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研究生： 黃心欣
Huang, Hsin-Hsin
論文名稱： 以資產負債表基礎之信用評分法或以權益基礎之信用評分法?
Balance-Sheet-Based or Equity-Based Credit Scoring: A Dynamic Perspective Using Binary Quantile Regression
指導教授： 黎明淵
Li, Ming-Yuan
學位類別： 碩士
Master
系所名稱： 管理學院 - 財務金融研究所
Graduate Institute of Finance
論文出版年： 2008
畢業學年度： 96
語文別： 英文
論文頁數： 36
中文關鍵詞： 二元分量迴歸 、信用評分 、違約距離 、邏輯斯迴歸
外文關鍵詞： Z-score, Logistic Regression, Distance to Default, Credit Score, Binary Quantile Regression
相關次數： 點閱：152  下載：5 
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    • 本篇使用二元分量迴歸測試兩種信用評分基礎: (1)財務比率基礎之Z-SCORE (2)權益市場法下之違約距離，對於倒閉公司以及健康公司具有不同的解釋能力。實證結果顯示，會計資訊來源之信用評等指標，健康公司有較高的權重，相對地，倒閉公司有較低的權重；然而權益市場來源之信用評等指標，則有相反的趨勢，對於倒閉公司具有較高的權重，對於好公司則具有較低的權重。本篇論文進一步結合兩種信用評分指標，在考量會計資訊以及股市資訊後，比較傳統線性模型以及非線性模型兩者間之配適力。發現二元分量迴歸模型相較邏輯斯模型較不易將倒閉公司誤判為健康公司。以風險偏好之觀點，二元分量迴歸模型較適合風險趨避者。

    Financial data for U.S. firms (bankrupt/healthy firms) listed during 1996-2006 are analyzed. A key feature of this study is analysis of changing distribution of corporate nature across firms and over time by binary quantile regression (hereafter BQR) model and comparison of the results with Logistic Regression (hereafter LR). The nonlinearities derived from conditional BQR reveal considerable discrimination between different sources of credit scoring indicator vary by firms-nature (bankrupt/ healthy firms). On view of pre-quarter prediction, source from market indicator have interpretation on bankrupt firms, source form accounting indicator has less interpretation on bankrupt firms. By validation of credit model, we evaluating BQR and LR performance have separate goodness. BQR model at each quantile outperform on type I error. For type II error, LR model outperform than BQR. In view of investor risk preference, BQR model is suitable for risk-averse investor.

    中文摘要 II ABSTRACT III 致　謝 IV CONTENTS V LIST OF TABLES VI LIST OF FIGURES VII I. Introduction 1 II. Literature Reviews 3 III. Empirical Methods 6 1. No quantile models: LR 6 2. BQR model 7 IV. Validation Methodology 10 1. Cumulative Accuracy Profile Curve (CAP curve) 10 2. Receiver Operating Characteristic Curve (ROC curve) 11 V. Data 13 1. The sample 13 2. Calculation of Distance to Default (DD) 13 3. Calculation of Z-score 14 VI. Empirical Results 16 VII. Conclusions and directions for future research 18 Reference 19 LIST OF TABLES TABLE 1 DEFINITION OF DEPENDENT/INDEPENDENT VARIABLES 21 TABLE 2 DESCRIPTIVE STATISTICS DEPENDENT/INDEPENDENT VARIABLES 21 TABLE 3 LR AND BQR ESTIMATES COEFFICIENT 22 TABLE 4 VALIDATION COEFFICIENT: LR AND BQR 22 TABLE 5 TYPE I ERROR 23 TABLE 6 TYPE II ERROR 24 LIST OF FIGURES FIGURE 1 CAP CURVE 25 FIGURE 2 ROC CURVE 25 FIGURE 3 ABSOLUTE IMPACT OF DD ON FIRMS-NATURE BY BQR 26 FIGURE 4 ABSOLUTE IMPACT OF Z-SCORE ON FIRMS-NATURE BY BQR 26 FIGURE 5 CAP LOGISTIC VS. QUANTILE=0.05 27 FIGURE 6 ROC LOGISTIC VS. QUANTILE=0.05 27 FIGURE 7 CAP LOGISTIC VS. QUANTILE=0.15 28 FIGURE 8 ROC LOGISTIC VS. QUANTILE=0.15 28 FIGURE 9 CAP LOGISTIC VS. QUANTILE=0.25 29 FIGURE 10 ROC LOGISTIC VS. QUANTILE=0.25 29 FIGURE 11 CAP LOGISTIC VS. QUANTILE=0.35 30 FIGURE 12 ROC LOGISTIC VS. QUANTILE=0.35 30 FIGURE 13 CAP LOGISTIC VS. QUANTILE=0.45 31 FIGURE 14 ROC LOGISTIC VS. QUANTILE=0.45 31 FIGURE 15 CAP LOGISTIC VS. QUANTILE=0.55 32 FIGURE 16 ROC LOGISTIC VS. QUANTILE=0.55 32 FIGURE 17 CAP LOGISTIC VS. QUANTILE=0.65 33 FIGURE 18 ROC LOGISTIC VS. QUANTILE=0.65 33 FIGURE 19 CAP LOGISTIC VS. QUANTILE=0.75 34 FIGURE 20 ROC LOGISTIC VS. QUANTILE=0.75 34 FIGURE 21 CAP LOGISTIC VS. QUANTILE=0.85 35 FIGURE 22 ROC LOGISTIC VS. QUANTILE=0.85 35 FIGURE 23 CAP LOGISTIC VS. QUANTILE=0.95 36 FIGURE 24 ROC LOGISTIC VS. QUANTILE=0.95 36

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