銀行對中小企業授信決策與授信風險關聯性之研究

THE CORRELATION BETWEEN CREDIT RISK AND FINANCIAL RATIOS OF SMALL AND MEDIUM SIZED ENTERPRISES

高裕豐(Kao,Yu-Feng)

指導教授 ： 古永嘉

國立臺北大學/商學院/國際財務金融碩士在職專班/碩士(2013年)

摘要

銀行為金融體係之主要機構，作為資金需求者與資金供給者橋樑。2011年受歐洲主權債務危機擴大面對如此的國內外經濟環境，銀行對中小企業授信之品質與風險管理已成為各家銀行鑽研之重要課題。銀行如何控管其風險，創造雙贏，乃是一大難題。本研究擷取A銀行授信正常戶291戶及違約戶50戶，借款期間自2008年至2012年之中小企業信用評等表，探討:
一、銀行面對中小企業授信評等可能風險因素及重要的指標比率研討。
二、針對正常戶與違約戶進行差異性t-檢定，以分析貸款額度等16項信評對企業特性有無重要顯著差異。
三、針對授信戶違約之可能影響因素，進行羅吉斯迴歸的預測模型分析。
經由實證結果，獲致三點結論：
一、經資料整理分析，銀行業對於中小企業之徵信人員撰寫徵信報告4C-品格(Character)、能力(Capacity)、資本(Capital)、擔保品(Collateral)及授信面對之資風險因素為信用五P-借款人（People）、資金用途（Purpose）、還款來源（Payment）、債權保障（Protection）及授信展望（Perspective）等要素之簡介。參考的財務比率為財務五力，即償債能力(Debt-paying ability)、資本結構(Stability)、經營效能(Activity power)、獲利能力(Profitability)及成長率(Growth)。
二、差異性t-檢定結果顯示，正常戶與違約戶在貸款額度、企業成立年限、負責人從事本業年資、聯徵J21外部信評、營業場所是否為租賃、營授比、負責人有無信貸、負責人有無卡循、負責人有無連保、分數等確實有很大差異，整體模型的準確預測率達98.24％，模型預測能力極高。此結果可提供銀行授信時之基本依據。
三、經過逐步羅吉迴歸分析後，發現貸款期間、有無擔保、聯徵J21外部信評、營業場所是否為租賃、營授比、企業近二年營業額成長率變動、借款戶之負責人其配偶有無作保等7個變數，整體模型的準確預測率達95.89％，模型預測能力極高。對授信品質更具有顯著影響力，此研究結論可提供未來銀行授信決策時之參考。

關鍵字

中小企業放款 ； 信用風險 ； 羅吉斯迴歸

並列摘要

As a major financial institution in financial system, bank is a bridge between fund demander and supplier. Due to the financial crisis in 2011, the quality and risk control of loan to small and medium-sized enterprises’ (SME) has become a popular issue from bank to bank. In this research, we choose 291 SMEs which financial status is defined as normal and 50 SMEs which is defined as delinquent firms. The data period is from 2008 to 2012 in order to discuss the associated risk factors and other indicators of SME’s credit ratings. We used T-test between normal and delinquent companies to analyze whether there exist significant difference in items such as operating risk, existence of factory/office, and different industries. This research adopted logistic regression models to analyze which factors may result in delinquency. The research findings are derived as follows:
1. Five risk factors, including people（borrower）, purpose, payment, protection, and perspective are the major factors in credit analysis. Five financial indexes were adopted to reflect the financial abilities of the enterprises, i.e., debt-paying ability, stability, activity power, profitability, and growth.

2. T-tests show that normal and delinquent firms exist significant difference in operating history of the enterprise, existence of factory/office, type of industry, amount of credit extended, capital, and business volume. In terms of the respective mean (average number) of the amount of credit extended, capital, and business volume, the normal accounts are significantly greater than those of default accounts. This means that normal enterprises need more funds for their operations. Meanwhile, it certifies that sufficient capital stabilizes asset structure, and high business volume represents the continuing growth on sales. These results on T-test provide an important reference for banks to conduct credit business. With ten financial variables, the logistic model shows 98.24% forecasting accuracy.

3. Stepwise Logistic regression analysis shows that financial ratios such as current ratio, quick ratio, capitalization ratio, debt-to-equity ratio, net worth ratio, receivable turnover ratio, assets turnover ratio, gross profit ratio, return on assets, etc. have notable impact on bank’s credit decision making. The seven financial variables model’s forecasting accuracy is 95.89%, indicating a very satisfactory findings and could be applied to bank loan management.

並列關鍵字

SME Loan ； Credit Risk ； Logistic Regression

參考文獻

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