The Present Role of Market Risks in the Financial Performance of

Indonesian Banks Post-2007 Financial Crisis and Post-2016 Financial

Technology Disruption

HERMAN KARAMOY, HIZKIA H. D. TASIK

Faculty of Economics,

Sam Ratulangi University,

Manado, North Sulawesi,

INDONESIA

Abstract: - The 2007-8 financial crisis and the 2016 technology disruption have motivated investors to be more

aware of the financial performance of the banks in Indonesia. This study attempts to examine the strength of

market risk post the financial crisis and financial technology disruption. To our knowledge, this is the first

study to examine the advancement of market risk in the Indonesian banking industry following the crisis and

disruption. Literature has shown that the role of market risk in other countries accentuates after the crisis. Using

panel data from forty-nine banks listed in the Indonesia stock exchange during the 2009  2020 period, this

study concentrates on the role of Market Risk Indicators (MRIs) in financial performance. The findings suggest

that MRIs alter the profitability indicators. The effect of MRIs becomes more robust as moving further away

from 2007. Additionally, there is no evidence that NIM has become a tool to manage risk.

Key-Words: - Market Risk, Financial Performance, Financial Crisis, Profitability

Received: January 11, 2023. Revised: May 2, 2023. Accepted: June 2, 2023. Published: June 20, 2023.

1 Introduction

Indicators preceded the financial crisis that erupted

in 2007 thought of as pleasing financial

achievements. A report published by [1], explained

that the financial crisis was preceded by an extended

period of fast credit expansion, low-risk premiums,

abundant liquidity, high leverage, and soaring asset

prices. No one knew that it was a signal that a

mistake had been made, a financial sin that would

last a long time. Additional factors, such as the

growth of real estate bubbles, made the list of sins

much longer. Some of the facts had been common

symptoms of the most major financial crisis in the

past, and the 2007 financial crisis was no exception.

Lenders were too effortless, letting the credit grow

buoyantly and the housing prices soar.

On the one hand, credit growth tends to boost

government revenues during booms and leave

substantial gaps during busts. On the other hand,

this fast growth could lead to an extreme increase in

market risk when the industry recklessly deals with

market risk management. Assets and liabilities were

prone to a problem. The same report by [1], argued

that excessive leveraging and the subsequent risk

spreading via securitization made financial

institutions extremely sensitive to asset market

corrections. As a result, a turnaround in a relatively

small part of the financial world (the U.S. subprime

market) was enough to set off a crisis that brought

down the entire system. Instead of purchasing assets

at a discount and selling them at a profit, people at

the time purchased them at a premium with their

fingers crossed, anticipating that asset prices would

continue to rise. The consequence of this mistake

was inevitably borne by almost everyone in the

world. As explained in [2], U.S. markets were at

their most extreme levels of risk before the 2008

financial crisis due to investors' large risk premiums.

[3], argued that risk management then became more

crucial after the 2007 global financial crisis. Not

only financial institutions, during or since the global

financial crisis, but firms with experience in risk

management also failed.

The 2007 crisis led to several consequences.

Many banks worldwide suffered from capital and

liquidity management crises due to the adverse

effect of the financial crisis on financial markets.

According to a report by [4], 168 banks were said to

have closed in the United States between the years

2007 and 2009. Banks and other financial

institutions were significant in every country to

function the financial systems like pension funds,

insurance, microfinance, deposits, and others. The

weak capability in managing the systems adversely

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affected the cash flow of the banks. The Housing

and Economic Recovery Act of 2008, the Economic

Stimulus Act of 2008, the Dodd-Frank Wall Street

Reform and Consumer Protection Act, the

Emergency Economic Stabilization Act, and the

Troubled Asset Relief Program (TARP) were the

support packages that the U.S. government released

to lessen the effects of the 2007 financial crisis, [5].

Meanwhile, the Federal Reserve Banks (Fed)

released policies, where some included lowering the

target for the Federal funds rate from 5.25% to 2%,

and the discount rate from 5.75% to 2.25%. In

December 2008, the rate lowered to 00.25%. [5],

pointed out that the Fed also undertook open market

operations to ensure member banks remain liquid

and created a variety of lending facilities to enable

the Fed to lend directly to banks and non-bank

institutions against specific types of collateral of

varying credit quality.

Many studies had attempted to suggest the early

signs of a crisis in the banks that could be

preventive ways to maintain financial performance

e.g., [6], [7], [8]. Additionally, [9], summarized

some significant crises that had ever occurred

globally. However, to our knowledge, no study had

ever attempted to examine the consequences of the

financial crisis on the financial indicators of the

banks in the subsequent years, especially in

Indonesia, mainly how the market risk post-

financial crisis would evolve in the next thirteen

years, particularly, how the market risk played a role

in the Indonesian banking industry following the

financial crisis and financial technology disruption.

Instead, the existing literature focused on

investigating the aftermath of the Asian financial

crisis in Indonesian banking from various

perspectives, for example, the net interest margin of

Indonesian banks, [10], market discipline, [11], and

bank ownership, [12], but not in market risk

perspectives yet.

This study aims to investigate the impact of the

crisis on the financial indicators through assets and

liabilities that potentially lose value due to market

risks. Particularly, this study attempts to determine

whether the altered market risk in the post-crisis

years may contribute to financial performance in the

subsequent years and whether the effect of altered

market risks weakens the further the banks move

away from the 2007 crisis. This is due to the

argument by [13], saying that the financial crisis

altered the market risk. This study also aims to

examine the size of market risk before and after the

2016 financial technology disruption in Indonesia to

ensure the effect of market risk in specific periods.

Although there are significant differences between

banks according to liquidity risk, credit risk, equity

risk, and profitability risk, this study assumes that,

on average, the risk of each bank is similar to enable

the investigation of the effect of risk on the financial

performance of the banking industry in Indonesia.

2 Literature Review

Banks prudently maintained financial performance

when they maximized the profits and the wealth of

shareholders. As a result, banks were exposed to

various risks that had an impact on their operations.

One of the major financial dangers to the banks was

a market risk. The market risk was the possible loss

of value in assets and liabilities as a result of

changes in market factors including interest and

exchange rates, equity prices, and commodity

prices, [14]. Although banks frequently restricted

the scope of market risks to the assets and liabilities

covered in trading books, they might also include

the market risk of assets and liabilities that were

designated as available for sale or even hold-to-

maturity assets and liabilities. Market liquidity risk,

in particular the risk that a business would find it

difficult to offset or liquidate a position without

significantly changing the market price due to

insufficient market depth or market disruption, was

a component of market risk for trading positions.

Global financial reforms had motivated banks to

comply with international standards, including

better risk management. In Indonesia, the central

bank of Indonesia, [15], regulated compliance under

Good Corporate Governance (GCG). AFDB, [16],

pointed out that when the banks complied with the

standards, they might improve the capacity to bear

the risks to support their development-related

activities, the core business risks. While market risk

could be relatively easier to control, the global

financial crisis was somewhat harder to predict. Bad

market risk management attenuated the power of the

banks to improve their performance, but

unpredictable financial crises scenario made it

worse. This scenario increased the funding costs and

shrank the liquidity of the banks.

Banks were exposed to different types of core

business risk in doing any activity to maximize

profit while maintaining or improving financial

performance. Therefore, minimizing their exposure

to other sources of non-core risk must also be part of

the agenda for improving financial performance.

One of the recent core business risks attracting

attention was market risks. The rising popularity of

market risk followed the 2007 financial crisis. There

were many views on how market risk could arise.

According to Bank Indonesia, [17], market risk

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could result from the portfolio's adverse movement

in the market, which would cause the bank to incur

losses. The market risk was a result of interest rates

and fluctuating exchange rates. The exchange rate's

market risk was directly correlated with the firm's

value, which was established by market

circumstances while calculating the share price of

the company. [18], explained that market risk was

caused by things like unfavorable price changes for

one or more instruments, which harmed a market

participant's portfolio. Another possible cause was

leveraged positions that squeezed the liquidity and

resulted in extreme losses or even bankruptcy.

Market risk, according to, [19], was the risk

associated with financial assets whose prices were

exogenously decided on financial markets. The

market risk was eliminated if an item was kept until

maturity. [20], stated that market risk was caused by

economic losses coming from adverse changes in

the market value of financial instruments, assets,

and obligations, caused by changes in

macroeconomic variables like interest rates and

stock prices. The key market risks were interest rate

risk, prepayment and extension risk, credit risk,

liquidity risk, and stock price risk. In addition, [20],

stated that market risk included interest rate risk,

currency rate risk, price risk, and banking credit

spread. [21], suggested that market hazards

stemmed from adverse market price fluctuations or

rates, including interest, foreign exchange, and stock

prices. Concerning changes in interest rates, the

level of risk associated with the bank's lending

activities depended on the makeup of its loan

portfolio and the extent to which the conditions of

its loans exposed the bank's revenue stream to rate

fluctuations. Typically, banks identified exposures

with heightened sensitivity to interest rate changes

and devised risk mitigation techniques such as

interest rate swaps.

Typically, market risk had always been confined

to the bank's operations, but the financial crisis had

proven the shifting in its importance. Markets grew

more turbulent post-financial crisis than before, and

asset prices became unpredictable. Broad

deterioration in credit quality, large increases in

funding costs, and squeezes on liquidity had harmed

the bank. The AFDB, [16], observed that market

risk consumed more capital resources than in the

past and, although being a non-core risk, required

higher attention and more active management.

There were five types of market risks: currency,

interest rate, liquidity, equity price, and

counterparty. Market risk interferes with both the

balance sheet and income statement. According to

AFDB, [16], specific to balance sheet risk, market

risk was inherent in the financial instruments

associated with the bank's assets (loan, equity

participations, investments earmarked for trading or

held to maturity portfolios) and liabilities

(borrowings and related derivatives), credit risk

mitigation, and others. Due to the difference in the

total assets and total liabilities, there would be

mismatches of assets and liabilities over a particular

period resulting in a net asset or liability position.

The mismatches could involve the currency, the

interest rate, or the structure of the maturity date.

Any risk arising from a mismatched balance sheet

position, if left unchecked, could result in a possible

loss or gain in the case of a change in interest rates.

One potential loss might include a lowering in

the banking system's efficiency. [22], examined the

effect of market risk in 15 banks in Iran during the

2005-2011 period. They found that both market risk

indicators, interest rate, and exchange rate,

considerably affected the market efficiency.

Notably, a higher interest rate reduced the

efficiency, and appreciation in the exchange rate

increased the efficiency. [23], estimated the

potential losses of the trading using GARCH models

and EVT. They argued that using VaR and E.S. test,

the result showed that the market's increased

volatility might determine the increased losses of

the portfolio. EVT and GARCH models with

structural breaks in the variance showed that higher

capital requirements were necessary, especially

when market shocks appeared.

The association of market risk and financial

performance was still in debate. Among others, [24],

[25], found a reverse relationship between risk

parameters and the financial performance of

commercial banks in Kenya. Notably, market risk

negatively affected profitability (i.e., return on

equity). Using the unbalanced panel data of twenty-

one banks from the years 2003 to 2012, [26], also

showed a negative relationship between risk and

financial performance in commercial banks in

Tanzania. In contrast, a study by [27], on ten leading

banks (i.e., five private banks and five public banks)

in India found that two balance risk parameters (i.e.,

interest rate and liquidity risks) were insignificant to

the profitability. They concluded that the market

risk indicator was insignificant among all risk

parameters. Similarly, [28], discovered no

correlation between market risk and the financial

performance of Malaysian public companies.

While financial crises shared some

characteristics, they could take many different

shapes. Noteworthy changes in asset prices and

credit volume; severe financial intermediation

disruptions and the supply of external financing to

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various economic actors; substantial balance sheet

problems (of firms, households, and financial

intermediaries); and extensive government support

were all characteristics of financial crises (in the

form of liquidity support and recapitalization). As a

result, [29], demonstrated that financial crises were

frequently complex occurrences that were difficult

to pinpoint with a single indicator. Financial

Stability Board, [30], reported that during the 2007

financial crisis, firms faced an increase in market

risks. During the crisis, the firms saw the leveraged

loan market collapse, the asset-backed commercial

paper market almost completely dried up, and the

value of subprime mortgages and some structured

products like collateralized debt obligations and

securities backed by subprime mortgages

plummeted.

Table 1. List of Banks

Banks

BRI Agroniaga

Bank Mandiri

Bank IBK Indonesia

Bank Bumi Arta

Bank Amar Indonesia

Bank Syariah Indonesia

Bank Jago

Bank Maybank Indonesia

Bank MNC Internasional

Bank Permata

Bank Capital Indonesia

Bank CIMB Niaga

Bank Net Syariah

Bank Sinarmas

Bank Central Asia

OCBC

Bank Harda Internasional

Bank BTPN

Bank Bukopin

Bank BTPN Syariah

Bank Mestika Dharma

Bank Victoria International

Bank Negara Indonesia

Bank Oke Indonesia

Bank Rakyat Indonesia

Bank Artha Graha Internasional

Bank Bisnis Internasional

PT Bank Multiarta Sentosa Tbk

BTN Indonesia

Bank Mayapada Tbk

Bank Neo Commerce

Bank China Construction BK

Bank JTrust Indonesia

Bank OUB Indonesia

Bank Danamon Indonesia

Bank Mega Tbk

BPD Banten

Bank of India Indonesia

Bank Ganesha

Bank Nationalnobu

Bank Ina Perdana

Bank Pan Indonesia

BPD Jawa Barat dan Banten

Bank Panin Dubai Syariah

BPD Jawa Timur

Commonwealth Bank

Bank QNB Indonesia

PT Bank Woori Saudara 1906

Bank Maspion Indonesia

Source: Authors’ Data

3 Methods

This study attempts to reveal the importance of

market risk indicators on the financial performance

of forty-nine banks in Indonesia that are selected

based on the data availability.

Table 1 presents the list of banks. This study

observes the financial performance of forty-nine

banks listed on the Indonesian Stock Exchange. The

variables used in this study include return on assets

(ROA), earnings per share (EPS), capital adequacy

ratio (CAR), net interest margin (NIM), operating

income  operating expense (OEOI), interest-

earning assets (so-called market risk-weighted

assets, MRWAs), and interest-earning liabilities (so-

called market risk-weighted liabilities, MRWLs).

These assets and liabilities are total assets (TA)

and total liabilities (TL) subject to the risk of

interest rate fluctuations matured or repriced at

different times or in different amounts. The MRWA

and the MRWL are used to proxy the market risk

indicators (MRIs). Table 2 presents the summary

statistics.

Figure 1 displays the trends of MRWAs. It is

interesting to investigate the patterns of MRWAs.

The annual financial report of the banks shows that

total assets tend to increase over time. Meanwhile,

Figure 1 shows that the MRWAs tend to remain

stable over the period. The report also shows that

between 2016 and 2020, some banks clearly showed

a spike in total assets. Meanwhile, the figure shows

no substantial change in MRWAs during the same

period. It is also clear that many banks did not deal

with MRWAs at the beginning of the period. Some

banks even did not have MRWAs over the period.

Figure 2 presents the trends of MRWLs. Unlike

in Figure 1, Figure 2 shows more variations in

MRWLs than in MRWAs.

Table 2. Summary Statistics

Variable

Obs

Mean

Std. Dev.

Min

Max

BANK

49 banks

YEAR

12 years

2009

2020

EPS

500

114.3225

212.2684

-368.0000

1182.0000

ROA

552

1.4467

4.9145

-20.1300

69.0400

PBV

290

1.3896

1.0408

0.0100

6.0700

CAR

497

31.3819

117.4182

2.2000

2529.4200

NIM

504

5.4488

2.4195

0.2200

19.3000

OEOI

542

90.0698

23.7867

33.2800

261.1000

LDR

428

80.8600

19.0100

5.7600

124.7000

MRWA

(in ln)

327

24.0659

1.8979

18.5143

27.9716

MRWL

(in ln)

276

23.7894

1.8509

18.5096

27.8821

TA (in ln)

406

4.24E+10

6.59E+10

5.84E+07

2.81E+11

TL (in ln)

458

3.12E+10

5.08E+10

4398049

2.40E+11

Source: Authors’ Data

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Fig. 1: The Trend of Market Risk-Weighted Assets

(MRWAs), 2009  2020.

While MRWAs show a relatively flat trend,

MRWLs of some banks show an increasing trend.

Despite fluctuations observed in some banks, most

banks show constant or increasing trends over time.

Both figures indicate that the banks experience

stability in assets and liabilities amid interest rate

fluctuations. Do these stable figures signal whether

interest rate-based market risk still has a significant

role in banking financial performance?

Indonesian banks are exposed to basic risk due to

the difference in repricing characteristics of the

various rate indices such as the Indonesian saving

rate, SBI, and other interest rates. Risk management

activities are directed at optimizing net interest

income as an instrument, taking the market interest

rate into account.

Fig. 2: The Trend of Market Risk-Weighted

Liabilities (MRWLs), 2009  2020.

For this reason, this paper also tries to display the

relationship between MRIs and the net interest

income, which, in this case, is proxied by the net

interest margin. The use of net interest margin can

represent net interest income because the higher net

interest income may be due to a higher net interest

margin and vice versa.

The first investigation made in this study is to

discover if there is any relationship between MRIs

and financial performance, particularly the

profitability indicators. Let  be the financial

performance indicators of bank      

observed at periods       and consider the

following panel data regression model below

    󰆒    (1)

where 󰆒 is a K-dimensional row vector of time-

β is

a K-dimensional column vector of parameters,  is

an individual-specific effect, and  is an

idiosyncratic error term. The variable of interest is

market risk indicators (MRIs), and the controlling

variables are other financial performance indicators.

In this study, the MRIs are proxied by the assets and

liabilities sensitive to the changes in interest rates.

These assets and liabilities are subject to market risk

or so-called market risk-weighted assets (MRWAs)

and market risk-weighted liabilities (MRWLs).

4 Results

4.1 MRIs and Financial Performance

There are two scenarios of analyses executed in this

study. Firstly, the analysis aims to discover the

effect of the assets that are subject to market risk

and the effect of their counterpart on market risk.

The counterpart is the total assets that cover both

assets subject to market risk and assets not subject

to market risk. The study then examines the

difference in the effects. In the second scenario, this

study employs a similar fashion to analyze the effect

of the liabilities that are subject to market risk and

the effect of its counterpart on market risk. Results

from Table 3 show that the MRWAs, indeed,

negatively affected the NIM (i.e., specification (3)),

while ROA and EPS (i.e., specifications (1) and (3))

were unaffected.

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Volume 19, 2023

Table 3. ROA, NIM, and EPS Models with Market

Risk-Weighted Assets (MRWAs)

(1)

(2)

(3)

VARIABLES

ROA

NIM

EPS

-0.0001

0.0007

(0.0025)

(0.0009)

PBV

-0.7136*

-0.0143

-38.6456***

(0.4312)

(0.1493)

(11.7923)

OEOI

-0.0925***

-0.0110

-1.4937**

(0.0230)

(0.0080)

(0.6557)

CAR

-0.1814***

-0.0625***

(0.0389)

(0.0136)

MRWAs

-0.0120

-0.6566***

3.2567

(0.5242)

(0.1925)

(16.2922)

NIM

2.5813

(6.3437)

Constant

14.8333

23.5747***

252.9455

(12.6034)

(4.6610)

(405.8977)

Observations

196

185

203

R-squared

0.2149

0.2023

0.0809

Number of

bank id

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

The result suggests that when banks expand the

level of MRWAs, the profit margin they earn from

their core lending and borrowing activities declines.

This is because a lower NIM indicates that banks

are generating less profit from their core operations

and is therefore viewed as a negative indicator of

financial health. In this case, the gap between the

interest income generated by a bank's assets and the

interest expense incurred by its liabilities narrows.

     

(Table 4), total assets affected both NIM and EPS.

While the effect on NIM is negative, the effect on

EPS is positive. Surprisingly, the roles of assets are

somewhat different.

Table 4. ROA, NIM, and EPS Models with Assets

(1)

(2)

(3)

VARIABLES

ROA

NIM

EPS

-0.0022

0.0015***

(0.0037)

(0.0006)

PBV

-1.1337*

0.3455***

-41.6047***

(0.6188)

(0.0947)

(12.5416)

OEOI

-0.0951***

0.0045

-1.5239**

(0.0302)

(0.0053)

(0.6823)

CAR

-0.3459***

-0.0281***

(0.0599)

(0.0089)

Total Assets

0.1983

-1.1664***

58.3340**

(0.8385)

(0.1544)

(22.8892)

NIM

25.6739***

(9.7332)

Constant

15.1775

31.6062***

-1,121.1399**

(18.6830)

(3.4194)

(538.2936)

Observations

216

200

209

R-squared

0.2004

0.3280

0.1014

Number of

bank id

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

Table 5. ROA, NIM, and EPS Models with Market

Risk-Weighted Liabilities (MRWLs)

(1)

(2)

(3)

VARIABLES

ROA

NIM

EPS

-0.0013

0.0002

(0.0032)

(0.0011)

PBV

-0.8490*

-0.1693

-53.0894***

(0.5055)

(0.1631)

(11.6234)

OEOI

-0.0929***

-0.0147*

-1.2599**

(0.0250)

(0.0080)

(0.6166)

CAR

-0.1862***

-0.0605***

(0.0426)

(0.0138)

MRWLs

-0.0739

-0.4473**

-8.8604

(0.5894)

(0.1944)

(14.7323)

NIM

1.0205

(6.2938)

Constant

16.6924

18.5599***

500.8930

(14.2395)

(4.7113)

(362.6718)

Observations

169

161

168

R-squared

0.2190

0.1886

0.1470

Number of bank

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

When the assets that bear market risk are

extracted from total assets, the MRWAs do not

affect EPS, despite the enormous magnitude of total

assets as shown in Table 4, specification (3).

The MRWLs of the banks have also shown a

significant effect on banks' performance. Table 5

shows that MRWLs significantly affect NIM. The

effect of MRWLs is thirty-two percent lower than

the effect of MRWAs. However, they both

significantly reduce the NIM. Tables 3 and 5 show

that both MRWAs and MRWLs have an

insignificant effect on ROA and EPS.

The lower contribution of MRWLs to NIM than

MRWAs is not surprising. Table 6 shows that the

total liabilities have a lower effect on NIM than the

total assets, only fifty-three percent. Total assets

have significant effects on NIM and EPS.

Meanwhile, total liabilities only have effects on

NIM.

The results from tables 3 through 6 show that the

number of assets held by a bank has a direct effect

on its NIM, as the interest revenue generated by

these assets contributes to the bank's net interest

income. Banks with greater asset levels can produce

more interest income, resulting in greater net

interest margins. Considering NIM is a major

predictor of a bank's profitability, it has a substantial

impact on earnings per share. Generally, banks with

higher NIMs are more profitable and will

consequently have greater EPS. In contrast,

liabilities have a lesser influence on NIM because

the interest expense paid on deposits and other

obligations is already factored into the margin

calculation.

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Table 6. ROA, NIM, and EPS Models with

Liabilities

(1)

(2)

(3)

VARIABLES

ROA

NIM

EPS

-0.0009

0.0012

(0.0037)

(0.0008)

PBV

-0.5210

0.2628**

-27.3406**

(0.5950)

(0.1253)

(10.9990)

OEOI

-0.0503*

-0.0103

-0.5965

(0.0261)

(0.0063)

(0.5345)

CAR

0.1603***

-0.0340***

(0.0423)

(0.0089)

Total

Liabilities

0.4978

-0.6171***

21.4051

(0.9572)

(0.1990)

(18.2551)

NIM

7.8610

(6.6328)

Constant

-0.5667

20.5804***

-291.5152

(21.4636)

(4.4510)

(422.3048)

Observations

237

220

229

R-squared

0.1477

0.1847

0.0571

Number of

bank id

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

However, liabilities do not directly impact profits

per share (EPS) because EPS is a measure of the

bank's performance after expenses and taxes have

been removed. EPS is primarily influenced by the

bank's net income, which is the difference between

the bank's total revenue and total expenses. Other

elements, like operating expenses, loan loss

reserves, and taxes, also contribute to the

computation of earnings per share (EPS), in addition

to the cost of capital (interest expense).

Table 7 shows the difference in magnitudes

between MRWAs and Total Asset and the

difference in magnitudes between MRWLs and

Total Liabilities. While the difference on the assets

side is huge, the difference in the magnitudes of

liabilities variables is relatively minor. Nevertheless,

the magnitudes of total assets and total liabilities in

attenuating the NIM is more powerful than the

magnitudes of MRWAs and MRWLs counterpart.

In other words, the differences, which are 0.5098

and 0.1698 are attributed to the free-market risk

assets embedded in total assets and the free-market

risk liabilities embedded in total liabilities,

respectively.

The results shown in Tables 3 through 7,

covering the data for the 2009  2020 period,

provide essential insights into how different kinds of

assets affect the financial performance indicators

differently. The significant difference is evident in

the effect of market risk indicators (MRIs) on net

interest margin (NIM). This study tries to examine

further the relationship between MRIs and NIM.

Table 7. Difference in Coefficients

(1)

(2)

(3)

VARIABLES

ROA

NIM

EPS

MRWAs minus Total Asset

-0.2103

0.5098***

-55.0773

MRWLs minus Total

Liabilities

-0.5717

0.1698***

-30.2655

Number of bank id under

Asset Specification

Number of bank id under

Liabilities Specification

*** p<0.01, ** p<0.05, * p<0.1

4.2 MRIs – NIM Relationship

Due to the risk management activities that banks

should undertake, including optimizing the net

interest margin (NIM), the data analyses may be

prone to endogeneity issues. Particularly, a

theoretical relationship does not fit into the "y  on 

X" regression which, by the assumption, the

regressors determine the dependent variable. At the

same time, one of the regressors is not determined

by an omitted variable that is part of the error term.

In other words, when an endogeneity problem

exists, at least one of the regressors is endogenous

or jointly determined with the dependent variable.

Due to the endogeneity problem, 󰇛 󰇜  .

Therefore, to show the relationship between

financial performance and MRIs, one must explain

the relationship between MRIs as the endogenous

variable and NIM as the instrumental variable (IV)

using a two-stage least square (2SLS) model. It is

assumed that this IV meets both conditions of IV;

namely, the IV must be valid, that is, 󰇛 󰇜

, and the IV must be correlated with the

endogenous variable 󰇛 󰇜  , which 

denotes the IV. To test 󰇛 󰇜  , one can test

the hypothesis   in      , the

first stage regression. These conditions will

guarantee that the IV estimate is the same as the true

estimate, particularly 

󰆹 

󰇛󰇜

󰇛󰇜  when 󰇛 󰇜  regardless of

the value of 󰇛 󰇜. If 󰇛 󰇜 , then



󰆹  so the IV estimate is inconsistent.

Moreover, if 󰇛 󰇜 is positive, then one will

have a positive bias, and if 󰇛 󰇜 is negative,

then one will have a negative bias. Also, when

󰇛 󰇜 is small, the bias will be larger. This

study assumes 󰇛 󰇜󰇛 󰇜 

󰇛 󰇜, to execute 2SLS analysis with IV. Let

the following equation be the structural model

predicting financial performance for banks  

    observed at periods      .

       (2)

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Volume 19, 2023

where  is the dependent variable, 󰆒 is a K-

dimensional row vector of financial indicators

variable (i.e., time-variant endogenous variables),

and 󰆒is an M-dimensional row vector of control

variables (i.e., time-variant explanatory variables

β is a K-

M-

dimensional column vector of parameters, and  is

an idiosyncratic error term. Then, let z be the

instrument with 󰇛 󰇜  in the following

reduced-form equation that regresses the

endogenous variable on all exogenous ones.

      (3)

where  is the K-dimensional row vector of NIM

(i.e., the instrumental variable),  is the K-

dimensional row vector of other instrumental

variables,  is the intercept,, and  is a K-

dimensional column vector of parameters, and  is

an idiosyncratic error term. The regression of this

equation is also called first-stage regression.

Therefore, IV will remove the attenuation bias when

there is an IV,  such that 󰇛 󰇜 , and

󰇛 󰇜   (i.e.,  ). In the 2SLS setting,

this study no longer uses profitability indicators to

proxy F.P. because the instrumental variable (i.e.,

NIM) used is a type of profitability indicator;

otherwise, it will violate the theoretical requirement

of 2SLS.

Instead, the proxy variable used in this study is

the loan-to-deposit ratio (LDR). The prominent

reason for employing this variable as the proxy is

because both assets and liabilities mathematically

have a relationship to LDR.

Table 8. 2SLS Models

VARIABLES

LDR (1)

LDR (2)

MRWAs

-0.0000

(0.0000)

OEOI

0.0060

0.0180

(0.0570)

(0.1490)

MRWLs

0.0000

(0.0000)

Constant

111.0520

-44.0810

(94.8310)

(705.6270)

Observations

342

Number of bank id

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

Table 9. Market Risk Models Before and After

Financial Technology Disruption in Indonesia (i.e.,

2009  2015 and 2015  2020)

(1) 2009-

2015

(2) 2009-

2015

(3) 2016-

2020

(4) 2016-

2020

VARIABLES

NIM

EPS

0.0028

0.0034

0.0021**

(0.0020)

(0.0022)

(0.0009)

PBV

0.5364*

0.7357*

-0.1143

-0.1425

(0.3158)

(0.3911)

(0.1302)

(0.1217)

OEOI

-0.0170

-0.0085

-0.0105*

-0.0094*

(0.0190)

(0.0204)

(0.0064)

(0.0055)

CAR

-0.1064***

-0.1037***

-0.0074

-0.0064

(0.0261)

(0.0289)

(0.0128)

(0.0114)

MRWA

-0.2111

-0.2933*

(0.3439)

(0.1573)

MRWL

-0.0951

-0.3055*

(0.3547)

(0.1588)

Constant

13.2276

9.2612

13.1500***

13.0838***

(8.9500)

(9.2088)

(3.8288)

(3.8894)

Observations

103

Number of

bank id

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

Based on findings from specifications (1) and (2)

of Table 8, there is no evidence of the relationship

between MRIs (i.e., either MRWAs or MRWLs)

and LDR when NIM is taken as an instrumental

variable. The use of NIM as an instrumental

variable aims to explain risk management when

NIM is one of the tools. Therefore, for this reason,

one can conclude that although, mathematically,

market risk indicators in this study have a close

relationship with LDR, the role of NIM as an

instrumental variable is meaningless.

Another exciting point one may consider when

examining the role of MRIs in the banking business

performance is if MRIs still have a significant

contribution to the profitability performance of the

banks throughout 2009 through 2020, and how the

contribution evolves during that time. For this

reason, this study further examines the impact of

MRIs in different periods.

4.3 The Development of MRIs

This study conducts multi-period analyses. These

multi-period analyses aim to examine whether

MRIs' effect attenuates or grows as the period

moves further away from 2007, the financial crisis

year. This study assumes that the effect of market

risk should attenuate when the MRIs become less

important post-financial crisis era. As seen from

Table 9, the periods of 2009 through 2020 are

divided into two sub-periods, namely the sub-

periods of 2009 to 2015 and 2016 to 2020. The

division of the period considers the disruption of

financial technology in Indonesia. [31], found that

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Volume 19, 2023

there was a significant increase in financial

technology in Indonesia from 2015 to 2016.

Specifications (1) and (3) in Table 9 present the

effect of MRWAs during the sub-periods of 2009

through 2015 and 2016 through 2020, respectively.

Meanwhile, specifications (2) and (4) present

MRWLs during the same sub-periods.

The findings from specifications (1) and (3)

suggest that the effect of MRIs (i.e., the MRWAs) is

statistically insignificant in the 2009  2015 sub-

period but then became significant in the 2016 

2020 sub-period. Meanwhile, the results from

specifications (2) and (4) suggest that MRIs (i.e., the

MRWLs) have shown similar patterns as their asset

counterpart, which means that the effect of MRIs

becomes more potent as moving further away from

the 2007 financial crisis. The results have shed light

on the importance of the market risk in different

periods. During the technology disruption era, the

role of market risk accentuates. Although further

investigation is necessary, one can assume that the

interest rate is one of the key factors that strengthen

the power of market risk. As pointed out by [31], the

interest rate has less power in the technology

disruption period which leads to increasing

uncertainty in the banking industry.

5 Discussion and Concluding

Remarks

The findings show that MRIs affect profitability

indicators but not all. The study has found that

among ROA, NIM, and EPS, NIM stands out as the

only variable affected by the MRIs. The effect

becomes more substantial as moving further away

from the 2007 financial crisis. The effect of

MRWAs as the proxy of MRIs is insignificant in the

2009 - 2015 sub-period but becomes significant in

the 2016  2020 sub-period. Meanwhile, the effect

of MRWLs has shown a similar pattern. That said,

both MRWAs and MRWLs can be powerful tools to

manage risk. Managing liability is vital as this

indicator covers the savings and deposits accounts.

Both assets and liabilities sensitive to interest rates

are proxies of market risk indicators.

The outcomes presented in Tables 3 to 6 indicate

that the number of assets held by a bank has a direct

impact on its NIM. That said, banks with greater

asset levels are more likely to generate more interest

income, which results in higher net interest margins.

Additionally, banks with higher NIMs are more

profitable and will have greater EPS. Conversely,

liabilities have a smaller effect on NIM since the

interest expense paid on deposits and other

obligations are already considered in the margin

calculation. However, liabilities do not directly

influence EPS, which is determined by the bank's

net income. Although there is a significant

difference in the magnitude of asset variables, the

difference in the magnitude of liabilities variables is

relatively minor. Nonetheless, the magnitudes of

total assets and total liabilities in moderating the

NIM are more powerful than the magnitudes of

MRWAs and MRWLs counterparts. In other words,

the differences are attributed to the free-market risk

assets embedded in total assets and the free-market

risk liabilities embedded in total liabilities,

respectively. The results provide crucial insights

into how various types of assets affect financial

performance indicators differently, notably in the

impact of market risk indicators (MRIs) on NIM.

However, further investigation is necessary to

examine the effect of MRIs on other financial

performance indicators. Likewise, further

investigation is needed to discover the reasoning

behind the insignificance of MRIs on ROA and

EPS.

One thing to point out is that before the Fintech

period took place in Indonesia, the role of market

risk was inconsiderable. Perhaps, the fear of having

a prolonged financial crisis made the banking

industry more cautious and reluctant to deal with

riskier financial management, which included the

prevention of rapid credit growth. However, during

the Fintech period, one could observe an increasing

role of market risk on net interest margin. The

increase in the role was triggered by the dependence

of banks on assets and liabilities that bear risks and

so-called MRWAs and MRWLs. Despite the

increase of the market risk role, the contribution of

overall assets and liabilities turned out to have a

bigger negative impact on the net interest margin

than the MRWAs and the MRWLs. These findings

assumed that risk management in Indonesia was still

on track. When the contribution of overall assets

and liabilities became more extensive than that of

MRWAs and MRWLs, it could signal that the risk

management had been in a danger zone.

Nevertheless, further investigation into this

assumption is necessary.

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E-ISSN: 2224-3496

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Contribution of Individual Authors to the

Creation of a Scientific Article (Ghostwriting

Policy)

-Herman Karamoy carried out the trend analyses,

problem formulation, and 2SLS Models.

-Hizkia Tasik carried out the panel data regression

analysis and risk calculations.

Sources of Funding for Research Presented in a

Scientific Article or Scientific Article Itself

No funding was received for conducting this study.

Conflict of Interest

The authors have no conflict of interest to declare.

Creative Commons Attribution License 4.0

(Attribution 4.0 International, CC BY 4.0)

This article is published under the terms of the

Creative Commons Attribution License 4.0

https://creativecommons.org/licenses/by/4.0/deed.en

_US

WSEAS TRANSACTIONS on ENVIRONMENT and DEVELOPMENT

DOI: 10.37394/232015.2023.19.60

Herman Karamoy, Hizkia H. D. Tasik

E-ISSN: 2224-3496

623

Volume 19, 2023