外文翻譯---商業(yè)銀行的信用評(píng)分步驟

1、<p><b>　　英文原文：</b></p><p>　　A credit scoring approach for the commercial banking sector</p><p>　　Ahmet Burak Emel, Arnold Reisman and Reha Yolalan </p><p>　　Yapi Kr

2、edi Bank, Levent, 80620, Istanbul, Turkey.</p><p>　　The Graduate School of Management, Sabanci University, Istanbul, Turkey </p><p>　　Available online 15 March 2007</p><p>　　The eco

3、nomic 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 anufa

4、cturing, agricultural, 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

5、 social fabric of the country as a whole and, as experien</p><p>　　Commercial banks provide financial products and services to clients while managing a set of multi-dimensional risks associated with liquidit

6、y, 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 pr

7、ovide products and services.</p><p>　　Banks are also “profit-seeking” organizations basically formed to make money for shareholders. In their typical decision-making processes (i.e. pricing, lending, funding

8、, hedging, etc.), they try to optimize their “risk-return” trade-off. Management of risk and of profitability are very closely related. Risk taking is the basic requirement for future profitability. In other words, today

9、's risks may turn up as tomorrow's realities. Therefore, banks may not live without managing these risks. </p><p>　　Among the different banking risks, credit risk has a potential “social” impact beca

10、use of the number and diversity of stakeholders affected. Business failures affect shareholders, managers, lenders (banks), suppliers, clients, the financial community, government, competitors, and regulatory bodies, amo

11、ng others. In the age of telecommunications, the ripple effect of a bank failure is virtually instantaneous and such ripples hold the potential of global impact. In order to effectively manage the </p><p>　　

12、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

13、managers’ judgments and, hence, the profitability 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 evaluatin

14、g the risk of their credit portfolio, in a timely manner, by central</p><p>　　Over the past decade, several financial crises observed in some emerging markets enjoying a recent financial liberalization exper

15、ience, showed that debt financing built on capital inflow may result in large and sudden capital outflows, thereby causing a domestic “credit crunch”. Experience with these recent crises forced banking authorities, i.e.

16、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 enco</p><p>　　Credit scoring has both financial and non-fina

17、ncial 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 exemp

18、lified by Bertels et al. </p><p>　　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

19、a sub-discipline of Finance during the early 1960s. Some of the early efforts were directed at evaluating a firm for purposes of mergers and acquisitions; some dealt with using investment portfolios to manage risk; other

20、s dealt with improvement/optimization of a firm's financing mix. They were all directed at enhancing extant </p><p>　　One of the fields in which formal or mathematical modeling of finance theory has foun

21、d 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 t

22、he prediction of business 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 fi</p><p>

23、　　The benefits and limitations of financial ratio analysis 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 ove

24、r some past period. However, there are still some limitations in using ratio analysis: (i) many large firms operate in a number of different industries. In such cases it is difficult to develop a meaningful set of indust

25、ry averages for comparative purposes; (ii) inflation badly distort</p><p>　　Across different countries, sectors and/or periods of time, financial ratios that have been found useful in predicting failure diff

26、er from study to study. </p><p>　　To deal with the above shortcomings of unidimensional financial ratio analysis, a variety of methods have appeared in the literature for modeling the business failure predic

27、tion process. An excellent comprehensive literature survey can be found in Dimitras et al.. </p><p>　　In the late 1960s, discriminant analysis (DA) was introduced to create a composite empirical indicator of

28、 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 v

29、ery significant by univariate models, could prove somewhat useful in a discriminant function which considers the relationships among variables. Hence, he considered several va</p><p>　　Up to this point the s

30、ample firms were chosen either by their bankruptcy status (Group 1) or by their similarity to Group 1 in all aspects except their economic well being. But what of the many firms which suffer temporary profitability diffi

31、culties, but in actuality do not become bankrupt.</p><p>　　During the years that followed, many researchers attempted to increase the success of MDA in predicting business failure. Among these are Eisenbeis;

32、 Peel et al.; and Falbo. Such work also involved Turkish firms. Examples are Unal, and Ganamukkala and Karan. </p><p>　　Linear probability and multivariate conditional probability models (Logit and Probit) w

33、ere introduced to the business failure prediction literature in late 1970s. The contribution of these methods was in estimating the probability of a firm's failure. The linear probability model is a special case of o

34、rdinary least-squares regression with a dichotomous dependent variable. </p><p>　　In the 1980s, studies utilizing the recursive partitioning algorithm (RPA) based on a binary classification tree rationale we

35、re applied to this problem by Frydman et al. and Srinivasan and Kim. </p><p>　　In the 1980s and 1990s, the use of several mathematical programming techniques enriched the literature. The basic goals of these

36、 methods were to escape the assumptions and restrictions of previous techniques and to improve classification accuracy. </p><p>　　In the early 1990s, decision support systems (DSS) in conjunction with the pa

37、radigm of multi-criteria decision-making (MCDM), were introduced to financial classification problems. Zopounidis, Mareschal and Brans Zopounidis et al. Diakoulaki et al., Siskos et al. and Zopounidis and Doumpos were a

38、mong the studies that measured firm performance aiming at predicting business failure by making use of DSS and MCDM. The ELECTRE method of Roy and the Rough Sets Method of Dimitras et al. represent studi</p><p

39、>　　In the late 1990s, data envelopment analysis (DEA) was introduced to the analysis of credit scoring as in Troutt et al., Simak, and Cielen and Vanhoof. As opposed to the broadly known MDA approach for business fail

40、ure prediction (which requires extra a priori information, i.e. good/bad classification), DEA requires solely ex-post information, i.e. the observed set of inputs and outputs, to calculate the credit scores. Thus, it ope

41、ned new horizons for credit scoring. </p><p>　　DEA, widely known as a non-parametric approach, is basically a mathematical programming technique developed by Charnes, Cooper and Rhodes (CCR) to evaluate the

42、relative efficiency of “decision making units” (DMUs). DEA converts a multiplicity of input and output measures into a unit-free single performance index formed as a ratio of aggregated output to aggregated input. A prod

43、uctivity maximization rationale is elegantly embedded in its original fractional formulation. The capability of dealing </p><p>　　A number of studies have attempted to use statistical methods (such as discri

44、minant, Logit and Probit analyses) with financial ratios to generate early warning signals for distressed banking institutions… The idea is to develop meaningful “peer group analysis”, that is, to develop specific financ

45、ial characteristics that distinguish between two or more groups, for example, failed and non-failed banks, or problem and non-problem banks, with relatively “good” or “bad” financial conditions. However,</p><p

46、>　　Although DEA was introduced in the early 1980s, its applications are acquiring more widespread recognition in the financial literature as time passes.</p><p><b>　　中文翻譯：</b></p><p

47、>　　商業(yè)銀行的信用評(píng)分步驟</p><p>　　在經(jīng)濟(jì)相當(dāng)被私有化的發(fā)展中國(guó)家，經(jīng)濟(jì)福利和社會(huì)福利與國(guó)家的商業(yè)銀行業(yè)的行為有相當(dāng)高的依賴性。銀行給制造業(yè)、農(nóng)業(yè)、商業(yè)服務(wù)和服務(wù)企業(yè)提供信貸。這些能提供工作、提高購(gòu)買力、影響消費(fèi)和儲(chǔ)蓄。特別是在此背景下，銀行倒閉其沖擊波會(huì)影響到該國(guó)的整個(gè)社會(huì)結(jié)構(gòu)。因此，這是當(dāng)務(wù)之急，貸款/信貸決定都是一樣謹(jǐn)慎，盡量保持決策過程的效率性和有效性。</p><

48、p>　　商業(yè)銀行對(duì)客戶提供金融產(chǎn)品和服務(wù)的同時(shí)，還要管理一套聯(lián)系了流動(dòng)資產(chǎn)、資本充足、信用、利率及匯率方面、操作和主權(quán)風(fēng)險(xiǎn)等多維風(fēng)險(xiǎn)，從這個(gè)意義上講，銀行可能會(huì)被認(rèn)為是“風(fēng)險(xiǎn)機(jī)器”。他們?cè)谔峁┊a(chǎn)品和服務(wù)時(shí)，必須承擔(dān)風(fēng)險(xiǎn)，嵌入或改造這種風(fēng)險(xiǎn)。</p><p>　　銀行也是“追求利潤(rùn)”組織， 其股東基本是以賺錢為主要目的。在典型的決策過程（即價(jià)格，貸款，資金，套期保值等），他們?cè)噲D優(yōu)化其“風(fēng)險(xiǎn)-收益”權(quán)衡。 風(fēng)

49、險(xiǎn)管理和贏利的關(guān)系非常密切。風(fēng)險(xiǎn)追求是未來盈利能力的基本要求，換句話說，今天的風(fēng)險(xiǎn)也許作為明天的現(xiàn)實(shí)出現(xiàn)。所以，商業(yè)銀行部管理好風(fēng)險(xiǎn)就無法生存。</p><p>　　在不同的銀行業(yè)務(wù)風(fēng)險(xiǎn)之中， 由于賭金保管人數(shù)量和變化影響，信用危險(xiǎn)有潛在的“社會(huì)” 沖擊。在電訊日趨成熟的現(xiàn)代社會(huì)，銀行倒閉的波動(dòng)行為幾乎是在瞬間產(chǎn)生全球性沖擊。為了有效管理現(xiàn)代銀行的信用風(fēng)險(xiǎn)，輔助決策支持系統(tǒng)就需要精密的分析工具來衡量，監(jiān)測(cè)和管理，

50、和控制財(cái)務(wù)與業(yè)務(wù)風(fēng)險(xiǎn)和低效率。</p><p>　　意識(shí)到冒險(xiǎn)的決定，呼吁定量風(fēng)險(xiǎn)管理系統(tǒng)提供銀行預(yù)警來預(yù)測(cè)潛在的企業(yè)倒閉。因此，盈利的銀行必須使有效的風(fēng)險(xiǎn)監(jiān)控單位支持經(jīng)理人的判斷。一個(gè)潛在客戶的信用風(fēng)險(xiǎn)水平常常用來評(píng)價(jià)銀行的內(nèi)部信用評(píng)分模型。這些目標(biāo)，以確定申請(qǐng)人是否有能力償還的評(píng)估信用風(fēng)險(xiǎn)貸款，這通常是利用歷史數(shù)據(jù)和統(tǒng)計(jì)方法。這些模型能給銀行提供一種手段，以及時(shí)評(píng)估它們的風(fēng)險(xiǎn)信用組合，集中了全球風(fēng)險(xiǎn)數(shù)據(jù)并對(duì)此

51、進(jìn)行了邊際分析。這些模型還可以提供有用的見解，并提供了一個(gè)重要的信息，以幫助銀行制定風(fēng)險(xiǎn)管理戰(zhàn)略。實(shí)驗(yàn)驗(yàn)證，數(shù)學(xué)模型是在概念上健全，輔以良好的歷史數(shù)據(jù)，并且對(duì)此執(zhí)行管理和理解，以充實(shí)業(yè)務(wù)成功的授信品質(zhì)。</p><p>　　過去十年，對(duì)幾個(gè)金融危機(jī)的觀測(cè)說明，在一些新興市場(chǎng)金融自由化的經(jīng)驗(yàn)，表明債務(wù)融資興建的資本流入可能導(dǎo)致大資金突然外流，從而造成國(guó)內(nèi)的“信貸緊縮”。縱觀這些金融危機(jī)的起因表明，信貸擴(kuò)張的資金主要

52、來自資本流入導(dǎo)致投資過高，使得銀行和公司部門易受沖擊。最近這些危機(jī)迫使銀行業(yè)監(jiān)管當(dāng)局，即國(guó)際清算銀行、世界銀行、國(guó)際貨幣基金組織以及美國(guó)聯(lián)邦儲(chǔ)備委員會(huì)，吸取一些教訓(xùn)。因此，他們鼓勵(lì)各商業(yè)銀行發(fā)展的內(nèi)部模式，以更好地量化金融風(fēng)險(xiǎn)。巴塞爾銀行監(jiān)督委員會(huì)，English和Nelson、聯(lián)邦儲(chǔ)備系統(tǒng)專責(zé)小組內(nèi)部信用風(fēng)險(xiǎn)模型，Lopez、Saidenberg、Treacy與Carey用最近的一些觀點(diǎn)和文獻(xiàn)來解決這些問題。</p>&

53、lt;p>　　信用計(jì)分有財(cái)政和非財(cái)務(wù)兩個(gè)方面。然而，當(dāng)前文件的范圍被限制對(duì)銀行客戶的財(cái)政表現(xiàn)的評(píng)估，Bertels試圖研究以衡量公司業(yè)績(jī)的基礎(chǔ)上的定性數(shù)據(jù)。</p><p>　　數(shù)學(xué)建模金融理論始于50年代末， Markowitz的工作是一個(gè)重大的里程碑。財(cái)政部在60年代初，將其作為一個(gè)分學(xué)科，使其從實(shí)踐中達(dá)到了“起飛”階段。早期一些嘗試，是針對(duì)評(píng)價(jià)一個(gè)公司用于兼并和收購(gòu)；一些處理利用投資組合風(fēng)險(xiǎn)管理；一些

54、人處理改進(jìn)/優(yōu)化企業(yè)的融資結(jié)構(gòu)。他們都是針對(duì)增強(qiáng)現(xiàn)有金融理論的指導(dǎo)決策者。</p><p>　　其中1948年的數(shù)學(xué)建模的金融理論已廣泛應(yīng)用，稱作為風(fēng)險(xiǎn)度量。在決策中形成不同黨派經(jīng)濟(jì)活動(dòng)的風(fēng)險(xiǎn)，一家公司的財(cái)務(wù)信息方面發(fā)揮了重要作用。廣泛的文獻(xiàn)致力企業(yè)倒閉的預(yù)言，并且近年來涌現(xiàn)了信用計(jì)分的概念。財(cái)務(wù)比率是為評(píng)估和預(yù)言企業(yè)財(cái)政表現(xiàn)的最簡(jiǎn)單的工具，財(cái)政比率分析的好處和局限演講廣泛應(yīng)用在管理財(cái)務(wù)的文獻(xiàn)。財(cái)政決算報(bào)告堅(jiān)定了

55、公司的立場(chǎng)和關(guān)于過去某一期間的業(yè)務(wù)。但是，仍然有一些在使用比率分析上的局限：①在許多大公司的運(yùn)作，在多個(gè)不同行業(yè)。在這種情況下，很難建立有意義的一套行業(yè)平均數(shù)為比較目的；②通貨膨脹嚴(yán)重扭曲了公司的資產(chǎn)負(fù)債表。此外，記錄的價(jià)值往往大大不同于他們的“真實(shí)”的價(jià)值觀；③季節(jié)性因素可以扭曲比率分析；④公司可聘請(qǐng)“門面技巧”，使他們的財(cái)務(wù)報(bào)表中尋找出路；⑤很難一概而論，對(duì)某一比率，是“好”或“壞”；⑥公司可能有一些的比例，別人很難權(quán)其衡輕重和強(qiáng)弱

56、。</p><p>　　不同國(guó)家、部門或特定時(shí)期，在不同的學(xué)習(xí)研究中，財(cái)務(wù)比率已經(jīng)發(fā)現(xiàn)有助于預(yù)測(cè)企業(yè)是否倒閉。在文獻(xiàn)中，各種各樣方法的出現(xiàn)為塑造企業(yè)倒閉進(jìn)行預(yù)測(cè)分析。Dimitras等人發(fā)現(xiàn)一份全面的文獻(xiàn)研究，來探討這些問題。</p><p>　　在60年代晚期， 判別分析（DA）被用來研究和創(chuàng)造財(cái)政比率綜合經(jīng)驗(yàn)主義。Beaver使用財(cái)政比率，開發(fā)了在未通過的和非倒閉的企業(yè)之間使用單變量的

57、分析技術(shù)的顯示最大區(qū)別。單變量的方法被改進(jìn)了和以后延伸到由Altman建立的多維分布分析。Altma建立的單變量的模型，能證明一些有判別作用可變量。因此，他同時(shí)考慮了幾個(gè)可變量，并使用多重判別分析（MDA）。他辯稱，MDA的優(yōu)點(diǎn)是考慮了對(duì)有關(guān)事務(wù)所對(duì)整個(gè)剖面的共同特點(diǎn)，這項(xiàng)研究還旨在預(yù)測(cè)未來失敗的基礎(chǔ)上的財(cái)務(wù)比率。但在另一方面，一項(xiàng)單變量的研究，認(rèn)為測(cè)量只被使用為小組一次一個(gè)任務(wù)。在選擇可變物至于使用在有識(shí)別力的作用之內(nèi)，Altman

58、審查了各種各樣的供選擇的作用、相互關(guān)系在相關(guān)的可變物之間，有預(yù)測(cè)性的準(zhǔn)確性各種各樣的外形和他自己的評(píng)斷的統(tǒng)計(jì)意義。他認(rèn)為，在破產(chǎn)的2 年之前，破產(chǎn)預(yù)言模型是破產(chǎn)的一位準(zhǔn)確預(yù)測(cè)員，并且模型的準(zhǔn)確性極大地減少當(dāng)訂貨交貨的時(shí)間的增加。竟管對(duì)MDA 的普遍用途，Altman交代對(duì)判別分析有以下弱點(diǎn)：</p><p>　　在隨后的歲月里，許多研究者試圖提高成功的MDA預(yù)測(cè)企業(yè)倒閉。其中Eisenbeis、Peel和Falb

59、o等。這些工作也牽涉土耳其的公司：Unal、Ganamukkala和Karan。</p><p>　　在八十年代，研究利用遞歸分割算法（RPA）基于二元分類樹理被Frydman等人應(yīng)用到了這個(gè)問題，還有Srinivasan和Kim。在80年代和90年代，使用幾個(gè)數(shù)學(xué)編程技術(shù)，豐富了文獻(xiàn)的基本目標(biāo)和方法，這些人逃避的假設(shè)和限制，以往的技術(shù)，以提高分類的準(zhǔn)確性。</p><p>　　在90 年

60、代初期，決策支持系統(tǒng)（DSS）與多準(zhǔn)決策法（MCDM），在財(cái)務(wù)分類問題上得到廣泛使用。Zopounidis、Mareschal、Brans、Zopounidis、Diakoulaki、Siskos、Zopounidis和Doumpos，這些人都是致力于DSS和MCDM的研究，同時(shí)神經(jīng)網(wǎng)絡(luò)方法也被運(yùn)用在了企業(yè)破產(chǎn)問題上。</p><p>　　在1990年代末期， 數(shù)據(jù)包絡(luò)分析（DEA）是Troutt引入到分析信用評(píng)

61、分上的。相對(duì)于目前大致已知的MDA方法，對(duì)企業(yè)倒閉的預(yù)測(cè)（其中需要額外的先驗(yàn)信息，即好/壞分類），DEA方法只需要事后信息，即觀察組的投入和產(chǎn)出，計(jì)算信用分?jǐn)?shù)。因此，它開拓了新的信用評(píng)分。</p><p>　　DEA是家喻戶曉的一個(gè)非參數(shù)方法，是Charnes用在一種數(shù)學(xué)的編程技術(shù)開發(fā)的，DEA的皈依多重輸入與輸出的措施納入單位自由的單一性能指標(biāo)。生產(chǎn)力最大化是嵌入其原始分?jǐn)?shù)表述的一種體現(xiàn)，有能力處理了多條設(shè)定D

62、EA的優(yōu)勢(shì)比其他分析工具。在概念上，DEA方法運(yùn)用到投入和產(chǎn)出，以確定相對(duì)的“最佳做法”?；谶@些最佳觀測(cè)，對(duì)環(huán)保的投入型DEA制定所產(chǎn)生的業(yè)績(jī)指數(shù)值（公信力評(píng)分），如果E小于1時(shí)，24.7%的被認(rèn)為是“低效率”，是相對(duì)于有效前沿所得最佳做法；如果E等于1，24.7%位于有效值。</p><p>　　雖然在八十年代初期，隨著時(shí)間的推移，對(duì)DEA在金融文獻(xiàn)的介紹和應(yīng)用研究是為了獲取更廣泛的承認(rèn)。</p>