4. Financial Distress Prediction
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Modeling Financial Distress:The Case of Indonesian Banking Industry[footnoteRef:1] [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. ]
Rinaldo Sjahril [footnoteRef:2] [2: RinaldoSjahrial (corresponding author; email@example.com) 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 (firstname.lastname@example.org); Andi M. AlfianParewangi (email@example.com) is an editor on Bulletin of Monetary Economics and Banking and a lecturer at Postgraduate Program, University of Muhammadiyah Jakarta; Hermiyetti (firstname.lastname@example.org) is lecturer at University of Muhammadiyah Jakarta and a lecturer at Bakrie University. ]
Andry PrihartaAndi M. Alfian ParewangiHermiyetti
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 .
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 - 2008TimelineCountryEvent
Aug. 2007 - Aug 2008
NetherlandsBayerische LandesBank is one ofthree LandesBankento receive capital injections, creditlines, and assetbackedsecurities lossguranatees.The government recapitalizes 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 recapitalization for 10.5 billion EUR.The Dutch government provides a cack-up facility to back up the risks of INGs 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 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"