1、PDF外文：http:/ 2835 字 ,1988 单词， 1.1 万英文字符 出处： Emel A B, Oral M, Reisman A, et al. A credit scoring approach for the commercial banking sectorJ. Socio-Economic Planning Sciences, 2003, 37(2):103-123. 英文原文： A credit scoring approach for the commercial banking sector Ahmet Burak Emel, Arnold
2、Reisman and Reha Yolalan Yapi Kredi Bank, Levent, 80620, Istanbul, Turkey. The Graduate School of Management, Sabanci University, Istanbul, Turkey &
3、nbsp; Available online 15 March 2007 The economic and, therefore, the social well-being of developing countries with fairly privatized economies is highly dependent on the behavior of a country's commercial banking sector. Banks provide credit to sustain anufacturing, agricultural,
4、 commercial and service enterprises. These, in turn, provide jobs thus enhancing purchasing power, consumption, and savings. Bank failures, especially in such settings, send shockwaves affecting the social fabric of the country as a whole and, as experienced recently, (Latin America and Asia) have t
5、he potential of a quick global impact. Thus, it is imperative that lending/credit decisions are made as prudently as possible while keeping the decision making process both efficient and effective. Commercial banks provide financial products and services to clients while managing a set of mult
6、i-dimensional risks associated with liquidity, capital adequacy, credit, interest and foreign exchange rates, operating and sovereign risks, etc. In this sense, banks may be considered to be “risk machines”. They take risks, and transform or embed such risks to provide products and services. Banks a
7、re also “profit-seeking” organizations basically formed to make money for shareholders. In their typical decision-making processes (i.e. pricing, lending, funding, hedging, etc.), they try to optimize their “risk-return” trade-off. Management of risk and of profitability are very closely related. Ri
8、sk taking is the basic requirement for future profitability. In other words, today's risks may turn up as tomorrow's realities. Therefore, banks may not live without managing these risks. Among the different banking risks, credit risk has a potential “social” impact because of the numb
9、er and diversity of stakeholders affected. Business failures affect shareholders, managers, lenders (banks), suppliers, clients, the financial community, government, competitors, and regulatory bodies, among others. In the age of telecommunications, the ripple effect of a bank failure is virtually i
10、nstantaneous and such ripples hold the potential of global impact. In order to effectively manage the credit risk exposure of a modern bank, there is thus a strong need for sophisticated decision support systems backed by analytical tools to measure, monitor, manage, and control, financial and opera
11、tional risks and inefficiencies. Conscious risk-taking decisions call for quantitative risk-management systems, which, in turn, provide the bank early warnings for predicting potential business failures. Thus, an effective risk-monitoring unit supports managers judgments and, hence, the profit
12、ability of the bank. A potential client's credit risk level is often evaluated by the bank's internal credit scoring models. Such models offer banks a means for evaluating the risk of their credit portfolio, in a timely manner, by centralizing global-exposures data and by analyzing marginal
13、as well as absolute contributions to risk components. These models can offer useful insight and do provide an important body of information to help a bank formulate its risk management strategies. Models that are conceptually sound, empirically validated, backed by good historical data, understood a
14、nd implemented by management, augment the business success of credit quality. Over the past decade, several financial crises observed in some emerging markets enjoying a recent financial liberalization experience, showed that debt financing built on capital inflow may result in large and sudde
15、n capital outflows, thereby causing a domestic “credit crunch”. Experience with these recent crises forced banking authorities, i.e. the Bank of International Settlements (BIS), the World Bank, the IMF, as well as the Federal Reserve. to draw a number of lessons. Hence, they all encourage commercial
16、 banks to develop internal models to better quantify financial risks. The Basel Committee on Banking Supervision, English and Nelson, the Federal Reserve System Task Force on Internal Credit Risk Models.Lopez and Saidenberg and Treacy and Carey represent some recent documents addressing these issues
17、. Credit scoring has both financial and non-financial aspects. The scope of the current paper, however, is limited to the evaluation of a bank client's financial performance. Studies attempting to measure firm performance on the basis of qualitative data are exemplified by Bertels et al. &
18、nbsp;Formal or mathematical modeling of finance theory began in the late 1950s. The work of Markowitz represents a major milestone. The practice reached its “take-off” stage as a sub-discipline of Finance during the early 1960s. Some of the early efforts were directed at evaluating a firm for purpos
19、es of mergers and acquisitions； some dealt with using investment portfolios to manage risk； others dealt with improvement/optimization of a firm's financing mix. They were all directed at enhancing extant finance theory toward the goal of guiding decision-makers. One of the fields in which
20、 formal or mathematical modeling of finance theory has found widespread application is risk measurement. A firm's financial information plays a vital role in decision making of risk-taking activities by different parties in the economy. An extensive literature dedicated to the prediction of busi
21、ness failure as well as credit scoring concepts has emerged in recent years. Financial ratios are the simplest tools for evaluating and predicting the financial performance of firms. They have been used in the literature for many decades. The benefits and limitations of financial ratio analysi
22、s are addressed in a widely used text on managerial finance. Financial statements report both on a firm's position at a point in time and on its operations over some past period. However, there are still some limitations in using ratio analysis: (i) many large firms operate in a number of differ
23、ent industries. In such cases it is difficult to develop a meaningful set of industry averages for comparative purposes； (ii) inflation badly distorts a firm's balance sheet. Moreover, recorded values are often substantially different from their “true” values； (iii) seasonal factors can distort
24、a ratio analysis； (iv) firms can employ “window dressing techniques” to make their financial statements look stronger； (v) it is difficult to generalize about whether a particular ratio is “good” or “bad”； and (vi) a firm may have some ratios looking “good” and others looking “bad” making it difficu
25、lt to tell whether the firm is, on balance, strong or weak. Across different countries, sectors and/or periods of time, financial ratios that have been found useful in predicting failure differ from study to study. To deal with the above shortcomings of unidimensional financial ratio ana
26、lysis, a variety of methods have appeared in the literature for modeling the business failure prediction process. An excellent comprehensive literature survey can be found in Dimitras et al. In the late 1960s, discriminant analysis (DA) was introduced to create a composite empirical indicator
27、of financial ratios. Using financial ratios, Beaver developed an indicator that best differentiated between failed and non-failed firms using univariate analysis techniques. Altman established that ratios found not to be very significant by univariate models, could prove somewhat useful in a discrim
28、inant function which considers the relationships among variables. Hence, he considered several variables simultaneously using multiple discriminant analysis (MDA). He argued that MDA had the advantage of considering an entire profile of interrelated characteristics common to the relevant firms. That
29、 study also aimed to predict future failure on the basis of financial ratios. He concluded that his bankruptcy prediction model was an accurate forecaster of failure for up to 2 years prior to bankruptcy and that the model's accuracy diminishes substantially as the lead-time increases. In spite of widespread use of MDA, Altman, confesses to the following weakness of discriminant analysis: Up to this point the sample firms were chosen either by their bankruptcy status (Group 1) or by their similarity to Group 1 in all aspects except their economic well
