial institutions
may vary according to each period of the economy. Therefore, in full consideration of the above, it is recommended that
more appropriate alternatives to the regulations and inspections should be provided for derivatives products and trading
methods of financial institutions.
Keywords: Derivatives Trading Volume; Enterprise Value; Return on Assets
1. Introduction
Derivatives trading can function positively for financial
institutions. When market risks are relatively low, the
volume of over-the-counter (OTC) trading of a financial
institution, which tends to be less strictly regulated and
thus can be more flexibly applied, is likely to have a pos-
itive association with the return on assets (ROA), as well
as the enterprise value proxied by abnormal return (AR),
before and after the US Financial Crisis. However, when
market risks are relatively high, this association would be
less clear. Instead, the volume of exchange trading,
which is more heavily regulated and thus has lower credit
risks, is likely to have a positive association with AR or
ROA. The goal of this paper is to test these hypotheses.
The legislation of Commodity Futures Modernization
Act (CFMA) in 2000 confirmed that OTC derivatives
trading would not be regulated. Since then, OTC deriva-
tives trading had actively grown until the U.S. Financial
Crisis, which resulted in intensified regulation. Hence,
this paper also studies the effects of derivatives trading
according to economic circumstances in diverse ways.
Because most of the major financial institutions se-
lected as samples for the study were banks and/or hold-
ing companies of the banks, ROA, which represents net
profit during the term based on assets size can be ex-
plained as the profit performance index of the banks. The
AR is the realized return net of the expected return. This
approach is also adopted in Ryu, Baek, Yang and Chae
[1], closely related to this paper.
Ryu, Baek, Yang and Chae [1] document a positive
association between derivatives trading volume, both
OTC and exchange, and AR and ROA for major U.S.
financial institutions. In addition, they analyze a similar
association by the type of financial institution on the
business performance. This paper studies how the asso-
ciation differs according to the market risks, in order to
understand the mechanism of derivatives trading. This is
meaningful especially because different regulations and
supervisions have been applied for OTC and exchange
derivatives. In addition, the derivatives market situation
before and after the financial crisis has changed quite a
bit and accordingly, it is expected that the effect on the
business performance of the financial institutions that
traded the derivatives would be different depending on
the market situation.
Numerous papers study the derivatives market. Ryu,
Baek, Yang and Chae [1] document that an increase in
exchange of OTC option trading volumes is positively
associated with AR. However, an increase in futures and
credit derivatives is negatively associated with AR. In
C
opyright © 2013 SciRes. ME
J.-Y. YANG
514
addition, Kwon, Park and Chang [2] report that deriva-
tives trading volumes are positively associated with AR.
This suggests that derivative trading would improve the
AR.
Jalivand [3] documents that the integrated level of
company size, efficiency of business, and financial ac-
tivities of a company are the major determinants of de-
rivatives traders, for non-financial institutions in Canada.
In a study of the listed companies in Nordic economies,
Brunzell, Hansson, and Liljeblom [4] find that most
firms trade derivatives for the purpose of hedging, but
more than a majority of firms were seeking returns in
addition to hedging. Ahmed, Kilic, and Lobo [5] study
the effects of SFAS 133, the financial accounting stan-
dard for derivatives, on the risk relevance of accounting
measures of derivative exposures.
This paper is organized as follows. Section 2 discusses
the research method. Section 3 provides the results. Sec-
tion 4 concludes.
2. Models and Data
2.1. Empirical Models
Our main hypothesis is that an increase in derivatives
trading volume of a major financial institution is posi-
tively associated with ROA and AR. Our regression
models are similar to the one used at Kwon, Park, and
Chang [2]. To be specific, for ROA, we consider
it1 1it2it3it
4it5it6 it
7it8it9t
10t11 t it
ROADEXDOTC CBI
CPO CTO CCA
SIZELEV INF
GDPUN ,
 



 



(1)
where ROAit is the net profit divided by total assets of
institution i at period t. Here, DEXit and DOTCit are
trading volumes of exchange derivatives and OTC de-
rivatives, respectively, measured by gross notional
amount of derivatives divided by total assets. Control
variables follow. CBIit is bilaterally netted credit equiva-
lent exposures, CPOit is the credit equivalent exposures
measuring potential future exposure to market prices
volatility, CTOit is the risk exposure to assets on total
credit exposure, and CCAit is the total credit exposure to
total assets. Each of CBIit, CPOit, CTOit and CCAit is
normalized by total assets. In addition, SIZEit is the asset
size and LEVit is the debt level, while INFt, GDPt and
UNt are inflation rate, the growth rate of GDP per capita,
and unemployment rate, respectively.
In addition, for AR, we consider
it1 1it2it3it
4 it4t6t7tit
ARab DEXbDOTCb SIZE
b
LEVb INFb GDPbUNe,
 

(2)
where ARit is the average abnormal return of institution i
at period t. To obtain AR, we first obtain daily observa-
tions on the market yield based on the S&P 500 index.
We then obtain ROAs from daily closing prices of each
financial institution. Using the period from -220 days to
-21 days from the end of the 4th quarter 2001 (i.e., Sep-
tember 30, 2001), we regress ROA of each financial in-
stitution on market yield to obtain beta. The AR of each
financial institution is obtained as the residual at each
period. The average of such ARs in each quarter was
calculated for analysis by quarter.
The results of previous studies document positive as-
sociations between risk management and enterprise value
according to derivatives trading. Hence, we expect that
the signs for β1, β2, b1 and b2 are positive. In addition, β7,
β8, b3 and b4 are also expected to be positive since it has
been documented that size and leverage are positively
associated with ROA. We use the size of a firm (SIZEit)
and its debt level (LEVit) as control variables. They were
used in previous research on risk management and per-
formance. In particular, Jalivand [3] argue that the size is
one of important factors to induce the use of derivatives.
That is, large-sized firms will engage in more derivatives
trade. Hence, the slope for SIZEit is expected to be posi-
tive.
It is also expected that INFt and GDPt would have a
positive correlation with ROAit and ARit since a positive
shock in monetary policy or GDP growth would posi-
tively affect the asset returns. Similarly, UNt would be
negatively correlated with ROAit and ARit. (For related
discussions on how macroeconomic variables are related
with ROAit and ARit, see, for example, Fu and Heffernan
[6]) As this study used exchange/OTC derivatives trading
volume by quarters for 40 quarters, the circumstances
according to time and economic situation in each quarter
should be taken into account. For this purpose, this study
employed variables of inflation, GDP, and unemploy-
ment rate, which were used as the macroeconomic vari-
ables in the study of Fu and Heffernan [6].
2.2. Data
Time is quarterly. The observations on the unemploy-
ment rate and the real GDP growth rate are the averages
of three monthly observations. The periods are classified
into before (2001Q4-2007Q2) and after (2007Q3-
2011Q3) the break of US Financial Crisis.
We consider major financial institutions, including
commercial banks, trust companies, bank holding com-
panies and financial holding companies, in the United
States. They are major traders in the US derivatives
market. To be specific, they consists of banks and trust
companies (Bank of America, Bank of New York Mellon,
Citibank, JPMorgan Chase Bank, Keybank, PNC Bank,
State Street Bank & Trust Co., Suntrust Bank, U.S. Bank,
and Wells Fargo Bank) and banks and financial holding
companies (Bank of America Corporation, Bank of New
Copyright © 2013 SciRes. ME
J.-Y. YANG
Copyright © 2013 SciRes. ME
515
York Mellon Corporation, Citigroup Inc. HSBC North
America Holdings Inc., JPMorgan Chase & Co., Keycorp,
Northern Trust Corporation, PNC Financial Services
Group, Inc., State Street Corporation, Suntrust Banks,
Inc., U.S. Bancorp, and Wells Fargo & Company).
The data are obtained from the Office of the Comp-
troller of the Currency (OCC) and investor relations
(FDIC insured commercial bank, OCC, call report).
Table 1 provides the descriptive statistics, for banks
and trust companies and for banks and financial holding
companies, respectively. Table 2 provides correlation
coefficients. The coefficients are positive and high
among risk measures, i.e., CBIit, CPOit, CTOit and CCAit.
3. Results
3.1. Regression Results
Table 3 summarizes the regression results. Part (A) es-
timates Model (1) for banks and trust companies. For
“Before the Crisis” sample of 2001Q4-2007Q2, the var-
iables, CBIit, CPOit, CTOit, and constant term have cor-
relations with independent variables. In order to elimi-
nate multicollinearity, they were removed from the ana-
lysis. In Estimations of (1) and (2), we obtain the vari-
ance inflating factor (VIF) as VIFj = 1/(1 R
j
2), where
Rj
2 is the R squared when Xj is regressed on all other
explanatory variables. The variables with VIFs exceeding
10 are excluded for a concern of multicollinearity. Those
variables are reported in Table 4 .
The results suggest that exchange-traded derivatives
trading volume has a significant negative () correlation
at the level of 1%. On the other hand, OTC derivatives
trading volume has a significant positive (+) correlation
at the level of 5%. This implies that banks and trust
companies can improve their returns by increasing OTC
derivatives trading volume. On the other hand, the analy-
sis of the relation between derivatives trading volume
and ROA of banks and investment companies after the
Table 1. Descriptive statistics. (a) Banks and trust companies; (b) Banks and financial holding companies.
(a)
Variable #Obs Mean Standard DeviationMin Median Max
ROAit 400 0.57% 0.56% 1.80% 0.53% 2.98%
DEXit 400 0.89 1.21 0.00 0.42 7.62
DOTCit 400 11.41 0.79 15.80 0.15 70.23
CBIit 400 0.05 0.10 0.00 0.03 1.03
CPOit 400 0.10 0.26 0.00 0.02 2.50
CTOit 400 0.14 0.35 0.00 0.05 3.53
CCAit 400 0.00 0.02 0.00 0.00 0.31
SIZEit 400 25.90 0.89 24.23 25.67 27.33
LEVit 400 0.01 0.11 0.00 0.00 0.90
INFt 400 2.02 0.53 1.23 1.90 2.90
GDPt 400 0.02 0.01 0.00 0.02 0.04
UNt 400 0.06 0.00 0.05 0.06 0.06
(b)
Variable #Obs Mean Standard DeviationMin Median Max
ARit 480 0.02 0.03 0.09 0.00 0.00
DEXit 480 8.54 12.26 0.17 2.88 62.39
DOTCit 480 0.37 0.47 0.00 0.21 3.18
SIZEit 480 26.41 1.12 24.32 26.22 28.50
Note: ROAit is the net profit divided by total assets of institution i at period t. DEXit and DOTCit are trading volumes of exchange derivatives and OTC deriva-
tives, respectively, measured by gross notional amount of derivatives divided by total assets. CBIit is bilaterally netted credit equivalent exposures, CPOit is the
credit equivalent exposures measuring potential future exposure to market prices volatility, CTOit is the risk exposure to assets on total credit exposure, and
CCAit is the total credit exposure to total assets. Each of CBIit, CPOit, CTOit and CCAit is normalized by total assets. SIZEit is the asset size and LEVit is the
debt level, while INFt, GDPt and UNt are inflation rate, the growth rate of GDP per capita, and unemployment rate, respectively.
J.-Y. YANG
516
Table 2. Pearson correlation coefficients. (a) Banks and trust companies; (b) Banks and financial holding companies.
(a)
DEXit DOTCit CBIit CPOit CTOit
DOTCit 0.970***
CBIit 0.246 0.240
CPOit 0.400** 0.378*** 0.972***
CTOit 0.359** 0.342*** 0.985*** 0.998***
CCAit 0.917*** 0.916*** 0.496*** 0.619*** 0.588***
SIZEit 0.756*** 0.721*** 0.124 0.241 0.210
LEVit 0.003 0.001 0.018 0.014 0.015
INFt 0.073 0.075 0.101 0.074 0.082
GDPt 0.091 0.079 0.069 0.018 0.033
UNt 0.012 0.027 0.049 0.015 0.024
(b)
ARit DEXit DOTCit
DEXit 0.137
DOTCit 0.127 0.899***
SIZEit 0.103* 0.638** 0.591***
Note: ***: Significant at 1%. **: At 5%. *: At 10%. ROAit is the net profit divided by total assets of institution i at period t. DEXit and DOTCit are trading vol-
umes of exchange derivatives and OTC derivatives, respectively, measured by gross notional amount of derivatives divided by total assets. CBIit is bilaterally
netted credit equivalent exposures, CPOit is the credit equivalent exposures measuring potential future exposure to market prices volatility, CTOit is the risk
exposure to assets on total credit exposure, and CCAit is the total credit exposure to total assets. Each of CBIit, CPOit, CTOit and CCAit is normalized by total
assets. SIZEit is the asset size and LEVit is the debt level, while INFt, GDPt and UNt are inflation rate, the growth rate of GDP per capita, and unemployment
rate, respectively.
financial crisis showed a different pattern. The trading
volume of exchange derivatives in financial institutions
had a positive effect on the increase in ROA but an in-
crease in trading volume in OTC derivatives had a nega-
tive effect on ROA.
Part (B) similarly estimates Model (2) for banks and
financial holding companies. The variables, CBIit, CPOit,
CTOit, CCAit and LEVit have correlations with the inde-
pendent variables. They are removed from the analysis.
Results suggest that before the US Financial Crisis, the
trading volume of exchange derivatives has a negative
effect on enterprise value. Unlike in Part (A), the trading
volume in OTC derivatives has a positive effect on en-
terprise value. Both are significant at a 1% level. After
the US Financial Crisis, an increase in trading volume of
exchange derivatives had a positive effect on the AR of
stocks after the financial crisis, which is different from
the results before the financial crisis.
3.2. Panel Analysis Results
In order to test robustness of the research results, Table 5
reports additional panel data analyses. Part (A) summa-
rizes the results on banks and trust companies. An in-
crease in trading volume of OTC derivatives before the
financial crisis had a negative effect on the AR of finan-
cial institutions. However, after the financial crisis, an
increase in trading volume of exchange derivatives only
in the panel model on random effects had a positive rela-
tionship with ROA. It is significant at a level of 5%.
Part (B) summarizes the results on banks and financial
holding companies. An increase in trading volume of
OTC derivatives had a positive effect on the AR of fi-
nancial institutions for the whole period of both before
and after the financial crisis. As for the period after the
financial crisis, an increase in trading volume of ex-
change derivatives only in the panel model on fixed ef-
fects had a positive relationship with enterprise value.
4. Concluding Remarks
Multi-regression analyses and panel analyses suggest that
for major US Financial institutions, an increase in trading
volume of OTC derivatives had a positive effect on ROA
and AR of financial institutions before the financial crisis.
is is because derivatives trade decreased the risk T
h
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J.-Y. YANG 517
Table 3. Regression results of the model. (a) Banks and trust companies; (b) Banks and financial holding companies.
(a)
Vairables Before the Crisis (2001Q4-2007Q2) After the Crisis (2007Q3-2011Q3)
DEXit 0. 00*** (2.47) 0.10 (0.00)
DOTCit 0.00** (2.27) 0.70* (2.09)
CBIit Excluded Excluded
CPOit Excluded Excluded
CTOit Excluded Excluded
CCAit 0.22 (0.40) Excluded
SIZEit 0.10*** (6.41) 0.08*** (6.84)
LEVit 0.04 (1.20) Excluded
INFt 0.00 (0.33) 0.00 (0.46)
GDPt 0.59 (0.94) 0.11 (0.22)
UNt 1.10 (1.45) 1.37** (2.25)
R2/Modified R2 33.4%/31.6% 32.1%/30.5%
(b)
Vairables Before the Crisis (2001Q4-2007Q2) After the Crisis (2007Q3-2011Q3)
DEXit 0.45*** (13.14) 0.10*** (6.47)
DOTCit 0.18*** (5.91) 0.02*** (3.92)
CBIit Excluded Excluded
CPOit Excluded Excluded
CTOit Excluded Excluded
CCAit Excluded Excluded
SIZEit 0.03*** (4.06) 0.00*** (4.40)
LEVit Excluded Excluded
INFt 0.00 (0.02) 0.00 (1.71)
GDPt 0.21 (0.40) 0.08*** (6.41)
UNt 0.86 (1.38) 0.11*** (6.57)
R2/Modified R2 45.2%/43.9% 45.2%/43.9%
Note: Dependent Variable: ROAit. ***: Significant at 1%. **: At 5%. *: At 10%. ROAit is the net profit divided by total assets of institution i at period t. DEXit
and DOTCit are trading volumes of exchange derivatives and OTC derivatives, respectively, measured by gross notional amount of derivatives divided by total
assets. CBIit is bilaterally netted credit equivalent exposures, CPOit is the credit equivalent exposures measuring potential future exposure to market prices
volatility, CTOit is the risk exposure to assets on total credit exposure, and CCAit is the total credit exposure to total assets. Each of CBIit, CPOit, CTOit and
CCAit is normalized by total assets. SIZEit is the asset size and LEVit is the debt level, while INFt, GDPt and UNt are inflation rate, the growth rate of GDP per
capita, and unemployment rate, respectively.
of a firm and accordingly provided a positive effect on
enterprise value by improving profitability. However,
after the financial crisis, the trading volume in OTC de-
rivatives was only marginally significant. Rather, the tra-
ding volume in exchange derivatives appears to become
significant. This implies that the effects of derivatives
trading may vary according to the level of the market risk
of the derivatives.
Since the financial crisis, many countries have intensi-
fied regulations on large financial institutions due to the
concerns for the risk of derivatives. In doing so, the in-
herent purpose of derivatives trading, which is risk
transfer and effective funding, was a little bit ignored.
The focus was given in reducing the risk of OTC deriva-
tives.
We have conducted a reseach on the effects on finan- r
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J.-Y. YANG
518
Table 4. Multicollinearity analysis.
(A) Banks and Trust Companies (B) Banks and Financial Holding Companies
Before the Crisis
(2001Q4-2007Q2)
After the Crisis
(2007Q3-2011Q3)
Before the Crisis
(2001Q4-2007Q2)
After the Crisis
(2007Q3-2011Q3)
DEXit (5.15)
DOTCit (6.74)
CBI
it
(3947.23)
CPO
it
(1522.59)
CTO
it
(3905.11)
CCAit (9.7)
SIZEit (3.2)
LEVit (4.0)
INFt (1.9)
GDPt (1.6)
UNt (1.7)
DEXit (8.14)
DOTCit (1.52)
CBI
it
(1687.09)
CPOit (967.24)
CTOit (315.30)
CCAit (17.82)
SIZEit (6.50)
LEVit (17.39)
INFt (1.9)
GDPt (1.6)
UNt (1.7)
DEXit (7.83)
DOTCit (4.19)
SIZEit (7.65)
LEVit (14.09)
INFt (1.89)
GDPt (1.37)
UNt (1.64)
DEXit (5.23)
DOTCit (6.78)
SIZEit (2.3)
LEVit (12.35)
INFt (2.85)
GDPt (3.62)
UNt (1.08)
Note: ROAit is the net profit divided by total assets of institution i at period t. DEXit and DOTCit are trading volumes of exchange derivatives and OTC deriva-
tives, respectively, measured by gross notional amount of derivatives divided by total assets. CBIit is bilaterally netted credit equivalent exposures, CPOit is the
credit equivalent exposures measuring potential future exposure to market prices volatility, CTOit is the risk exposure to assets on total credit exposure, and
CCAit is the total credit exposure to total assets. Each of CBIit, CPOit, CTOit and CCAit is normalized by total assets. SIZEit is the asset size and LEVit is the
debt level, while INFt, GDPt and UNt are inflation rate, the growth rate of GDP per capita, and unemployment rate, respectively.
Table 5. Panel results of the model. (a) Banks and tr ust companies; (b) Banks and financial holding companies.
(a)
Fixed Effects Random Effects
Before the Crisis
(2001Q4-2007Q2)
After the Crisis
(2007Q3-2011Q3)
Before the Crisis
(2001Q4-2007Q2)
After the Crisis
(2007Q3-2011Q3)
DEXit 0.01 (0.67) 0.16 (1.02) 0.01 (0.12) 0.24** (2.27)
DOTCit 0.22* (2.17) 0.76 (1.83) 1.37** (3.49) 0.43 (0.04)
CCAit 0.18 (0.02) 0.46 (0.52) 0.06 (0.18) 0.90 (0.06)
SIZEit 0.00 (0.03) 0.01 (0.09) 0.09 (1.00) 0.04*** (2.45)
LEVit 0.00 (0.02) 0.10 (0.92) 0.71 (0.29) 0.00 (0.65)
INFt 0.30 (0.19) 0.86 (0.98) 0.05 (0.18) 0.22 (1.62)
GDPt 0.03 (1.00) 0.24 (1.03) 0.24 (0.27) 0.92 (1.22)
UNt 0.14 (0.96) 0.98 (0.17) 0.40 (0.94) 3.32 (1.07)
Modified R2 0.34 0.12 0.32 0.11
N 230 170 230 170
(b)
Fixed Effects Random Effects
Before the Crisis
(2001Q4-2007Q2)
After the Crisis
(2007Q3-2011Q3)
Before the Crisis
(2001Q4-2007Q2)
After the Crisis
(2007Q3-2011Q3)
DEXit 0.03 (0.61) 0.17* (1.99) 0.02 (0.08) 1.40 (0.00)
DOTCit 0.08* (2.09) 0.21 (0.81) 0.30*** (7.57) 0.29 (0.94)
SIZEit 0.04*** (4.12) 0.02** (2.21) 0.02 (1.02) 1.92*** (2.86)
INFt 0.03** (2.38) 0.62 (4.29) 0.22 (0.02) 0.94 (1.00)
GDPt 0.66*** (3.61) 7.12 (0.23) 0.48 (0.118) 3.30 (1.02)
UNt 0.73*** (8.02) 0.62** (2.17) 0.70*** (2.39) 1.03 (0.01)
Modified R2 0.27 0.40 0.22 0.37
N 276 204 276 204
Note: Dependent Variable: ROAit. ***: Significant at 1%. **: At 5%. *: At 10%. ROAit is the net profit divided by total assets of institution i at period t. DEXit
and DOTCit are trading volumes of exchange derivatives and OTC derivatives, respectively, measured by gross notional amount of derivatives divided by total
assets. CBIit is bilaterally netted credit equivalent exposures, CPOit is the credit equivalent exposures measuring potential future exposure to market prices
volatility, CTOit is the risk exposure to assets on total credit exposure, and CCAit is the total credit exposure to total assets. Each of CBIit, CPOit, CTOit and
CCAit is normalized by total assets. SIZEit is the asset size and LEVit is the debt level, while INFt, GDPt and UNt are inflation rate, the growth rate of GDP per
apita, and unemployment rate, respectively. c
Copyright © 2013 SciRes. ME
J.-Y. YANG 519
cial institutions when there is an increase in derivatives
trade volume in financial institutions and identify that the
kinds of derivatives products that affect positively or
negatively the enterprise value of financial institutions
may vary according to each period of the economy. In
consideration of the findings, more appropriate alterna-
tives should be provided to the regulations of derivatives
products, inspection of the derivatives market, and trad-
ing methods of financial institutions.
5. Acknowledgements
This research is financially supported by 2013 Research
Fund of Hankuk University of Foreign Studies.
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