Volume of Derivative Trading, Enterprise Value, and the Return on Assets

doi:10.4236/me.2013.48055

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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: [email protected]

Received April 24, 2013; revised May 24, 2013; accepted June 24, 2013

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

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

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

J.-Y. YANG

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

2), where

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

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

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

(3947.23)

CPO

(1522.59)

CTO

(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

(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

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.

REFERENCES

[1] D. Ryu, J. Baek, J. Yang and J. Chae, “Derivatives Tra-

ding Volume and Abnormal Return,” Unpublished Manu-

script, 2011.

[2] T. Kwon, R. Park and U. Chang, “Derivatives Use, Firm

Value, Risk and Determinants: Evidence of Korean Firms,”

Korean Journal of Futures and Options, Vol. 19, No. 4,

2011, pp. 335-362.

[3] A. Jalivand, “Why Firms Use Derivatives: Evidence

From Canada,” Canadian Journal of Administrative Sci-

ences, Vol. 16, No. 3, 1999, pp. 213-225.

doi:10.1111/j.1936-4490.1999.tb00197.x

[4] T. Brunzell, M. Hansson and E. Liljeblom, “The Use of

Derivatives in Nordic Firms,” European Journal of Fi-

nance, Vol. 17, No. 5-6, 2011, pp. 355-376.

doi:10.1080/1351847X.2010.543836

[5] A. S. Ahmed, E. Kilic and G. J. Lobo, “Effects of SFAS

133 on the Risk Relevance of Accounting Measures of

Banks’ Derivative Exposures,” Accounting Review, Vol.

86, No. 3, 2011, pp. 769-804.

doi:10.2308/accr.00000033

[6] X. Fu and S. A. Heffernan, “The Effects of Reform on

China’s Bank Structure and Performance,” Journal of

Banking and Finance, Vol. 33, No. 1, 1999, pp. 39-52.

doi:10.1016/j.jbankfin.2006.11.023