Modeling Financial Distress

Modeling Financial Distress:

The Case of Indonesian Banking Industry[1]

Rinaldo Sjahril [2]

Andry Priharta

Andi 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 willprovide them clearer indicator to choose rational decision within the market.

Keywords: Financial Market, binary logit, banking, financial distress.

JEL Classification: G32, C33 .

1

INTRODUCTION

As an important sector in the economy role of a country, banking has a unique function of business, as well as stabilizingthe State financial atmosphere.It also has some influence in company financial condition (as creditors)as disclosed in Mexico research that banking bankruptcywill cause a decrease in the company value asset at 4.1%, net sales decline amounting to 3.7% for companies that have a relationship asbankingcreditor. This illustration explainsthat 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 experiencetwin 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 of2007 - 2008

Timeline / Country / Event
Aug. 2007 - Aug 2008
Sept. 2008
Oct. 2008
Nov. 2008
Dec. 2008
Jan. 2009 / Germany
France
U.S.
Netherlands
France
Sweden
Germany
U.S.
Italy
Germany
Germany
France
Netherlands / Bayerische LandesBank is one ofthree LandesBankento receive capital injections, creditlines, and assetbackedsecurities lossguranatees.
The government recapitalizes Dexia
Emergency 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 available
The 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.
BayerischeLandesBank 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 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 burdenthrough 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 developingdue 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 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 valuationfor the bank, butalso 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 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 Distress

Almilia (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 NurHasanah (2010). Endri (2009:37) assumed thatfinancial 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 situation 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 distress may lead a firm to default on a contract, and it may involve; financial restructuring between a firm, its creditors and itsa 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 sufficient 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 restructuring, creditors and investors of capital (equity investors) of the company.

Based on the statement of Zaki, et al. (2011) in the journal entitled Assessing probabilities 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 cannotpay the repayment schedule or when the cash flow projections indicate that the company will soon be unable to pay its obligations. There are several definitions of financial distress according the type that is economic failure, business failure, technical insolvency, insolvency in bankruptcy, and legal bankruptcy (AmaliaFachrudinKhaira, 2008:2).

2. Financial Distress Agent

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:

1. Neoclassical Model

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

b. Financial Model

The Mixture of asset is true, otherwise financial structure with liquidity constraints (liquidity constraints). This means that even if the company can survive in the long term but without it should also go bankrupt in the short term. Imperfect relationship between capital markets and inherited capital structure as a key trigger of this case. There is no explicit definition whether the bankruptcy is good or bad forrestructurization. This model estimates the distress with financial or performance as indicators, for isntance turnover / total assets, revenues / 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, EPSratio 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 unsolved problems in corporate governance.

There are several indicators or sources of information regarding with the likelihood of financial 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 quality management and so forth.

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

d)External variables such as return securities and bond valuation.

Bankruptcy is the worst condionof the financial distress. In Darsono and Ashari (2005) in DaulatSihombing (2008), as outlines thatthe agency of bankruptcy can be divided into two: internal 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 receivable –payable, owned.Second, extremely debts that lead to huge interest expense so far will create a minimum earningsthen could even cause harm. Receivables that are too big too detrimentalbecause 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, ArnabBhattacharjee and Jie Han (2010) reveal the causes of bankruptcy are divided into two factors meansmacroeconomic 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 Crisis

On 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 multivariatelogit models. The determining factors include macroeconomic, financial, and institutional.