informativeness of earnings and cash flows

INFORMATIVENESS OF EARNINGS AND CASH FLOWS: EVIDENCE

IN INDONESIA, MALAYSIA, AND THAILAND BANKING INDUSTRY

Elisa Tjhoa, University of Indonesia

Ancella Anitawati Hermawan, University of Indonesia

Abstract

Financial statements’ information, particulary net income and cash flows from

operations has been widely used by investors as one of the basis in making

investment decisions. Interests are the main source of income in the banking

industry, and therefore the information of which are considered to have a

significant role for the investors. The objective of this study is to examine the

informativeness of earnings, cash flows from operations, and net interest income

in the banking industry in three South East Asian countries, i.e. Indonesia,

Malaysia, and Thailand. The hypothesis testing is carried out using multiple

regression method with the sample of publicly listed Banks in each country during

the year of 2006 to 2010. The empirical results show that net income of banking

industry is informative in Indonesia and Thailand, but not in Malaysia. In

Malaysia, cash flows from operations is more informative, similar to Thailand. In

term of interest icome, Malaysia and Thailand indicates that this information is

informative, but not in Indonesia. In Thailand, the cash flows for interest is also

informative.

Key words : earnings response coefficient, net interest income, cash flows from

operations, cash flows for interest, bank.

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1. Introduction

The information about the company or the market condition is very

crucial for capital market investors in making their investment decisions. Optimal

decisions need high quality information, i.e. relevant and reliable information.

One source of information that is available for the investors to evaluate the

company’s prospective performance is the income statement and cash flows

statements. Bruns dan Merchant (1990) state that the cash flows statement provide

better information, since the accrual basis used in the income statements allows

the company’s management to manage the earnings reported. But Cheng et al.

(1997) conclude that net income still have value relevance, and cash flows from

operations contribute an incremental value relevance on top of the net income.

The quality of earnings reported in the financial stements is considered

high if it can be a valid base to predict to future company’s performace, but this

quality is often difficult to measure (Dechow et al., 2010). One approach to

measure the earnings quality is based on the earnings response coefficient (ERC).

Investors perceptions about the quality of earnings is captured by how the

investors react to the earnings information in the capital market. Teoh and Wong

(1993) shows that investors are willing to pay a higher price for earnings of "high

quality" because the high-quality earnings are seen as sustainable profits.

Therefore, earnings quality is measured based on the response of investors on the

information content of accounting earnings (informativeness of accounting

earnings). The magnitude of the change of abnormal retuns associated with the

change in unexpected earnings is called the earnings response coefficient (ERC).

The ERC will be high if investors perceive the informativeness of earning is high,

meaning that the earnings quality is high.

Banks have a unique business process with its intermediaries roles in the

financial market. Eventhough the industry is very highly regulated due to the

nature of the business, the business risk of banking industry is relatively high.

The standard of financial reporting format is different from other industries to be

able to present the relevant business performance results. Therefore, it is

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interesting to know which information regarding the result of the operating

performance that represents higher quality information from the investors’

perspective. As one of the emerging countries, Indonesian economy is still rely

more on banks for the financial system than the capital market. The banking

industry has shown a high growth rate in the past two decades. The performance

of the banks stock price in the market is usually better than other companies,

therefore banks stocks are usually taken as the underlying asset of a mutual fund.

But some Indonesian banks have also experienced financial difficulties, which

ended up with merger or acquisition by other banks, or declared bankruptcy. In

order to evaluate the bank’s prospect in profitability and risks, investors may have

different perception for every information item in the financial statement

depending on their perceived quality of such information. This study examines

how the investor’s response for the information of earnings, cash flows from

operations, and net interest income reported by banks in Indonesia. In addition, to

be able to understand how the quality of banks financial report in Indonesia

compared to other countries, the study includes the banking industry in Malaysia

and Thailand. As the member of South East Asian Nations, the three countries in

this study assumed to have some similarities and differences in the

macroeconomics conditions, government regulations, and financial reporting

standards, therefore comparing among those countries for this study will provide

some more insight about the banking industry financial reporting quality.

2. Literature Review and Hypothesis Development

Research and empirical studies on the effect of earings on stock returns

has been done many times before. Ball dan Brown (1986) are one of the pioneers

in the research, and found that there were movements in stock price around the

company’s accounting income announcements. Kothari (2001) also finds strong

correlations between the stock price movements with the company’s income

movements. Dastgir et al. (2004) show that the correlation between net income

and stock returns is stronger than the correlation between cash flows and stock

returns. But in a research on effect of SFAS No. 95 Statement of Cash Flows on

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stock price movements, Cheng et al. (1997) find that both net income and cash

flows from operations have positive and significant impact on stock returns.

Chen (2009) concludes that both accounting income and cash flows from

operations has significant effect on price movements. But the effect of accounting

income is stronger in predicting stock returns in longer period, while the cash

flows from operation is stringer in predicting stock returns in shorter period.

Dastgir et al. (2009) performed a research to see the effect of income statements

and cash flows statements informations on the stock returns for Tehran Stock

Exchange, using the components of the reports as independent variables of the

research. The components of income statements used are gross profit, operating

income, income before tax and net income. While the cash flows statements

componenets used are cash flows from operating activities, cash flows from

investing activities and cash flows from financing activities. The result of the

study showed that the component of income statements with the most significant

effect on stock returns is net income, whereas the component of the cash flows

statements with the most significant effect on stock returns is cash flows from

investing activities.

The quality of earnings refers to the relevance of earnings in measuring a

company’s performance (Subramanyam and Wild, 2009). Besides, the quality of

earnings also can be defined as the conservatism level of reporting by a company,

in which the company with higher price to earnings ratio will indicate a higher

quality of earnings. Statement of Financial Accounting Concepts No. 1 (SFAC

No. 1) stated that financial reporting should provide information regarding the

company’s financial performance in certain period. Dechow et al. (2010) defined

the quality of earnings as earnings that provide information regarding a company;s

financial performance, which could influence the decision made by a decision

maker.

Earnings response coefficient could be defined as the measure of abnormal

market return of a stock as the response of unexpected component of earnings

announced by the company (Scott, 2009). Ambarwati (2008) describes that

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earnings response coefficient will be based in the investor’s expectation on the

earnings before its announcement. Approaching the earnings announcement date,

the information obtained and gathered by investors will also increase. If the actual

earnings is higher than the investor’s expectation, then there would be good news,

and investors will decide to buy the shares. On the other hand, if the actual

earnings is lower than what was expected, there would be bad news, and the

investors will decide to sell the shares.

Earnings response coefficient will be different for each company, and is

influenced by a number of factors. Biddle and Seow (1991) and Ahmed (1994)

show that earnings response coefficient will be significantly influenced by the

company’s characteristics. Teoh and Wong (1993) state that the auditors also

influence the company’s earnings response coefficient. Their study show that the

auditor’s quality, or auditor with bigger scale of reputation will be more reliable,

which is proved by the higher earnings response coefficient in companies audited

by Big Six audit firms. This is caused by the perception of the investor that the

financial reports audited by the big six audit firms are less vulnerable to

misstatements compared to those who were audited by non Big Six audit firms.

Study of earnings response coefficient on banking sector has been done by

Ariff dan Cheng (2011) for Asia Pacific countries such as Australia, South Korea,

Malaysia, and Thailand. In the study, in addition to observe the effect of total

earnings information on stock price movements, they also examine the effect of

disaggregated non-interest fee income information.This study finds that the total

earnings movement in the financial reports have a positive and significant effect

on stock price movement in all four countries. As for the effect of the change in

disaggregated non-interest fee income to the stock price, this study shows a

positive effect in Australia, South Korea, and Malaysia, but a negative effect in

Thailand. They also find that there is no association between the level of

disaggregated non-interest fee income and stock price movement.

Based on the study of Ariff dan Cheng (2011), the first hypotheses in this

study is:

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H1a : Unexpected earnings is positively associated with cumulative abnormal

return

Interest revenue and expense are the results of main operational activities

in banks. Therefore the financial statements users often reflect the operational

performance of banks for the net interest income achieved during a ertain period.

Based on the above, the second hypotheses of this study is:

H2a : Unexpected net interest income is positively associated with cumulative

abnormal returns.

This study would also observe the quality of components on cash flows

statements, so the third and fourth hypotheses are:

H3a : Unexpected cash flows from operations is positively associated with

cumulative abnormal returns.

H4a : Unexpected net cash flows from interest is positively associated with

cumulative abnormal returns.

3. Research Method

3.1 Data and Sample

The sample of this study are publicly listed banks in the stock exchange of

Indonesia, Malaysia, and Thailand during the period of 2006 to 2010. Table 1

shows that total sample is 30 banks, therefore the total observation for five-year

period for all countries is 150 observations. The list of the banks used as the

sample in this study is presented in the appendix.

The data used are from the companies annual report taken from the stock

exchange or company’s website, and also from Yahoo finance and Bloomberg..

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Table 1 Sample Determination

No. Sample Criteria Indonesia Malaysia Thailand

1 Number of Banks listed on the

stock exchange as of May

2012.

42 27 47

2 Banks that are listed during

the whole period of study

(2006-2010)

(10) 0 (2)

3 Banks with negative equity (1) 0 0

4 Banks with incomplete data (6) 0 0

5 Total sample 13 8 9

3.2.Research Model

The test on the above hypotheses are performed using the multiple

regression method, that is the effect of unexpected earnings and unexpected cash

flows from operations, and also unexpected net interest earnings and unexpected

net cash flows from interests on stock returns.

Hypotheses 1 to 4 will be tested using the following 2 models :

= + . + . + . + . +

. + . +

= + . + . + . + . +

. + . +

Where:

= cumulative abnormal return stock company i in year t

= unexpected earnings company i in year t

= unexpected cash flows from operations comapany i in year t

= unexpected net interest earnings company i in year t

= unexpected net cash flows from interest company i in year t

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= risk of company i measured by beta in year t

= loan to deposit ratio of company i in year t

= price to book value (PBV) ratio of company i in year t

= size of company i measured by outstanding shares times closing

price in year t

3.3 Variable Measurements

The dependent variable in this study is the company’s market adjusted

stock return proxied by the cumulative abnormal market adjusted return. Stock

price data used are the weekly closing price of public companues in banking

industry and weekly closing rice of the composite index on each country

observed. For companies with reporting period ended 31 December, the stock

return period used are from 1 April 2006 to 31 March 2011. For companies with

reporting period ended 31 March, the stock return period used are from 1 July

2006 to 30 June 2011. While for companies with reporting period ended 30 June,

the stock return period used are from 1 October 2006 to 30 September 2011.

( )

( )

( )

( )

From the above we could get weekly market adjusted return which were

cumulated for one year to obtain cumulative abnormal return which is the

dependent variable in this study.

∑

Where:

= stock return of company i in week w

= market return in week w

= stock price of company i in week w

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( ) = stock price of company i in week (w-1)

= stock exchange index in week w

( ) = stock exchange index in week (w-1)

= market adjusted return company i in week w

= cumulative abnormal return company i in year t

The independent main variables in this study comprised of unexpected

earnings, unexpected interest earnings, unexpected cash flows from operations

and unexpected cash flows from interests.

Proxy for unexpected earnings was calculated as follows:

( )

( )

Proxy for unexpected net interest earnings was calculated as follows:

( )

( )

Where:

= proxy unexpected earnings company i in year t

= earnings per share company i in year t

( ) = stock price company i in year (t-1)

= proxy unexpected net interest earnings company i in year t

= net interest earnings per share company i in year t

In this study, the proxy for unexpected cash flows from operations was

calculated as follows:

1

0

( )

( )

While the calculation for the proxy of unexpected cash flows from

interests will be as follows:

( )

( )

Where:

= proxy unexpected cash flows from operations company i in year t

= cash flows from operations per share company i in year t

= proxy unexpected net cash flows from interest company i in year

t

= net cash flows from interest per share company i in year t

The control variables includes:

Loan to Debt Ratio

Based on Mulyono (1995:101), the loan deposit ratio is the ratio that

compares funds distributed to the society as loans with the funds gathered from

the market and its own capital. The ratio describes the ability of the bank to repay

the depositor using the loans as its source of liquidity. The higher the ratio, the

lower the liquidity of the bank (Dendawijaya, 2000:118). The loan to deposit ratio

is calculated as follows:

1

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Company’s Risk

The research by Murwaningsari dan Rachmanto (2011) showed that a

company’s risk can significantly influence the stock return. The company’s risk is

reflected in the company’s beta obtained by performing regression on weekly

stock return with the market return for each company observed. The beta value is

obtained using the following formula (Bodie et al., 2008):

Where:

= stock return company i for period t

= market return for period t

= intercept of regression between stock and market return

= slope of regression between stock and market return which

shows the response of stock return to movement of market price

regresi

Growth Opportunity

Senthilkumar (2009) and Tresnaningsih (2007) in their research proxied

the company’s growth opportunity using market price to book equity ratio. The

ratio is calculated as follows:

Company’s Size

Quiroz and Timmermann (1999) stated that the stock return is also

influenced by the size of the company, whereas the smaller the size of the

company, the more the chance to have asymmetric information which can

influence the company’s stock return.Based on the study of Siregar and Utama

(2008), the size of the company can be proxied by the market value of equity in

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end of year, which is obtained by multiplying outstanding shares with closing

price at end of year.

4. Results

4.1 Descriptive Statistics

Table 2 shows that on average, the value of cumulative abnormal return CAR for

Indonesian banks in the sample is higher than the other two countries. Higher

return also means higher risks, and it is represented by higher standard deviation

Table 2 Descriptive Statistics

MINIMUM MAXIMUM

MEAN STANDARD DEVIATION

Indonesia Malaysia Thailand Indonesia Malaysia Thailand

Indonesia Malaysia Thailand Indonesia Malaysia Thailand

CAR -0,75 -0,34 -1,03 2,94 0,94 1,16 0,28 0,17 0,05 0,63 0,30 0,38

UE -0,09 -0,09 -1,28 0,22 0,12 4,02 0,02 0,02 0,08 0,05 0,04 0,65

UNIE -2,64 -0,13 -3,08 0,46 0,14 0,48 -0,05 0,01 -0,07 0,47 0,04 0,48

UCFO -2,21 -1,96 -11,10 2,04 2,63 2,35 0,03 0,08 -0,26 0,86 0,91 2,02

UNCFI -0,31 -0,13 -2,90 0,28 0,14 1,43 0,03 0,01 -0,04 0,09 0,04 0,50

RISK -0,02 0,69 0,40 1,30 1,53 1,25 0,71 1,06 0,84 0,30 0,18 0,20

LDR 0,35 0,50 0,57 1,18 0,88 1,59 0,67 0,71 0,98 0,17 0,10 0,18

GROWTH 0,56 0,46 0,32 6,07 3,73 4,37 1,97 1,60 1,42 1,03 0,81 0,79

SIZE (in

million LC) 172.638 2.017 1.557 157.792.064 66.855 351.136

25.060.550 18.622 110.419 33.162.458 17.176 93.343

Number of observation: 150

CAR = cumulative annual weekly abnormal return, UE = unexpected earnings, UNIE = unexpected net interest earnings, UCFO = unexpected cash flows from

operations, UNCFI = unexpected net cash flows from interests, RISK = company’s beta, LDR = loan to deposit ratio, GROWTH = company’s growth opportunity

measured by price to book value, SIZE = company’s size measured by the market value of equity at the end of fiscal year in local currency

of Indonesian banks in the sample compared to Malaysia and Thailand. Banks.

This reflects that the Indonesian banks have higher risks than Malaysian and

Thailand banks..

For the value of the UE, Thailand banks which are included in the sample

have the highest average UE compared to the other two countries. This suggests

that investors in Thailand have more uncertainties which are not able to be

identified beforehand and therefore these factors are not determined in the

expected earnings.

UNIE value in Indonesia and Thailand on average is negative, which

indicates that there is a decline in net interest income during the period of 2006-

2010. This is due to a decrease in interest rates in the two countries during that

period. As for Malaysia the average net interest income positive, so maybe the

situastion in Malaysia is somewhat different. In addition, Thailand banks average

UCFO and UNCFI are also negative, which reflects that their economic condition

may not as good as the other two countries. In term of LDR, Thailand banks have

the highest ratio compared to Indonesia and Malaysia. But in general, LDR from

all countries in the sample shows that the banks have conducted the intermediaries

role quite well.

The correlation between each variables used in the research model is

presented in the appendix 2.

4.2. Regression Result

The regression results are presented in table 3 and 4 and the hypothesis testing

analysis is discussed in the following section.

4,2,1 Informativeness of Net Income

Test on hypotheses 1a is performed to gain knowledge on quality of net

income based on the investor’s perspective, where the coefficient of the test will

indicate the quality on informativeness of net inceome to the investors.

Corresponds to the the study by Ariff and Cheng (2011) and other previous

researches regarding the effect of net income on stock price movements, the

results for Indonesia and Thailand showed positive signs, with p-value

Table 3: Regression Result Model 1

= + . + . + . + . + . + . +

Indonesia Malaysia Thailand

Variable

Exp

Sign

Unstd

Coef t-Stat Sig. Exp

Sign

Unstd


Sign

Unstd

Coef t-Stat Sig.

B

(Constant) 1,568 1,649 0,052* 0,658 0,806 0,213 -0,793 -0,905 0,186

UE + 2,973 1,884 0,032** + 0,225 0,262 0,397 + 0,430 3,990 0,000***

UCFO + -0,088 -1,106 0,137 + 0,079 1,893 0,034** + 0,066 1,521 0,069*

RISK + 0,488 1,760 0,042** + -0,484 -1,832 0,038** + 0,170 0,625 0,268

LDR + 0,808 1,648 0,053* + 1,005 1,975 0,028** + 0,109 0,329 0,372

GROWTH + 0,043 0,552 0,291 + 0,254 3,961 0,000*** + 0,106 1,354 0,092*

LNSIZE - -0,147 -2,244 0,014** - -0,068 -1,199 0,120 - 0,039 0,605 0,274

R-squared 0,417 0,510 0,408

Adjusted R-squared 0,344 0,421 0,293

F-statistic 5,726 5,735 3,543

Prob(F-stat) 0,000 0,000 0,005 *** Significance at the level of = 1% (1-tailed)

** Significance at the level of = 5% (1-tailed)

* Significance at the level of = 10% (1-tailed)




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Table 4: Regression Result Model 2

= + . + . + . + . + . + . +

Indonesia Malaysia Thailand

Variable

Exp

Sign

Unstd


Sign

Unstd


Sign

Unstd

Coef t-Stat Sig.

B

(Constant) 1,689 1,759 0,042** 0,745 0,980 0,167 -0,957 -1,478 0,074*

UNIE + -0,197 -0,737 0,232 + 2,575 2,363 0,012** + -0,449 -7,153 0,000***

UNCFI + 0,126 0,127 0,450 + 0,014 0,160 0,437 + 0,315 2,784 0,004***

RISK + 0,610 2,069 0,022** + -0,436 -1,654 0,054* + 0,228 0,806 0,213

LDR + 0,802 1,812 0,038** + 0,980 2,039 0,025** + 0,231 0,854 0,199

GROWTH + 0,044 0,539 0,296 + 0,269 4,467 0,000*** + 0,112 1,102 0,139

LNSIZE - -0,158 -2,294 0,013** - -0,077 -1,474 0,075* - 0,037 0,686 0,249

R-squared 0,368 0,570 0,416

Adjusted R-squared 0,289 0,492 0,323

F-statistic 4,666 7,285 4,505

Prob(F-stat) 0,000 0,000 0,002 *** Significance at the level of = 1% (1-tailed)






= 5% for Indonesia, and = 1% for Thailand. The earnings response coefficient

value for Indonesia and Thailand are 2.973 and 1.443, respectively. The results

indicate that the information of net income on banking industry for those countries

perceived to have a good quality by the investors. The ERC value for Indonesia is

higher than Thailand, which suggests that the earnings quality of Indonesian

earnings report in banking industry is better than Thailand. The finding in this

study for Malaysian context does not support Arrif and Cheng (2011) which state

that ERC of banking industry in Malaysia is significant. The different results

could be due to different time range and sample of study. Therefore the result for

Malaysia is still mixed.

4.2.2 Informativeness of Net Interest Income

Test on hypotheses 2a is performed to gain knowledge on the

informativeness of net interest income as one of the main componenet for banks.

In this test, the hypothesis is only supported for Malaysian banks, with the p-value

= 0.012 and ERC value 2.575. Related to the result for hypothesis 1, this finding

suggests that in Malaysia, the information about interest income is considered to

have higher informativeness than net earning information. This condition is the

opposite of Indonesian situation, where the net earnings information has a high

informativeness, but interest income information is considered low quality and

less informative. On the other hand, the test for Thailand shows opposite results,

with ERC value -0.449 and p-value = 0.000. This findings suggests that the

increase in unexpected interest income has a negative impact on the investor’s

perception.

4.2.3 Informativeness of Cash Flows from Operations

Test on hypotheses 3a is performed to gain knowledge on quality on

informativeness of cash flows from operating activities. Among the three

countries tested, Malaysia and Thailand showed positive and significant results on

= 5% and = 10%, respctively. The results for these countries correspond with

study of Chen (2009) which stated that cash flows information also has significant

effect on stock returns. The findings in this study suggests that informativeness of

cash flow from operations which reflects the quality of cash flow statement only

1

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happens in Malaysia. Related to the findings for hypothesis 1a for earnings

informativeness in Malaysia which is not supported, it can be concluded that

information about cash flow from operations is perceived to be more informative

for investors than information about earnings. As for Thailand, the

informativeness of cash flows from operations is still weak, therefore investors

rely more on the earnings information. In Indonesia, hypothesis 3a is not

supported, which means that investors do not consider cash flow from operations

to be informative, and rely more on net earnings information.

4.2.4 Informativeness of Cash Flows from Interests

Test on hypotheses 4a is performed to gain knowledge on the

informativeness of cash flows from interests. The results of the tests shows that

the hypothesis is only supported by Thailand’s result with the level of = 1%.

This findings suggests that the information about cash flow from interests in

Thailand is considered informative by the investors., but not the interest income

based on accrual basis. Based on hypothesis 2a, positive unexpected interest

income in Thailand perceived negatively by investors. In contrast, Malaysian

investors consider interest earnings information to be informative, but not the cash

flow form interest. In Indonesia, both information about interest, accrual based

and cash flow based, do not have any informativeness for the investors..

4.2.5 Control Variables Analysis

In the first model, risk factor significantly influences stock return for tests

in Indonesia and Malaysia at level = 5%, while in Thailand the risk factor has

insignificant effect. What is interesting from the result is that the test in Indonesia

and Thailand show positive signs which indicate that the higher the company’s

risk the higher the stock return, while in Malaysia shows negative sign which

indicates that the higher the risk the lower stock return. The difference could be

cause by the investors in Indonesia perceived higher risk in the banks as higher

opportunities forthem to gain higher returns from the uncertainty movement of

stock prices. While the investors in Malaysia’s banking industry view the

company’s risk will have bad influence to the stock price movements. The second

model has slightly different results, where the risk factor has only significant

1

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effect in Indonesia and Malaysia, with positive effect in Indonesia and negative

effect in Malaysia. These effects are consistent with the results in the first model.

In Thailand, the risk variable in the second model does not have significant

influence on stock returns.

For loan to deposit ratio variable, the first and second model yield similar

results, where the variable has positive and significant effect only for Indonesia

and Malaysia. The result indicate that only the Indonesian and Malaysian markets

view the deposit fund distribution in the form of loans has good impact on the

company’s investments, which in turn wull increase the company’s earnings and

stock return. On the other hand, the tests result for Thailand showed that the loan

to deposit ratio has insignificant effect on stock return.

Growth opportunity variable has a consistent positive effect on stock

returns in Malaysia. For Thailand, growth opportunity has a positive and

significant effect on stock returns only in the first model. In Indonesia, growth

opportunity does not have any effect on stock returns, but company’s size is

consistently has a negative and signifcant effect on stock returns. This result

indicates that the larger the size of the company the higher the company’s stock

returns is. The result could be due to investors’ view that smaller company has

larger opportunities to be developed, therefore the opportunity for increase in

return is also larger. It could also caused by investors’ perception that smaller

company also contains higher risk, which corresponds with the explanation above,

will have significant and contraty effect with the stock return.

In Malaysia, company’s size has a weak negative effect on stock returns only

in the second model. Therefore, in Malaysia growth opportunity has more

influence on stock returns than company’s size. In Thailand, company’s size does

not have any effect on stock returns, but growth opportunity does.

5. Conclusion

This study finds that the informativeness of information from the income

statement and cash flow statement in the banking industry is different for every

2

0

country. The component in each report has also different informativeness level.

In Indonesian banks, net income is the only component that is perceived to be

informative. On the other hand, in Malaysian banks information from the

income statement that is censidered informative by the investors is net interest

income rather than net income. But from the cash flow statements, cash flows

from operations is informative, rather than cash flows for interests. In Thailand,

both net income and interest income information, based on accrual or cash

flows, are all informative. These findings are interesting because the

informativeness of earnnings and earnings component in the banking industry is

different for each country. Further research can explore more on factors that

could be the determinants for these differences, for example the corporate

governance, the government regulation, the adoption of accounting standards,

etc. Besides, the findings in this study also indicate that the the accrual based

earnings informativeness and cash flows based informativeness can be

substituteable. Also, the component of earnings informativeness can subtititute

the net earnings informativeness. Further research can also explore more on

other components in the income statement which can also informative for the

investors.

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Appendix 1: List of Banks Used as Sample

No.

Code

(Bloomberg) Company Name Country

1 BABP:IJ Bank Icb Bumiputera Tbk. Indonesia

2 BBCA:IJ Bank Central Asia Tbk. Indonesia

3 BBNI:IJ Bank Negara Indonesia (Persero), Tbk Indonesia

4 BBRI:IJ Bank Rakyat Indonesia (Persero), Tbk Indonesia

5 BDMN:IJ Bank Danamon Indonesia Tbk. Indonesia

6 BKSW:IJ Bank Qnb Kesawan Tbk. Indonesia

7 BMRI:IJ Bank Mandiri (Persero) Tbk. Indonesia

8 BNII:IJ Bank Internasional Indonesia Tbk Indonesia

9 BNLI:IJ Bank Permata Tbk Indonesia

10 BVIC:IJ Bank Victoria International Tbk Indonesia

11 MEGA:IJ Bank Mega Tbk. Indonesia

12 NISP:IJ Bank Ocbc Nisp Tbk Indonesia

13 PNBN:IJ Bank Pan Indonesia Tbk Indonesia

14 AHB:MK Affin Holdings Bhd Malaysia

15 AFG:MK Alliance Financial Group Bhd Malaysia

16 AMM:MK Ammb Holdings Bhd Malaysia

17 CIMB:MK Cimb Group Holdings Berhad Malaysia

18 HLBK:MK Hong Leong Bank Bhd Malaysia

19 MAY:MK Malayan Banking Bhd Malaysia

2

5

Appendix 1: List of Banks Used as Sample (Continued)

No.

Code

(Bloomberg) Company Name Country

20 PBK:MK PUBLIC BANK BHD Malaysia

21 RHBC:MK RHB CAPITAL BHD Malaysia

22 BAY:TB Bank of Ayudhya PCL Thailand

23 BBL:TB Bangkok Bank PCL Thailand

24 CIMBT:TB CIMB Thai Bank PCL Thailand

25 KBANK:TB Kasikornbank PCL Thailand

26 KK:TB Kiatnakin Bank PCL Thailand

27 KTB:TB Krung Thai Bank PCL Thailand

28 SCB:TB Siam Commercial Bank PCL Thailand

29 TCAP:TB Thanachart Capital PCL Thailand

30 TMB:TB TMB Bank PCL Thailand

Appendix 2: Pearson Correlation – Indonesia Model 1

= + . + . + . + . + . +

. +

CAR UE UCFO RISK LDR GROWTH LNSIZE

CAR 1,00

UE 0,334** 1,00

(0,006)

UCFO -0,205

(1,101)

-0,073

(0,562)

1,00

RISK 0,210 0,348**

0,063 1,00

(0,093) (0,004) (0,617)

LDR 0,240 -0,010 -0,020 0,140 1,00

(0,055) (0,935) (0,875) (0,267)

GROWTH -0,204 -0,129 0,032 0,014 0,034 1,00

(0,104) (0,304) (0,802) (0,911) (0,787)

LNSIZE -0,347**

(0,005)

0,028

(0,824)

0,158

(0,208)

0,407**

(0,001)

-0,042

(0,738)

0,491**

(0,000)

1,00

Pearson Correlation – Indonesia Model 2

= + . + . + . + . + . +

. +

CAR UEI UCFI RISK LDR GROWTH LNSIZE

CAR 1,00

UNIE -0,286* 1,00

(0,021)

UNCFI -0,105 0,547**

1,00

(0,406) (0,000)

RISK 0,210 -0,107 0,000 1,00

(0,093) (0,397) (0,999)

LDR 0,240 0,038 0,054 0,140 1,00

(0,055) (0,762) (0,672) (0,267)

GROWTH -0,204 0,174 -0,008 0,014 0,034 1,00

(0,104) (0,165) (0,952) (0,911) (0,787)

LNSIZE -0,347**

0,277* 0,198 0,407

** -0,042 0,491

** 1,00

(0,005) (0,026) (0,115) (0,001) (0,738) (0,000)



Number in parantheses is p-value

Pearson Correlation – Malaysia Model 1

= + . + . + . + . + . +

. +


CAR 1,00

UE 0,047 1,00

(0,775)

UCFO 0,159 0,140 1,00

(0,326) (0,388)

RISK -0,333* 0,047 0,119 1,00

(0,036) (0,773) (0,466)

LDR 0,090

(0,583)

0,003

(0,985)

0,008

(0,962)

0,493**

(0,001)

1,00

GROWTH 0,618**

(0,000)

-0,045

(0,781)

-0,115

(0,479)

-0,398*

(0,011)

-0,029

(0,860)

1,00

LNSIZE 0,334* -0,115 -0,136 -0,198 0,305 0,635**

1,00

(0,035) (0,479) (0,402) (0,221) (0,055) (0,000)

Pearson Correlation – Malaysia Model 2

= + . + . + . + . + . +

. +


CAR 1,00

UNIE 0,284

(0,076)

1,00

UNCFI 0,036

(0,826)

0,542**

(0,000)

1,00

RISK -0,333*

(0,036)

0,020

(0,902)

0,321*

(0,043)

1,00

LDR 0,090

(0,583)

0,013

(0,937)

0,084

(0,604)

0,493**

(0,001)

1,00

GROWTH 0,618**

(0,000)

-0,101

(0,535)

-0,186

(0,250)

-0,398*

(0,011)

-0,029

(0,860)

1,00

LNSIZE 0,334*

(0,035)

-0,060

(0,713)

-0,102

(0,531)

-0,198

(0,221)

0,305

(0,055)

0,635**

(0,000)

1,00




Pearson Correlation – Thailand Model 1

= + . + . + . + . + . +

. +


CAR 1,00

UE 0,465**

(0,001)

1,00

UCFO -0,059

(0,701)

-0,615**

(0,000)

1,00

RISK 0,132 -0,104 0,239 1,00

(0,386) (0,495) (0,114)

LDR -0,007

(0,965)

-0,196

(0,197)

0,396**

(0,007)

0,076

(0,621)

1,00

GROWTH 0,249 0,045 0,107 0,112 -0,091 1,00

(0,100) (0,769) (0,484) (0,462) (0,554)

LNSIZE 0,212

(0,162)

-0,146

(0,338)

0,297*

(0,047)

0,528**

(0,000)

0,038

(0,803)

0,242

(0,109)

1,00

Pearson Correlation – Thailand Model 2

= + . + . + . + . + . +

. +


CAR 1,00

UNIE -0,391**

(0,008)

1,00

UNCFI -0,230

(0,129)

0,307*

(0,041)

1,00

RISK 0,132 0,141 -0,015 1,00

(0,386) (0,357) (0,923)

LDR -0,007 0,330* 0,188 0,076 1,00

(0,965) (0,027) (0,216) (0,621)

GROWTH 0,249 -0,140 -0,236 0,112 -0,091 1,00

(0,100) (0,360) (0,118) (0,462) (0,554)

LNSIZE 0,212 0,221 0,215 0,528**

0,038 0,242 1,00

(0,162) (0,144) (0,155) (0,000) (0,803) (0,109) ** Signifikan at the level = 1% (2-tailed)

* Signifikan at the level = 1% (2-tailed)


informativeness of earnings and cash flows

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