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1 INTRODUCTION

If the money supply in the economy is like the blood that flows in the human body, then the financial sys-tem (which is largely dominated by the banking sys-tem) is the heart. Strong Heart will be able to pump blood throughout the body so that the body becomes healthy and sustainably balanced. Likewise, the strong and healthy banking system will be able to mobilize funds, develop and grows strong economy, and leads into prosperity. It is clear that strong and healthy banking system is required to create a devel-oping economic system. The world financial crisis condition is impacting the domestic financial sector, particularly the banking sector, where the banks as a financial institution have a vital influence on the economy of a country. Several indicators indicates that the global crisis ef-fecting the Indonesian economy, including the wea-kening Rupiah exchange rate at the level of 10,000 for an American dollar, the lack of banking liquidity, and there are at least 19 banks potentially enter the

intensive supervision of Bank Indonesia, as Non Performing Loan ( NPL ) raise above 5 % ( Bank Indonesia , 2010) . Banking business is always a high risk business and received a special attention from the government through the pillars set out in regulations such as CAMEL, as stated in Bank Indonesia Circular Letter No. 26/5 / BPPP, dated May 29, 1993 and refined in-to CAMELS in Bank Indonesia Regulation ( PBI ) 6 / 10 / PBI / 2004 dated 12 April 2004 and Circular 6 / 23 / DPNP dated May 31, 2004 on the System Rat-ing for Commercial Banks . These pillars formed by government in order to ensure that the banks are running healthy, because unhealthy bank becomes an economy burden. How financial distress can be predicted becoming very important for the stakeholders of the company, so that the topics remain interesting to observe, be-cause the late identification of the financial distress will cost resolving effort on businesses financial dis-

FINANCIAL DISTRESS PREDICTION ON PUBLIC LISTED BANKS IN INDONESIA STOCK EXCHANGE

Dionysia Kowanda Faculty of Economics, Gunadarma University

Rowland Bismark Fernando Pasaribu Faculty of Economics, Gunadarma University

Muhammad Firdaus Faculty of Economics, Gunadarma University

ABSTRACT:

A financial distress of company should be able anticipated smartly by its management to rerun the business without having any loss due to business failure. Thus, we need a model which could provide an early signal to company the probability of financial distress so that remedial efforts can be run immediately. This study aims to explore CAMEL’s ratio as an early classificator, and also to reexamine the capacity of CAMEL ratio as a predictor of banks distress. Using a logit binary to classified the probability of distress and non-distress, then multiple regression to determines the ability of financial ratios as a predictor of distress issuers which ob-tained the following results: a) An exploration CAMEL ratios as an early classificator resulting high classifi-cation capacity with a range of 78.7%-91.4%, Furthermore, when CAMEL ratio were used as a predictors, still resulted a high of capability to classify samples accurately by 82.4%.

Keyword : CAMEL, distress, financial distress, logit binary, rasio

tress becoming much more expensive (Poghosyan & Cihak , 2011) . Several ways to predict early warning of financial distress is by sought declining a trend of global fi-nancial and using a risk management. In addition, the common approach to measure a healthy of bank is using financial ratios that reflect (CAMEL). Previous studies using financial ratios ranging from as predictors of financial distress (Pasaribu , 2008), predictors of failure (Aryati , 2002) to a predictors of bankruptcy of a business (Endri , 2009) . The use of CAMEL financial ratios as predictors distress and non-distress of a bank have been carried out in Indo-nesia, but the dynamics of the research haven’t con-verging on a consensus yet. The Research of Almilia & Herdinigtyas (2005), Wicaksana ( 2011) have quite different results with Sumantry & Jurnali (2010) , Nurazi & Evans (2005), Prasetyo & Pangestuti (2011) in terms of CAR in-fluence on the prediction of financial difficulties. While Ediningsih (2010) and Almilia & Herdinig-tyas (2005) in contrast to Mulyaningrum (2008) in terms of ROA. Then in terms of BOPO (Almalia & Herdinigtyas, 2005; Wicaksana, 2011) and Nurazi & Evans (2005) in contrast to research of Mulyanin-grum (2008) and Sulityowati (2002). In terms of LDR ratio there are differences between Wicaksana (2011), Sumantry & Jurnali (2010) and Ediningsih (2010) with research by Sulityowati (2002). The re-sults discrepancies between studies provide a re-search gap and underlying this present study. Financial research to date puts early classificator va-riable as independent variables. This study clarifies in advance the capacity power of each early classifi-cator variable in accurately classifying samples, and then put all the classificator variables as distress probability predictor of research sample. Or in other words, this study tries to present not just a classifica-tion power of samples accurately, also partial signi-ficance as a distress probability predictor of the sample. By understanding the partial significance, we can use these predictors to estimate the distress or non-distress probability of a bank. This study aims to explore the capacity of CAMEL ratios as classifier of early corporate distress and non-distress, then using proxy for liquidity, solvency and profitability as a predictor of the initial classifi-cation results and to re-test the capacity of CAMEL ratios as predictors of distress conditions on public listed banks in Indonesia Stock Exchange 2009-2012 period.

2 LITERATURE REVIEW

2.1 Financial Distress in Bank

Financial distress hereafter referred to condition where a company is in financial difficulties. Finan-cial distress models need to be developed , because actions to anticipate the conditions that lead to worse conditions such as financial failure and bankruptcy is expected to be done early (Almilia & Kristijadi , 2003; Atmini & Wuryan , 2005) which would elimi-nate the stakeholders confidence on the manage-ment. Financial distress signal according Pasaribu (2008) can be seen from the lower sales volume , the decline in the company 's ability to make profits , re-liance on debt, amount of dividends distributed to shareholders is decreasing during several periods in a row.

2.2 Financial Ratios as Determinants of Banking Financial Distress

Financial performance reflects the management's ability to manage the company. In this study, finan-cial performance can represent The CAMEL. Table 1. Ratio Represent CAMEL

Ratios Represent CAR Capital on CAMEL NPL Asset on CAMEL BOPO Management on CAMEL ROA Equity on CAMEL LDR Liquidity on CAMEL

CAR of the bank is set at 8% represent the bank resi-lience to face assets depreciation due to troubled/bad or risky assets. It shows that there is a negative rela-tionship between the CAR with financial distress. The research of Almilia & Herdinigtyas (2005), Pra-setyo (2011), Wicaksana (2011) founds a negative relationship between the CAR and bank distress condition, while Sumantri & Jurnali (2010) and Nu-razi & Evans (2005) shows the opposite condition.

NPL is determined by Bank Indonesia regulation worth less than 5% represent the management's ability to manage credit (Almilia and Herdinigtyas, 2005). The higher the NPL, provides information of bank failures in managing the business (Almilia & Herdinigtyas, 2005; Mulyaningrum, 2008; Suher-man, 2007; and Wicaksana, 2011).

BOPO was assessed by comparing the operating costs to operating income, the higher value of BOPO shows that the bank's operations are inefficient, and prove that management not performed well. High BOPO value describe that banks earning or insuffi-cient to cover the required bank expenses and ulti-mately brought the bank to the financial difficulty,

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so it can be said that BOPO has a positive relation to bank distress. Previous research (Almalia & Hedi-ningtyas, 2005) showed that BOPO has a significant positive effect on distress prediction, together with along with Wicaksana (2011) and Nurazi & Evans (2005), Mulyaningrum (2008) found that BOPO has a positive effect but not significant. On otherside, Sumantry & Jurnali (2010) found that BOPO has a insignificant negative effect and while Sulityowati (2002) conclude that BOPO ratio has a significant negative effect on the prediction of bank financial distress.

ROA used to measure the ability of company to gen-erate revenue from asset management. The greater value of ROA ratio, the greater income achieved the better bank performance in managing company as-sets. Thus the higher of bank's assets allocated to loan and the lower the capital ratio, the possibility of banks financial distress will increase, while the higher ROA, the possibility of the bank distress will be smaller, resulting that ROA has a negative effect on bank distress.

Previous research by Ediningsih (2010) resulting that ROA has a significant negative effect on finan-cial predictions disstress. In contrary, Sumantry & Jurnali (2010) and Nuzari & Evans (2005) found that ROA has a positive effect, while Mulyaningrum (2008), Almalia & Herdaningtyas (2005) and Wi-caksana (2011) found that ROA has insignificant negative effect.

LDR is assessed by comparing the total credit/loan provided to a third party with the funds received from third parties. LDR ratio indicates the ability of banks to provide the funds received from third par-ties when due. According to Santoso (2006) in As-moro & Widyarti (2010), LDR ratio proportional to the probability of bank financial distress as research Aryati & Balafi (2007). This happens because the funds allocated for loan/credit has a high risk, the greater the credit allotted, the greater the non-performing or uncollected loan, and the greater the possibility of financial distress. Theoretical Frame-work can be seen in Figure 1. Figure 1 Theoretical Framework

2.3.Hypothesis

Based on brief description on the introduction, for-mulation and problem definition, research objec-tives, the basic theory and previous research, as well as the framework in this study, the hypothesis that will be used are as follows: H1: Financial ratios are significantly influential to

distinguish companies that are considered dis-tress and non-distress classification based on CAR, LDR, ROA, NPL, and BOPO.

H2: Financial distress prediction model generated through the use of financial ratios in the binary logit analysis has high classification accuracy.

H3: CAMEL ratio as predictor has significant effect on the prediction of the distress probability on listed banks.

H4: Financial distress prediction model generated through the use of CAMEL ratio as a predictor in binary logit analysis has high classification accuracy.

3 RESEARCH METHODOLOGY

3.1 Population and Sample

The population used in this study is 27 bank listed in Indonesia Stock Exchange (IDX) 2009-2012. The sample selection is done based on purposive sam-pling method, the sample selection of banking com-panies during the study period with considerations and criteria that are tailored to the research objec-tives are:

1. Public banks listed on Indonesia Stock Ex-change period 2009-2012;

2. Finance report document complete which will be used to divided sample into :

a. Non-Distress: Companies that do not suffer losses for two consecu-tive years

b. Distress: company Debt is larger than the capital and experiencing loss for at least two consecutive years

Finally, 27 banks were selected as research samples.

3.2 Research Variable

The dependent variable in this study is distress and non-distress clasificator referring to Bank Indonesia concerning CAMEL ratio performance. The sample will be classified using dummy technique as follow: “0” = non-distress and “1” = distress (look table 2)

The independent variables in this study are several financial ratios derived from report such as balance sheet, profit/loss, cash flow, in the form of liquidity

ratios, solvency, profitability, leverage and cash flow that were adopted from Almilia (2006) and Brahma-na (2007) studies (look table 3). Table 2. Distress and Non-Distress Classification Ratio Distress Non-Distress

CAR < 0.08 ≥ 0.08

LDR > 1.10 ≤ 1.10

ROA < 0.015 ≥ 0.015

NPL ≤ 0.05 > 0.05

BOPO > 0.9325 ≤ 0.9325

* Source: Bank Indonesia (2009) Table 3. The Dependent Variable as Distress and Non-Distress Indicators. ______________________________________________ Proxy Indicator ______________________________________________ Liquidity QR = Cash assets/Total Deposit

IPR = Securities/Total Deposit BR = Total Loans/Total Deposit ALR = Total Loans/Total Assets CR = Liquid Asset/Short Term Borrowing DRR = Equity Capital/Total Deposit ______________________________________________

Solvency RAR = Equity Capital/(Total Assets-Cash As-sets-Securities) CRa = Equity Capital + Reserve for loan losses/Total Loans Cri = Equity Capital/Secondary Risk Ratio ______________________________________________

Rentability GPM = (O. Income-O. Expense)/O. Income NPM = Net Income/O. Income NITA = Net Income/Total Assets IML = (I. Income-I. Expense)/Total Loans CoF = I. Expense/Total Dana CoE = Total Expense/Total Earning Assets _____________________________________________

* Source: Kashmir (2008) All data gathering from the financial report directly from the sample website

3.2.1 Data Analysis

3.2.2 Techniques This study used logistic regression model as follow: Ln [odds (S│X1,X2,Xk)] = Ln p/(1-p)=Yn= α+β_1 QR+β2IPR+β3BR+β4ALR+β5CR+β6DRR+β7RA+ β8CRa+β9CRi+β10GPM+β11NPM+β12NITA+β13IML+β14CoF+β15CoE Yn=CAR,LDR,ROA,NPL,BOPO Equation on the post-test: Ln p/(1-p)=Y= α+β_1 CAR+β_2 LDR+β_3 ROA+β_4 NPL+β_5 BOPO With the following provisions: Y = 0 = Non Distress; If the sample has a CAMEL financial ratios ≥ BI minimum requirements and on-ly breaking 1 CAMEL ratio

Y = 1 = Distress; If the sample had 2 or more CA-MEL financial ratios < BI minimum requirements.

4 RESULTS

4.1 Hypothesis Testing

This hypothesis testing conducted to test how accu-rate CAMEL ratio able to predict the condition of distress and non-distress of a company. In the post test, hypothesis testing is done to determine the sig-nificance of partial CAMEL ratio as a predictor of bank distress probability. The steps performed in this test as follows: feasibility of regression models in analyzing the early classification to each group proxy.

4.2 Results

Referring to Table 6 containing Implications and Significance CAR approach, there are three ratios having significant effect, namely: CRi GPM and NPM. From the three ratios, two of them have nega-tive implications for bank distress probability, which are the capital risk and net income. Moreover, ga-thered capacities of CAMEL ratio as the initial sam-ple classifiers in the distress and non-distress catego-ry are in the range of 78.7% - 91.4%.

From Table 7, the results obtained if the manage-ment does not improve, it actually increases the probability of a bank experiencing distress. Other findings, CAR and ROA variable have a negative implication on the probability of distress, this means that if there is a positive change in the level of capi-tal adequacy, and then it is actually lowering the probability for bank in experiencing distress proba-bility. On the other hand, LDR, NPL, and BOPO ratio have a significant positive impact, in other words changes in these three ratios will increase the probability of bank experiencing distress. ROA variable have negative implications even though it have an insigni-ficant effect on the distress probability. In Overall CAMEL ratio has the capacity to explain fluctua-tions distress probability at 39.5% while the remain-ing 60.5% are other variables that are not used on this research. Furthermore, the results of the initial classification of the combined ratio of CAMEL have a large enough capacity in determining sample in distress and non-distress category correctly, which is about 82.4%.

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Table 6. Implications and Significance ____________________________________________________________________________________________

Variable CAR Model LDR Model ROA Model NPL Model BOPO Model β Sig.t β Sig.t β Sig.t β Sig.t β Sig.t

____________________________________________________________________________________________ QR .672 .800 1.022 .706 2.204 .460 -3.978 .024 4.304 .082 BR -2.866 .197 -1.676 .264 -5.347 .027 .721 .732 -2.630 .094 IPR 1.967 .284 -1.201 .373 -.026 .988 5.300 .006 2.325 .105 ALR .001 .930 .001 .901 -.002 .964 .001 .927 -.002 .942 CR -.031 .560 .014 .851 .010 .861 .345 .161 .023 .669 DRR 4.494 .477 -2.577 .255 38.102 .000 6.849 .241 20.119 .003 RAR .004 .902 .005 .878 -1.335 .679 .005 .892 -16.06 .017 CRa -1.980 .542 1.163 .395 -7.198 .043 -4.493 .113 -5.952 .045 Cri -9.462 .011 .266 .922 -6.889 .310 -4.950 .172 2.726 .369 NITA 2.272 .183 -1.631 .033 .294 .790 .201 .852 .567 .474 GPM 2.935 .044 1.859 .154 4.375 .011 2.680 .077 1.756 .103 NPM -5.485 .001 -.961 .500 2.495 .183 -1.697 .342 2.015 .143 IML 6.161 .162 .191 .916 -2.146 .132 10.696 .027 .495 .722 CoF -.112 .262 .057 .635 -.017 .869 -.017 .914 .768 .097 CoE 6.881 .501 30.495 .085 1.169 .924 14.108 .198 -6.579 .247 Constant 5.355 .011 2.879 .063 2.822 .205 .720 .717 1.827 .192 Nagelkerke R2

(%) 12.43 6.76 27.59 14.97

13.18

Classification (%) 91.4 89.4 89.6 90.5 78.7 * Source: Processed data Table 7 Post-Test Results of CAMEL Ratio Variable B Sig.

CAR -3.147 .108

LDR 3.598 .000

ROA -.598 .877

NPL 10.705 .000

BOPO 10.202 .000

Constant -13.365 .000

N.R2 .395 Classification Sample % Correct

NON DISTRESS 305 94.4

DISTRESS 51 46.8

Aggregate (%) 82.4

* Source: Results Data

5. DISCUSSION

The calculation results shows that CAR have a nega-tive implication but insignificant to the probability of bank distress. These results support the studies conducted Harjanti & Sampurno (2011), Nugroho & Sampurno (2011), Almilia & Herdinigtyas (2005), Martharini & Mahfud (2012), Asmoro & Widyarti (2010), Pratiwi (2011), Nugroho (2012) and Susanto & Njit (2012) which states that a high CAR means of capital assets held to underwrite the risk is also higher so the lower probability for bank distress be-cause of capital owned by the banks is bigger. This study does not support the results of study conducted

by Wongsosudono & Chrissa (2013), Prasetyo & Pangestuti (2011) and Sumantry & Jurnali (2010), which stated that CAR had a positive effect on the bank distress probability. The empirical results indicate that LDR has a posi-tive impact and significant effect, or in other words, poor liquidity management will increase the proba-bility of bank distress. This study support Pratiwi (2011), Wongsosudono & Chrissa (2013) studies, as well as Nugroho & Sampurno (2011) which also states that LDR has a positive impact on the proba-bility of bank distress. In contrast, this study does not support Asmoro & Widyarti (2010), Nugroho (2012), Prasetyo & Pangestuti (2011), Harjanti & Sampurno (2011), Sumantry & Jurnali (2010), Mar-tharini & Mahfud (2012), as well as Susanto & Njit (2012) studies which states that LDR has negative implications on the probability of bank distress. Rentability aspects indicate a bank's ability to gener-ate earnings represent by ratio is ROA and BOPO. The results show that ROA has a negative implica-tion even though with insignificant effect on the bank distress probability. These results support con-ducted Asmoro & Widyarti (2010), Pratiwi (2011), Wongsosudono & Chrissa (2013), Harjanti & Sam-purno (2011), Nugroho & Sampurno (2011), Almilia & Herdinigtyas (2005), Martharini and Mahfud (2012), and Susanto and Njit (2012) studies which states if a high CAR means of capital assets held to underwrite the risk is also higher and the probability of distress is lower because of the capital owned by banks is bigger. This study does not support the re-

sults of Prasetyo & Pangestuti (2011) and Sumantry & Jurnali (2010), which were both agree that CAR has a positive effect on the bank distress probability. On the other hand this study does not support pre-vious research conducted by Nugroho (2012), Pra-setyo & Pangestuti (2011), Harjanti & Sampurno (2011), Nugroho & Sampurno (2011), Sumantry & Jurnali (2010), Martharini & Mahfud (2012), Susan-to & Njit (2012) study which states that ROA have positive implication on the bank distress probability. The empirical results indicate that BOPO has a posi-tive impact and significant effect, or in other words poor operational management will ultimately in-crease the bank probability in a state of distress. The results of this study are supporting the study of Nu-groho & Sampurno (2011) and Nugroho (2012). In contrary, this study does not support the studies con-ducted by Asmoro & Widyarti (2010), Pratiwi (2011), Prasetyo & Pangestuti (2011), Harjanti & Sampurno (2011), Nugroho & Sampurno (2011), Almilia & Herdinigtyas (2005), Martharini and Mahfud (2012) which states that BOPO has a posi-tive impact on the probability of bank distress. The result of this study does not support the conducted Sumantry and Jurnali (2010) and Susanto and NJIT (2012) studies which states that the ROA actually negatively affect the probability of bank distress. The calculation results shows that NPL have a posi-tive and significant implication on the bank distress probability or in other words, if the credit/loan man-agement is not performing as it should, then just a matter of time a bank will experience distress. Thus, this study supports the study by Pratiwi (2011), Nu-groho (2012), Prasatio and Pangestuti (2011), Har-janti and Sampurno (2011), Nugroho and Sampurno (2011), Almilia and Herdinigtyas (2005), as well as Martharini and Mahfud (2012). Instead of this study do not support the studies conducted by Asmoro and Widyarti (2010), Sumantri and Jurnali (2010) and Susanto and Njit (2012) which states that the NPL has negative implications on the bank distress prob-ability. 6 CONCLUSIONS AND RECOMMENDATIONS

6.1. Conclusions

Based on the calculation results are as follows: a) exploration of CAMEL ratios as early samples clas-sificator have classification power with a range of 78.7% -91.4%. While the proxy capacity of liquidi-ty, solvency, and profitability as a probability pre-dictor of bank distress are range between 6.76% -27.59%. A model which is formed from the CAMEL ratio is sufficiently good, for example by correctly classifying samples by 82.4%.

6.2.Research implications

The result of this study indicates that the bank's fi-nancial ratios can be used as a financial distress pre-diction tool prior to bankruptcy. The study then at-tempted to see the CAMEL capability in classifying company’s distress and non-distress condition and in parallel re-examined CAMEL ratio as a distress conditions probability predictor.

6.3.Limitations

There are several limitations of this study, it is rec-ommended for the following research: 1) Longer ob-servation period and a larger number of samples, 2) Ratio – The wider range of financial ratios for ex-ample performance with added value such as Eco-nomic Value Added, Market Value Added and Cash Value Added.

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