- 期刊
探討支持向量機器在發行人信用評等分類模式之應用
A Study of SVM Classification Models in Issuers' Credit Ratings
摘要
信用評等制度在金融市場已行之有年，其在企業籌資、投資人資訊取得、銀行授信參考，以及規範一般機構投資標的上，均扮演著相當重要的角色。信用評等的主要目的乃在評量債券、票券發行機構或存款機構信用品質的良窳，以利投資人做出合理的決策。過去信用評等的研究大多針對一般產業的公司債建立分類模式，較少針對發行機構本身的信用評等進行研究。早期的研究方法大多採用統計方法，近期乃有以人工智慧為基礎之各種演算法，例如類神經網路及case-based reasoning等。本研究嘗試應用一項新近發展且已獲得相當高分類正確率的人工智慧方法support vector machines (SVM)來建構發行人信用評等分類模式。為驗證SVM的可用性，我們將以標準普爾公司（Standard and Poor's，以下簡稱S&P）所發佈的一般產業發行人信用評等資料樣本為例，選擇S&P評時所考慮之相關重要財務變數及國家風險因素作為模式的輸入變數，並以類神經網路方法為基準與SVM方法進行比較，實證結果顯示SVM模式優於類神經網路模式。
並列摘要
Credit rating systems have existed for a long time in most financial markets and played a major role in corporate capital raising, providing investment information for both individual investors and institutional investors, and credit granting in banks. The purpose of credit ratings is to measure the credit worthiness of credit securities' issuers so as to provide investors valuable information in making financial decisions. Due to the fact that the subordination of bonds has a great impact on the bond's rating (hence render the rating problem much easier to solve), most of the early researches have focused on industrial bond ratings rather than issuers' credit rating. In terms of classification approaches, early researches relied on conventional statistic methods, while recent studies tended to apply artificial intelligence based techniques, such as artificial neural networks and case-based reasoning. The main objective of this research is to propose a classification model for the issuers' credit ratings based on support vector machines, a novel classification algorithm famous for dealing with high dimension classification. To verify the capability of the proposed model, a set of Standard and Poor's issuers' credit rating data as used as the test bed. To construct our classification models, the ten key financial variables used by Standard and Poor's (S&P), and country risk were chosen as the input variables. An artificial neural network based classification model as selected as the benchmark. Our empirical results showed the superiority of the support vector machine model over the neural artificial network model.