Modeling Financial Distress:The Case of Indonesian Banking Industry1

Rinaldo Sjahril 2

Andry PrihartaAndi M. Alfian Parewangi

Hermiyetti

Abstract

The existing financial distress models vary in their prediction accuracy. Some well known models are Altman, CAMEL, NPL, and Take Over Models, which involve around 6 financial ratios. Possible sources are different uses of composition ratio, different Industry specification, the level of data aggregation, and possibly due to different timescales. On the account of these conditions, this study aims to obtain a more precise prediction on financial distress focusing on CAMEL ratio. This research applies panel estimation model on assessing the financial distress in Indonesia banking, covering monthly periods of 2002 to 2013, the sample consist of 21 banks. The analysis technique used is a binary logit regression. The result shows that ROA and BOPO have some significant effect, while CAR, NPL and LDR have no significant effect on the financial distress. We expect this research will contribute solid foundation for authority monetary in guiding the financial industry. As for the practitioners, we expect this research will provide them clearer indicator to choose rational decision within the market.

Keywords: Financial Market, binary logit, banking, financial distress. JEL Classification: G32, C33 .

1 This paper is prepared for International Economic Modeling Conference, July 16-18, 2014, Bali – Indonesia. The author thank to University of Muhammadiyah for the funding. We also thank to Central Bank of Indonesia and EcoMod for hosting the conference. 2RinaldoSjahrial (corresponding author; aldo2008_umj@yahoo.co.id) is a lecturer in Economic Department University of Muhammadiyah Jakarta; Andry Priharta is Dean and a lecturer in Faculty of Economics, University of Muhammadiyah Jakarta (andrypriharta@yahoo.co.id); Andi M. AlfianParewangi (alfian.parewangi@gmail.com) is an editor on Bulletin of Monetary Economics and Banking and a lecturer at Postgraduate Program, University of Muhammadiyah Jakarta; Hermiyetti (hermi_yetti@yahoo.com) is lecturer at University of Muhammadiyah Jakarta and a lecturer at Bakrie University.

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INTRODUCTION

As an important sector in the economy role of a country, banking has a unique function of business, as well as stabilizing the State financial atmosphere. It also has some influence in company financial condition (as creditors) as disclosed in Mexico research that banking bankruptcy will cause a decrease in the company value asset at 4.1%, net sales decline amounting to 3.7% for companies that have a relationship as banking creditor. This illustration explains that banking play an important role in the financial stability of a country (Verdugo, 2013).

In the economic crisis that began with the liquidation of 16 banks in November 1997, has led the Indonesian nation fall to in poverty rates increased dramatically, reaching 49.5 million people. This monetary crisis has changed the country economic activity. Ranging from 1997 to 2001 many banks were dismissed and the operations being supervised by BPPN/National Body for Banking Recovery (Arthesa and Handiman, 2006) in (Kamal, 2012).The growing crisis became severe as found any fundamental flaws in the system of the Indonesian economy, as reflected in the inefficiency of the management of the economy and the business sector as well as the vulnerability of the financial and banking sector in Indonesia. This monetary crisis has turned into an economic crisis, the decline of economic activity due to the increasing number of companies are closed, banks were liquidated and the increasing number of unemployed workers, which shows how big the economic impact will be, caused in the event of failure of the banking business.

Moreover the impact on a country as mentioned by Batunanggar (2002) in Indonesian Reformulated Management Crisis, that Southeast Asia's financial crisis is one of the most powerful crises in the twentieth century. After relishing economic growth for more than three decades, Indonesia, Thailand, and South Korea together experience twin crisis turmoil - outstanding currency and banking crisis. The impact of this crisis is very bad. Particularly Indonesia unfortunates in the worst and continuous recession. The fiscal costs of crisis resolution in Indonesia exceeds 50% of the annual GDP. The fiscal costs are the second largest over the past quarter-century after the Argentine crisis in the early 1980s. Although the crisis has passed, but Indonesia will bear the impact over the next few years.

Tabel 1. World Bankcrupcy Bank in the Periode of 2007 - 2008

Timeline Country Event

Aug. 2007 - Aug 2008

Sept. 2008

Germany

France

Bayerische LandesBank is one ofthree LandesBankento receive capital injections, creditlines, and assetbackedsecurities lossguranatees.The government recapitalizes

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Oct. 2008

Nov. 2008

Dec. 2008

Jan. 2009

U.S.

Netherlands

France

Sweden

Germany

U.S.

Italy

Germany

Germany

France

Netherlands

DexiaEmergency Economic Stabilization Act, containing a commitment for up to 700 bln. USD to purchase bad assets from banks.The government announces that public funds can be used for bank recapitalization, of which 20 bln. EUR are immediately availableThe government approves 320 bln. EUR to provide loans to banks and other financial firms, including a 40 billion euro recapitalization plan.The government announces that will guarantee up to 1.5 trillion SEK in new debt issues, and 15 billion SEK stabilization fund.The government announces a 400 billion EUR plan to guarantee bank financing, including a 70 billion EUR recapitalization fund.The Treasury subscribes 20 bln. USD preferred shares at Citigroup and ring-fences its troubled assets worth up to 300 billion USD.The government approves a law to inject capital into sound banks.Bayerische Landes Bank receives 7 billion EUR of capital from the Bavarian state.The Finance ministry provides Commerzank with an 8.2 billion EUR loan, and buys 1.8 trillion EUR worth of equity.The government implements a second round of bank

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recapitalization for 10.5 billion EUR.The Dutch government provides a cack-up facility to back up the risks of ING’s securitized mortgage portfolio worth 35.1 billion EUR.

Source: processed by researchers

Table 1 shows that some cases of bankruptcy of the banking industry in several countries during the period 2007-2008 due to the occurrence of financial distress that hit along with the cost to be government burden through the central bank that covering all cost and of course,will affect the economic activities of the concerned country.

From these cases, we realized that the banking sector plays an important role in people's lives. Banking is a company that has direct contact with the public activities. Banking activities are influenced largely by the customers or public trust. When within the body of bank occuring turmoil then the reaction will be emerged by the community.

Research on financial distress in European banking with a commercial bank sample, interval period 2003-2007 found that European banks experiencing financial distress problems (Fiordelisi and Cipollini, 2009). In Africa, some banks in Kenya, Nigeria, Uganda and Zambia were closed or taken over by the central bank due to insolvency and liquidity problems caused by non-performing loans (Brownbridge, 1998).

Analysis of financial distress is one of the predictions that is very important to determine whether a financial institution is fair or unfair, especially in banking as the central vein of the economy of a country. Therefore it needs an early warning system to identify initial symptoms of financial distress. Predictive models can be used as an early warning tool for users of corporate financial ratio information, such as lenders, investors, regulators, auditors, and management, in making relevant decisions with the information of financial distress possibility in companies listed on stock exchange, including the banking sector. Financial distress occurs prior to bankruptcy. Financial distress model need developing due to knowing the condition of company financial distress at early stage that is expected to execute some actions in order to anticipate the bankruptcy.

The banks that experienced financial distress will be depressed if it leads towards bankruptcy by the additional costs. In an effort to reduce costs associated with bankruptcy, regulators and corporate managers to act quickly to prevent bankruptcy or lowering the cost of failure, namely by developing early warning system (EWS) to predict potential problems that occur in the company. However, statistical techniques most often used to analyze bankruptcy is a parametric analysis, the logit model and MDA (multivariate discriminant analysis), whereas the new non-parametric model often used these days as a model of trait recognition and artificial neural network (ANN).

The emergence of various bankruptcy prediction models is anticipated and early

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warning systems against financial distress because the model can be used as a means to identify even improve the condition prior to the crisis or bankruptcy.

In real condition of a company financial distress determined by a variety of factors that cannot be disregarded. The process of identification and quantification on those factors are also not always possible. Additionally the definition of financial distress is also not an easy subject to be quantified, thereby modeling financial distress will always depend on a number of assumptions that can be quantified. This research will use qualitative variables assumed that a company's financial situation can be expressed with variables, such as binary technique, where "1" states the condition of distress and "0" represents the company in a non-distress condition (Pasaribu, 2008).

Banking industry has its own financial ratio analysis as expressed by (Fauzi, 2011), such as CAMEL (Capital, Assets, Management, Earnings, Liquidity) and NPL (Non Performing Loan) ratio because of the level of risk associated with bank financing. Other studies related to prediction of bankruptcy of banks in Indonesia conducted by Wilopo (2001). The variables used in this study predict remedy CAMEL model of financial ratios, size of banks as measured by the log, assets and dummy variables (current credit and management).

The results show that overall the level of predictive variables used in this study is in high level. But when viewed from the type of error that occurred appears that the predictive power of liquidated banks 0% because of the sample banks are liquidated, all predicted not liquidated. Special case in Indonesia was CAMEL ratios and other variables used in this study have not been able to predict bank failures. Thus needs further exploration towards other variables outside of the financial ratios in order to obtain more accurate models to predict bank failures.

The ratio is not only used as a valuation for the bank, but also can be used as a tool for predicting bankruptcy of a bank. Almilia (2005) stated that the Capital Adequacy Ratio (CAR) and Operating Expenses to Operating Income (ROA) have a significant influence in predicting bankruptcy of a bank. When both these ratios do not meet the minimum, then the health of a bank will be disrupted. However, Naser and Aryati in Almilia (2005) states that the CAR does not have a significant effect.

Mulyaningrum (2008) states that BOPO is not influence significantly. But the ratio of loan to deposit ratio (LDR) has a significant influence on a bank's bankruptcy prediction. In Almilia study (2005) describes that LDR has no significant effect. In addition to these ratios, net interest margin (NIM), Return on Equity (ROE) and the Non-Performing Loan (NPL) has declared no significant effect for predicting bankruptcy of a bank in both study.

In another study (Kurniasari, 2013), indicates that the CAR, NPL, ROA, and ROE do not significantly affect the probability of financial distress of banks. While the LDR and ROA significantly influence the probability of financial distress Indonesian banks.

The inconsistency of the previous research into the background of research in this paper. In particular, this paper analyzes the ability of CAMEL financial ratios to predict financial distress banking in Indonesia.

The second part of this paper to review the theory, the third section to review the

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methodology and data used. The results and analysis are presented in the fourth section, while the fifth section presents the conclusions.

LITERATURE REVIEW

1. Financial DistressAlmilia (2004) defines financial distress or problematic conditions as a condition in

which the company experienced delisted as a result of net income and book value of negative equity continuously and the company was in a merger. Financial distress is an early symptom of the bankruptcy of a company. Financial distress can also be a stage prior to the bankruptcy or liquidation (Luciana, 2006:1) in Nur Hasanah (2010). Endri (2009:37) assumed that financial distress as a condition of a company that is experiencing negative net income (net profit) for several years. Meanwhile, research conducted by Luciana (2004) defines financial distress as a condition in which the company experienced delisted as a result of net income and book value of negative equity in a row and the company was in a merger.

Ross and Westerfield (2007) in Andre Boy (2008:30) defines "financial distress is a sit-uation where a firm's operating cash flows are not sufficient to satisfy current bond (such a trade credit or interest expense) and the firm is forced to take corrective action. Financial dis-tress may lead a firm to default on a contract, and it may involve; financial restructuring be-tween a firm, its creditors and its a equity investors. Usually the firm is forced to take actions that it would not have taken if it had sufficient cash flow.

Financial distress is a situation in which the company's operating cash flow is not suffi-cient to cover the company's liabilities or current, such as Letter of Credit (L/C) or interest costs, so the company was forced to perform a corrective action. Financial distress can bring a company's default on the contract, which eventually must be done on company financial re-structuring, creditors and investors of capital (equity investors) of the company.

Based on the statement of Zaki, et al. (2011) in the journal entitled Assessing probabili-ties of Financial Distress of Banks in the UAE, financial distress or financial hardship can be defined to be "a period when a borrower (either individual or institutional) is Unable to meet a payment obligation to lenders and other creditors." A company can be said to be in financial distress or trouble condition if the company experienced a negative net income (net profit) for several years (Whitaker, 1999).

Financial distress is begun when the company cannot pay the repayment schedule or when the cash flow projections indicate that the company will soon be unable to pay its obliga-tions. There are several definitions of financial distress according the type that is economic fail-ure, business failure, technical insolvency, insolvency in bankruptcy, and legal bankruptcy (Amalia Fachrudin Khaira, 2008:2).

2. Financial Distress Agent

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Lizal (2002) in Fachrudin (2008:6) classifies the causes of the financial distress and name it as the Basic Model or Trinity Bankruptcy Agents of Financial Distress. There are three reasons that lead the company into bankruptcy, namely :a. Neoclassical Model

In this case the bankruptcy will be if the allocation of resources is not right. This case oc-curs when the bankruptcy restructuring has the wrong mix of assets. Estimating the diffi-culty is done with the data sheet and income statement. For example, profit / assets (to mea-sure profitability) and liabilities / assets.

b. Financial ModelThe Mixture of asset is true, otherwise financial structure with liquidity constraints (liquid-ity constraints). This means that even if the company can survive in the long term but with-out it should also go bankrupt in the short term. Imperfect relationship between capital mar-kets and inherited capital structure as a key trigger of this case. There is no explicit defini-tion whether the bankruptcy is good or bad for restructurization. This model estimates the distress with financial or performance as indicators, for isntance turnover / total assets, rev-enues / turnover, ROA, ROE, profit margin, stock turnover, receivables turnover, cash flow / total equity, debt ratio, cash flow (liabilities-reserves), current ratio, acid test, current liquidity, gearing ratio, turnover per employee, working capital, EPS ratio and so on.

c. Corporate Governance Model Bankruptcy has a mix of assets and proper financial structure but poorly managed. This inefficiency becomes encourage companies out of the market as a consequence of the un-solved problems in corporate governance.

There are several indicators or sources of information regarding with the likelihood of fi-nancial distress (Luciana & Kristijadi, 2003:189):a) Cash flows analysis for the recent and future period b) Corporate strategies analyses that take into account potential competitors, relative cost

structure, expansion plans in the industry, the ability of firms to pass on cost increases qual-ity management and so forth.

c) Financial statements analysis off the company as well as its comparison with other compa-nies. This analysis can be focused on a single financial variable or a combination of finan-cial variables.

d) External variables such as return securities and bond valuation.Bankruptcy is the worst condion of the financial distress. In Darsono and Ashari (2005) in

Daulat Sihombing (2008), as outlines that the agency of bankruptcy can be divided into two: in-ternal factors and external factors. Internal factors are factors that originate from the internal management of the company, while external factors can come from external factors that relate directly to operations or macro economic factors.

Internal factors that can lead to the bankruptcy of the company include : First, inefficient management causes continuous losses that ultimately induce the company not able to pay its obligations. This inefficiency is caused by the wastage in the cost, lack of skills and management expertise; second, an imbalance in the number of owned capital with a numbers of

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receivable –payable, owned. Second, extremely debts that lead to huge interest expense so far will create a minimum earnings then could even cause harm. Receivables that are too big too detrimental because too many idle assets that do not generate revenue; and third, the moral hazard by management. Fraud by company management could lead to bankruptcy. The fraud resulted in losses for the company that eventually bankrupted the company. Cheating can be corrupt management or providing incorrect information to shareholders or investors.

From a different perspective, Arnab Bhattacharjee and Jie Han (2010) reveal the causes of bankruptcy are divided into two factors means macroeconomic factors (exchange rates, interest rates, etc.) And microeconomic factors (profitability, capital structure, cash flow, and the individual characteristics of other companies).

3. Inducement Factors of Banking CrisisOn the account of journal of The Determinants of Banking Crises in Developing and

Developed Countries, Kunt and Detragiace (1998) describe the factors that determine the occurrence of banking crises.

Using data from the years 1980 - 1994 of economic crisis in several countries, then choosing as the samples are countries include in the International Financial Statistics (IFS) of the in IMF. To identify these factors do estimate multivariate logit models. The determining factors include macroeconomic, financial, and institutional.

The first determinant is the macroeconomic factors. Since the early 1980s the systemic problems in the banking sector have occurred largely in most countries. Vulnerable banking crisis occurs in a weak macroeconomic conditions. Lower GDP growth may increase risk in the banking sector. Vulnerability on aggregate output shocks is not always a sign that the banking system is not efficient. Because of the role of banks as financial intermediaries is risk-taking.

Inflation in high level also influences the increasing of banking sector risk. Nominal interest rates are high and fluctuate due to the high inflation makes it difficult to conduct banking maturity transformation. Consequently tightening in monetary policy is used to create stability in banking sector.

However the implementation of stability of inflation policies can increase significantly real interest rate. Based on the journal explaines that high real interest rates tend to increase the possibility of banking crisis. Therefore the application of inflation stabilization policies must consider the impact of the banking system.

The second element is the financial factor. In addition to the inflation stability policy, high real interest rates also caused by other things, such as financial liberalization. In the journal explained that the level of financial liberalization significantly affect the possibility of a banking crisis even though real interest rates can be controlled.

The third part is institutional factor. This factor will focus on internal activities within banking. The existence of deposit insurance schemes tend to increase the likelihood of systemic banking problems. While on the one hand the existence of deposit insurance can reduce panic atmosphere in the banking sector, but on the other hand the existence of deposit insurance may lead to moral hazard. Therefore, reducing moral hazard in the implementation of the deposit

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insurance a significant priority.Here are the indicators of each factor affecting the banking crisis:

a. Macroeconomic factors: growth, changes in TOT (Terms of Trade), depreciation, real interest rates, inflation, budget surplus to GDP.

b. Financial factors: the ratio of bank cash and reserves to bank assets, and credit growth.c. Institutional factors: the deposit insurances.

The journal also defines a crisis constitutes a situation where one of the following conditions is met :a. Non-performing assets reaches at 10% of total assets of the banking system.b. The cost to rescue the banking system hits at 2% of GDP.c. Transferring of ownership of banks massively to the government.d. Widespread bank run occurs or there are emergency measures executed by the government in

the form of freezing deposit, closing bank in long period of time, or the implementing comprehensive deposit insurance.

4. Financial Distress Prediction

The first useful information when a company experiences financial distress management first is to accelerate action to prevent problems before the bankruptcy. Then the second is the management can take merger action/ take over the model. The third, the company is more capable to pay the debt and run itself better. The prediction of company survival are essential for management and company owners to anticipate the possibility of potential bankruptcy; and the fourth is giving an initial sign in bankruptcy existence (Brahmin, 2005:3).

Focusing on banking, fair bank can be viewed from various aspects. Fair bank assessment aims to determine the bank condition, that varies from very fair, fair, fair enough, less fair, and unfair. Fairbank assessment conducted by Bank Indonesia every year. The aim is that Bank In-donesia as a supervisor and instructor can provide guidance on how the bank management is able to run the business, or even stopped the activities. For the banks that are unhealthy, Bank Indonesia may advise them to change management, merger, consolidation, acquisition, or liqui-dation.

The healthy assessment can be generated in various ways, such as financial ratios. One of the financial ratios are proxied by CAMEL ratio. CAMEL is a ratio that describes the relation-ship or a ratio between a certain numbers to the other amounts included in the financial state-ments of a financial institution. In Dictionary of Banking (Indonesian Institute of Bankers 1999), CAMEL is a measure of object bank checks conducted by bank supervisors.

The first aspect of CAMEL is used to assess healthy aspects of is capital (capital). As-sessed is based on the existing minimum of banks capital adequacy. Usually healthy assessment has an aspect ratio of capital by using CAR (capital adequacy ratio). CAR is an indicator of the ability of the bank to cover the decrease in assets as a result of losses caused by a bank's risky assets (Dendawijaya, 2009). This ratio can be formulated as:

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CAR it=Equity it

ATMR itx 100%

Eq. 1

And CAR grouping used is > 12% Very fair; 9% <CAR <12% fair; 8% <CAR <9% Fit/Fair enough; 6% <CAR <8% Less fair; and CAR <6% with Unfair category, (Bank Indonesia 2004).

CAMEL as the second aspect is the asset quality component. This aspect assesses the types of assets owned by the bank. Valuation of the assets must be in accordance with Bank Indonesia regulations. This assessment is comparing Earning Assets to Assets classified, or using comparison Allowance for Earning Assets to Assets.

Non-performing loan is also used as a tool to measure the health level of banking. The standard formulation is :

NPLit=Bad Loanit

Loanitx 100 %

Eq. 2

The categories used are NPL <2% with a Very Fair category; 2% <NPL <3% Fair; 3% <NPL <6% Fairly Fair; 6% <NPL <9% Less Fair; and the final NPL> 9% by the Unfair category, (Bank Indonesia, 2004).

CAMEL third element is the aspect of quality management (management). Quality management can be seen from the quality of the employees that work. The quality also can be seen by education level as well as experience in dealing with cases in the company. In this aspect usually using questionnaires distributed to corporate management.

CAMEL fourth factor is the aspect of profitability (earnings). This aspect measures the bank's ability to increase earnings each period. This aspect also measure the level of business efficiency and profitability of the bank achieved. The healthy bank is a bank that increases profitability continuously. The ratio used in this aspect include ROA (return on assets), ROE (return on equity) and BOPO (ratio of operating expenses to operating income). The higher the bank assets allocated to the loan and the lower the capital ratio, the more increasing in failure possibility for banks. Meanwhile, the higher ROA the smaller the failure (Haryati, 2001). Higher ROE shows a net profit of banks is increasing, which results in increased stock prices of banks (Dendawijaya, 2009). According to Siamat (1993), decreasing in BOPO level will shows higher operational efficiency achieved bank. This means more efficient bank assets to generate profits. 3

Table 2. Evaluation criteria of ROA, ROE, dan BOPO

3 This Ration formulated as BI circulars (Circular Letter of Bank Indonesia Nomor 3/30/DPNP, December 14th 2001).

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RASIO PREDICATE

ROA > 1,5% ROE > 15% BOPO< 94% Very Fair

1,25% < ROA < 1,5%

12,5% < ROE < 15%

94% < BOPO < 95%

Fair

0,5% < ROA < 1,25%

5% < ROE < 12,5%

95% < BOPO < 96%

Fairly Fair

0% < ROA < 0,5% 0% < ROE < 5%96% < BOPO <

97%Less Fair

ROA < 0% ROE < 0% BOPO >97% Unfair

Source: Bank Indonesia 2004

The last CAMEL aspect is the aspect of liquidity. A bank is expressed to be liquid if the bank can pay all debts mainly savings deposits, demand deposits, and deposits at the time billed. Banks said as liquid when all of the credit application is worth financing. Analyse that applied in this ratio is the ratio of net liabilities to assets and call money LDR (loan-to-deposit ratio). The higher level will indicate the lower liquidity of concerned Bank.. This is because of funds amount required to finance the credit getting greater (Dendawijaya, 2009). LDR can be formulated as follows:

LDR it=Loanit

DPK itx100 %

Eq. 3

with the LDR category of <75% as Very Fair; 75% <LDR <85% Fair; 85% <LDR <100% Fit; 100% <LDR <120% Less Fair; and the final LDR> 120% categorized as Unfair, (Bank Indonesia, 2004).

The measurement results based on CAMEL analysis tool is applied to determine the health of banks that fall within two predicates, namely: "Fair" and "Unfair" With a title of such banks, financial distress can be immediately identified and quickly resolved to anticipate the bank's collapse.

On the account of empirical level, the application of financial distress prediction models have been carried out. Ohlson (1980) presents some empirical results of a study predicting failure as evidenced by the company's bankruptcy case. The main conclusions of the study can be summarized clearly. First thing is to identify the four basic factors as important variables that affect the probability of failure (in one year) are: (1) firm size, (2) measurement of the financial structure, (3) performance measurement, (4) liquidity. Conditional logit econometric analysis methodologies chosen to avoid some of the problems associated with Multivariate Discriminant Analysis. The study population is limited to : (1) the period from 1970 to 1976, (2) companies equity must have been trading on the stock exchange market or some over-the-

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counter (OTC) market, (3) to be classified as an industrial company. By 105 companies sample, there is eighteen of samples (17%) had accounting reports revealed that the company has entered bankruptcy zone. The results finds that significant measurement for the purpose of assessing the bankruptcy are: size and financial structure, which is reflected in the size of the leverage (TLTA), or a combination of several performance measures such as NITA and FUTL.

Thomson (1991) states that the CAMEL model of financial ratios has a strong effort to predict bankruptcy of a bank and give a sign as early warning of bank failures. Surifah (1999) in (Puspitasari, 2003) revealed that there is a difference between the average ratio of CAMEL regarding with failed bank and a bank that does not fail, that have greater. an average ratio of CAMEL. Wilopo (2000) stated that on the CAMEL model of financial ratios, stated that the bank size and compliance level to Bank Indonesia can not be used as predictors of failure.bank

Research conducted by Kusumo (2002), with variables: CAR, RORA, COM, ROA and LDR using logit models Regression analysis showed that financial ratios in financial statements significantly influence the bankruptcy that are most related to the ratio of capital , profitability and liquidity.

Meanwhile, according to research conducted Januarti (2002), with CAMEL variables (capital, assets, management, earnings, and liquidity), suggesting that the univariate test results over the CAMEL variable to variable NIM, ROA and overhead can distinguish insolvent banks and unbankrupt bank. As for the bank characteristic variables of the univariate test results shows neither consistently significant variable.

Research conducted by Ahmad, et al (2003). Variables used CAR, RORA (Return on Risk Assets), COM (cost on money), ROA and LDR with logit regression research tool. This study shows that the ratio of CAMEL can be used as predictors of bankruptcy of a bank, a financial ratio that shows the difference between bankrupt banks and insolvent banks are capital and profitability ratio is proxied by CAR and ROA.

The testing in study using logit regression to examine the strength of financial ratios prediction to determination of a company financial distress. Data analysis was performed by assessing the overall model (overall model fit), Nagel Karke value analyze and test the the regression coefficients. In this study formed 12 logit regression equation. In each equation always combine profit margin ratios, liquidity, operating efficiency, profitability, financial leverage, cash position and growth o and the research results shows : Of the twelve of regression equation that formed above suggests that financial ratios can be used to predict financial distress of a company. Financial ratio is the most dominant in determining the financial distress of a company is the profit margin ratio, financial leverage, liquidity, and growth ratios.

Almilia (2005) also reviews that the CAMEL ratios as a tool to predict the condition of conflictive bank. Ratios used in these studies include CAR, ATTM, APB, NPL, PPAP, PPAP compliance ratio, ROA, ROE, NIM, ROA, and LDR. The analysis used in this study is logistic regression analysis that use dependent in dummy or only consists of two values, namely: problematic or not problematic. The result of this study indicates that CAR and ROA are only

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able to predict the condition of the financial distress in Indonesian banking. Research by Prasetyo (2011) that used logistic regression with CAR variable ratio, Fulfillment PPAP, NPL, BOPO, NIM, ROA, ROE, and LDR lead to the conclusion that the CAR, NPL, NIM, LDR, and BOPO significantly effect on bank financial distress prediction listed on the Stock Exchange.

The current studies uses CAMELS by Rahman and Masngut (2014) that used neural net-work with the addition of Shari’ah compliance ratio in order to detect the financial distress of Islamic banks in Malaysia. It was found that all Islamic banks have higher ETA ratios which portray a good performance of capital adequacy and are likely to face financial distress. As for the asset quality did not have possibility to face financial distress. Earning efficiency will be less likely to face financial distress. Liquidity indicates have a large number of loans but they have sufficient liquid assets in order to cover their liabilities. Lastly for Shariah Compliance, Is-lamic banks have complied with all rules and regulations that have been regulated by Bank Ne-gara Malaysia’s Shari’ah Advisory Council.

METHODOLOGY

1. Types, Sources, dan Sample Method The population of this study focuses on all the go public banks listed in Indonesia Stock Exchange through Bank Indonesia website (www.bi.go.id) or Financial Services Authority / OJK (www.ojk.go.id) that has 21 banks consisting of all commercial banks operating in Indonesia. Data period is range from Januari 2002 until December 2013.

The dependent variable in this study is financial distress or conflicting banks that meets at least one of the following criteria: (1) negative operating income, (2) negative net income, (3) negative equity book value, and (4) companies that have merged.

Therefore dependent variable is a binary variable with a value of “0” for non-distress bank and “1” for distress bank.

Independent variables used in this study is CAMEL ratio , which is proxied by: ROA, ROE, CAR, NPL, LDR, and BOPO. Estimated empirical model is as follows:

Y it= ln [ pit /(1−p it )]=β0+ β1CAR it +β2 NPLit+β3 ROEit +β4 ROA it+β5 LDRit+β6 BOPOit+εit

Eq. 4

2. Multinomial Logistic ModelBinary logistic model represents a special case of multinomial logistic regression. The

estimation procedure constitutes a device that analyzes the relationship between the dependent variable or responses to a set of independent variables or explanatory variables. In contrast to ordinary regression, in this procedure the response variable is assumed as categorical variable

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while the explanatory variable does not need to be categorical but can be continuous. In general, the explanatory variables will be divided into two, namely factor is just as categorical variable and covariates as continuous variable.

Regression multinomial logistic is very useful when we want to classify the subjects based on the value of a predictor variable. This model is common in binary logistic regression but its nature is more general due to no longer needed of the restriction for maximum two-categorized dependent variable. The concrete sample is when we want to know about the level of customer satisfaction in a service company, hence it is good to discuss that we categorize the level of satisfaction to 5 (very satisfied, satisfied, regular, dissatisfied, very dissatisfied) rather than draft two categories of satisfied and dissatisfied . Wider intervals satisfaction will certainly provide more information and precision in the analysis of the influence of predictor variables on dependent variable (in this case the level of satisfaction). When the dependent variable is categorized more than two, consequently the logistic regression is no longer possible to be used.

In general, multinomial logistic regression model, can be formulated as follows:

ln [Pij

Pio] = ∑j=1

n

βjXij + εj , j = 1, 2, ......, n. Eq. 5

In the binary logistic regression model, we use ln (p / 1-p), where: Pij, is the conditional probability of category jn, the independent variable. In this case

pij indicates the chances of fibrosis. Pio, is the conditional probability of category j = 0 is used as a comparison (reference

category). Xi, is the dependent variable can be categorical or continuous

Βi, is a parameter that indicates the tendency of the ratio between the categories of explanatory variables X.

εi, is an error factor.To estimate the logit model above, we need the values of Yi and Xi so that the parameters

βi can be obtained. This estimation can be done either by least squares method of as well as the maximum likelihood method as used in the SPSS program.4 Based on these parameters, we will be able to see how the explanatory variables affect the response variable probability.

In this regression model, the assumption used is the tendency ratio of the two categories, and not depend on other response categories. Assuming that a respondent is willing to determine the choice of j category that available on the response variable, then the probability expressed as follows:

4 To avoid the error of heteroscedasticity term case, then we need to transform the above equation by multiplying both sides by, d where is the probability of the sample to a category in the explanatory variable X, which is used to represent the probability of the population.

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Pij=exp( x ' ij β )

∑ exp( x ' ij β )

Eq. 6With this probability, we can calculate the likelihood ratio between categories j = 1 to j = 2, for example, as follows:

P i1

P i2=

exp ( x ' i 1 β )/∑j=1

j

exp( x ' ij β )

exp ( x ' i 2 β )/∑j=1

j

exp( x ' ij β )

=exp( x ' i 1 β )

exp( x ' i 2 β )

Eq. 7If the preferred alternative on the response variable was increased to j *, where j *> j, then the ratio of the tendency for category 1 and two will be:5

P i1

P i2=exp ( x ' i 1 β )/ ∑

j∗¿1

j∗¿ exp(x ' ij∗¿ β )

¿exp(x ' i 2 β )/ ∑j∗¿ 1

j∗¿exp (x ' ij∗¿β )

¿=exp( x ' i1 β )

exp( x ' i2 β )

¿¿¿¿¿

Eq. 8Here we can analyze that the addition of these alternatives, it will not change the trend of the ratio between the two categories. For example, if a new product is launched into the market, it is assumed that the market share of other products will be affected proportionally. This proportional change does not cause a change in the ratio of one product with another.

It is also assumed that the covariate pattern, each response is independent multinomial variables. In fact, based on actual data that can be collected, mostly enable the value variables such as gender, ethnicity, religion and equal income level to be each other observation.

From the likelihood ratio above, we can formulate :

P i1

P i2=

exp ( x ' i 1 β )

exp ( x ' i 2 β )

=exp ( x ' i 1−x ' i2 ) β

Eq . 9 It is seen that when the explanatory variables for category 1 and 2 are equal, then the obtaining of likelihood ratio will be equal to 1 (one)6. It will not provide information on how the explanatory variables can explain why an observation i choose a category rather than the category b (a, b j). The only way for this does not happen is to let variable varies from one category to another category. 5 See ‘Baby’ Judge et.al, The Theory and Practice of Econometric, p.770

6 Likelihood ratio (RK) = exp (0) = 1

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Thus we can formulate the probability of observation i to select a category j as follows:

Pij=exp( x ' ij β j )

∑ exp( x ' ij β j)

Eq. 10Suppose we want the ratio tendency to k category and 1, then

Pik

P i1=

exp ( x ' ik βk )

exp ( x ' i1 β1)

Eq. 11In the case of the same explanatory variables for the choice between the two categories, so that Xi1 = Xik = Xi, then RK it will be :

Pik

P i1=

exp ( x ' ik βk )

exp ( x ' i1 β1 )

=exp x ' i( βk−β1 )

Eq. 12Here we need the assumption that 1 = 0, so that the logit model deficiencies can be resolved.

3. Validation Model

Some of the references used to select the best model is as follows, (Winarno, 2011):a. the largest McFadden R-squared and the smallest AIC (Akaike Info Criterion).

McFadden R-Squared is a measurement that seeks to imitate the size of R2 in multiple regression based on the likelihood estimation technique with a value that is always between 0 and 1.

b. Hypothesis Testing (Partial Test)Hypothesis Testing of this analysis is to determine the effect of independent variables on the dependent variable. Hypothesis testing is done by comparing the output with a probability value of α. If the output is less than α, then H0 is rejected and H1 is accepted, which means that the independent variables significantly influence the dependent veriabel. However if the output is greater than α, then H0 is accepted and H1 is rejected, which means that the independent variable on the dependent variable is not significant.

c. LR Statistic (Simultaneous Test)Are the independent variables simultaneously test to see whether the probability values impact Significant (<0.05) or Not Significant (> 0.05) against the possibility of financial distress.

DATA ANALYSIS AND DISCUSSION

This study uses a go public sample of banking companies are listed on the Stock Exchange, with financial statements observation on banking for the period of 12 years i.e..

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2002 to 2013 where the bank always makes positive profit and positive net worth ratio. With a number of companies as 21 companies for 12 years and monthly data. Hence the number of samples data amount of 3024 (21 x 12 x 12).

Data analysis

Variabel Independent

Coeff.Model 1

Coeff. Model 2

ProbModel 1

ProbModel 2 KET

ROAROECARNPLLDRBOPOC

5370,817--4,174027-0,716525-1,636388

-5417,365-0,5039254,520890-0,1478320,722591-1,706794

0,0004--0,24130,35920,00830,0000

0,0004*-0,16590,14440,2046 0,0091*0,0000

SIG

SIG

R2McF

AICLR-statisticProb(LR stat)

0,9454960,0306301425,385*0,000000

0,9457160,0311851425,716*0,000000

Notes: Dep, DNNI, *) Sig at 1%, **) Sig at 5%, ***) Sig. at 10%Sources: processed by researchers using Eviews 8

On the basis of McFadden R-squared value on both models of 0.9454 and 0.9457 has the meaning of Financial Distress dependent variable can be explained by the independent variables are ROA, ROE, CAR, NPL, LDR and ROA of 94.54% (model 1) or 94.57% (model 2), while the remaining of 5.46% or 5.43% is described by others outside the research object variables.

From table 4.2 above, estimate can be constructed the equation of logit regression result for the two models above as follows:

Model 1:Ln P/(1-P) = -1,636388–5370,817ROA–0,134695LDR+ 0,716525 BOPO+4,174027 NPL + e

Model 2:Ln P/(1-P) = -1,706794 – 5417,365 ROA + 0,503925 CAR – 0,147832 LDR +0,722591 BOPO +4,520890 NPL + e

Accordance to table 4.2 that the siginificant factor is ROA variable with significant level at 0.0004 < 0.05 for both model on financial distress with degree of significance at α = 5%. Judging from the coefficient is negative -5370.817 in model 1 negative at -5417.365 in model 2, it interprets that the higher ROA will affect the lower financial distress possibilities. And

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BOPO variables with significance at 0.0083 <0.05 in model 1 and 0.0091 <0.05 in model 2 to the financial distress with the significance level α = 5%. Evaluating from the positive coefficient is 0.716525 and 0.722591 in model 1 model 2, which meaning the higher the ROA will affect the higher financial distress possibility. While the LDR and NPL have significance value at 0.3592> 0.05 and 0.2413> 0.05 in model 1 and model 2 have significant value at 0.2046> 0.05 and 0.1444> 0.05 against financial distress. It denotes that LDR and the NPL does not have the possibility of affecting significant financial distress.

From the results of logit regression, ROA and ROA variables are significant because it has a significant value of 0.0004 and 0.0083 or 0.0091 that eligible significant with probability value <0.05. While coefficient value effect or a better predictor variables owned by the ROA with the coefficient value -5370.817 compared with BOPO variable with a value of 0.716525 coefficients in model 1, as well as in model 2 better predictor variables owned by ROA with coefficient -5417.365 compared to the BOPO variable coefficient value of 0.722591.

Feasibility Test of Model

Partial Significance Testing (Test Statistic z) The effect of ROA variable on Financial Distress

Sig. value of 0.0004 in model 1 and 2 is smaller than the significance level of 0.05, with a significance level α = 5%. It means that ROA significantly influence financial distress variables.

The Effect of LDR variable on Financial Distress Sig. value of 0.3592 (model 1) and 0.2046 (model 2) is greater than the significance level of 0.05, with a significance level α = 5%. It means that ROA no significant effect on the variables of financial distress.

The Effect of BOPO variable on Financial DistressSig. value of 0.0083 (model 1) and 0.0091 (model 2) is smaller than the significance level of 0.05, with a significance level α = 5%. It means that BOPO significantly influence financial distress variables.

The Effect of NPL variable on Financial Distress Sig value of 0.2413 (model 1) and 0.1444 (model 2) is greater than the significance level of 0.05, with a significance level α = 5%. It indicates that the NPL does not significantly influence financial distress variables.

The effect of CAR variable on Financial DistressSig. value of 0.1659 (model 2) is greater than the significance level of 0.05, with a significance level α = 5%. It infers that the NPL does not significantly influence financial distress variables.

Simultaneous Significance Testing (Null Hypothesis / LR Statistic Test)

LR statistic value of 1425.385 (model 1) and 1425.716 (model 2) with probability (LR statistic)

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0.0000 in table 4.2 above for models 1 and 2 are smaller than 0.05 explains that the dependent variable financial distress can be explained simultaneously by the independent variables of ROA, CAR, NPL, LDR, and BOPO.

Cross consistency model (Model 1 vs. Model 2) Cross Consistency model comprises a mechanism to compare the coefficients of

independent variables in the data processing results model with the basic theory. It can show the correspondence between the model with the theory, whether directly or inversely proportional. Table 4.3 Signs Conformity Inter Variable Vs. Theory

Model 1 Variable Coefficient Model2

Variable Coefficient Summary

ROA -5370,817 ROA -5417,365 ROA FDLDR -0,134695 LDR -0,147832 TIDAK SIGBOPO 0,716525 BOPO 0,722591 BOPO FDNPL 4,174027 NPL 4,520890 TIDAK SIG

CAR 0,503925 TIDAK SIG Sources : data processed by researchers using Eviews 8

On the level of actual value of ROA coefficient after having processed with Eviews is -5370.817 (model 1) and -5417.365 (model 2) showed higher ROA will indicate the lower financial distress possibilities. This presumes that the value of ROA coefficient is equal with the prevailing theory.

Accordance to the level of actual value of BOPO coefficient processed by Eviews is 0.716525 (model 1) and 0.722591 (model 2) shows higher BOPO will indicate higher financial distress possibilities. This means that BOPO coefficient value is equal with the prevailing theory.

CONCLUSIONS AND RECOMMENDATIONS

ConclusionsBased on the discussion of the research results, can be concluded as follows:1. Return on Assets (ROA) variable in the logit models 1 and 2 have negative effect and

partially significant on financial distress where as the dependent variable with negative net income categor. It can be derived that the higher ROA will affect the lower the occurrence possibilities of financial distress in Indonesian the banking sector. This study result support the researcher names Thomson (1991).

2. Operating Expenses Variable to Operating Income (BOPO) in the logit models 1 and 2 are positive and significant effect on the dependent variable category of financial distress with negative net income. Based on that statement it can be concluded that the higher BOPO,

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will affect the higher financial distress possibilities in the banking sector in Indonesia. This study results support the researcher names Almilia (2005).

3. CAR, LDR, and NPL variables have not significant value result, so it does not have the possibility of influencing on financial distress occurance. In contrast to Prasetyo (2011) studies, CAR, NPL, NIM, LDR, and BOPO have significant effect on the prediction of financial distress of banks listed on the Indonesian Stock Exchange (BEI).

4. Simultaneously (LR Test Statistic), variable ROA, CAR, NPL, LDR, and BOPO together influence the dependent variable that allows the occurrence of financial distress condition of banking sector in Indonesia.

5. Selected model i.e. logit model 2 with the R-squared value of 0.9457 that is greater than logit model 1 with R-squared value of 0.9454.

6. Logit regression result of ROA and BOPO variables are declared significant having coefficient effect or a more dominant predictor in predicting the financial distress possibilities that consist in ROA variable with higher coefficient than -5417.365 compare with BOPO, which has coefficient value of 0.722591.

Research Limitations and SuggestionsBased on the research results and conclusions presented above it is recommended :1. Suggestions for implementation :

a. It is recommended to investors that in considering the financial statements analysis with indicators of financial ratios CAMEL models in the banking sector. Furthermore in predicting economy the financial distress also contemplates several factors that affect the macro economic level, for instance foreign exchange rates, interest rates, etc. as a sensitive indicator for trigerring financial distress in the banking sector.

b. Banking company is expected to know early the symptoms of financial distress by improving the banks financial performance.

2. Suggestions for further research :a. In order to increase financial ratios proxies to be used as the independent variable

indicators that can be generalized which predictors are most dominant in predicting financial distress in the banking sector.

b. Adding a sample of qualitative data to predict financial distress in the banking sector.c. To enlarge numbers of non-bank sample listing in BEI such as Regional Banks,

Commercial Banks, Non-Foreign Exchange Bank both conventional and shariah that have not include into the study sample.

d. Finally, to create a model of financial distress more precisely representing all available financial ratios proxies in recognizing the symptoms of financial distress in the banking sector early.

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