過去研究文獻,顯少將信用評等制度與財務危機預警模型一併探討,大多採取單一主題作研究,本研究試圖以實務性角度,利用因素分析、區別分析、logistic迴歸分析等統計技術建立起危機預警模型,而後考量加入公司治理觀念之非財務性變數,並採用層級分析法(AHP),將專家對各項變數權重的看法納入,以產生信用評分模型,本研究係結合財務危機預警模型與信用評分模型,以期能提高預警模型的正確性。研究設計分成兩階段,一次探討兩個主題,得到下述二個結論。 一、 茲將各研究期間根據轉軸後之因素矩陣進行Logistic迴歸分析後發現,以「企業安全性因素」在各研究期間都達最大之影響力。 二、 第二階段加入公司治理觀念之非財務性變數,並採用層級分析法,將專家對各項變數權重的看法納入,以產生信用評分模型,其研究結果發現近年來銀行在甄選授信客戶時,已改變過去只注重在財務指標上表現,對於非財務指標也愈趨重視,本研究結果在財務指標與非財務指標其比重約略各佔50%,顯示銀行授信態度在財務指標偏重於企業的「安全性因素」與「變現性因素」;而在非財務指標首重董監質押率。
Abstract As for the past relevant researches, there were few articles described with integral discussion of both credit rating systems and finance precaution models. Most research articles are adopted with the investigation about each single theme. This research is aimed to take the practical consideration by using the factor analysis, variance analysis and logistic regression analysis statistically to establish the crisis precaution models. Thereafter, we take the non-financial variables from company management ideas into our consideration and also adopt the AHP to combine various weighting variables proposed by experts so that we can create the credit rating systems. This research is to combine the finance crisis precaution models and credit rating models together with the expectation that we can improve the accuracy of finance crisis precaution systems. The research is divided into 2 stages and we can reach 2 conclusion results from these 2 stages as below: 1. We have made the logistic regression analysis onto the factor matrix after the axial rotating from various investigation periods and find that the “enterprise safety factors” will always exert the largest influence during various investigation periods. 2. During the 2nd stage, we add the non-financial variables from company management ideas and also adopt AHP to combine various weighting variables proposed by experts. And then, we create the credit rating systems. We find the results that recently the customer selection made by banks has been changed from the past emphasis onto the financial indicators into the additionally increasing attention to the non-financial indicators. Within this research, the financial indicators and non-financial indicators respectively occupy 50% each. It reveals that the attitude for bank crediting evaluation put its main focus on “safety factors” and “cashing factors” in finance indicators and the directorate and supervision members' hypothecation ratio in non-financial indicators