【摘要】：公司債券市場是債券市場的重要組成部分,公司債券市場的發(fā)展與完善直接關系著債券市場甚至資本市場的運行效率,因此,完善中國公司債券市場是我國資本市場健康發(fā)展的根本所在。然而,我國債券市場尤其是公司債券市場已經遠遠落后于股票市場；除去制度因素之外,我國公司債券市場落后的主要技術根源便是公司債券評級方法的落后。 本文在國內外債券評級的研究基礎之上,選用MDA、Logistic模型、Probit模型以及神經網(wǎng)絡四種債券評級方法,結合中國上市公司的風險特征,從變量甄選的角度對債券評級方法進行優(yōu)化,除選取部分國內外公認的財務指標外,還選取了公司控股性質,Tobin q,β以及EBIT/流動負債四個指標；同時采用中國上市公司數(shù)據(jù)對評級方法的應用能力進行實證檢驗,并基于評級結果,從資產定價理論出發(fā)構建出債券組合的投資策略。實證結論表明：本文甄選出的評級變量較國外常用的評級指標更好的刻畫了中國上市公司的風險特征；Logistic模型、Probit模型和神經網(wǎng)絡方法都對中國上市公司的債券有較高的評級分類能力,對于訓練樣本,這三種債券評級方法都能夠將95%以上的債券類型正確區(qū)分,尤其是Probit模型,能夠將訓練樣本中的所有上市公司正確分類,對于測試樣本,這三種評級模型均能夠將93%的公司債券正確分類。綜合考察訓練樣本和測試樣本,Probit模型和BP神經網(wǎng)絡方法的評級結果非常準確,債券評級的誤判率幾乎為0。
[Abstract]:The corporate bond market is an important part of the bond market. The development and perfection of the corporate bond market are directly related to the operating efficiency of the bond market and even the capital market. Therefore, improving the Chinese corporate bond market is the root of the healthy development of the capital market in China. However, the bond market, especially the corporate bond market, is far away in China. Far behind the stock market; apart from institutional factors, the main technical root of the backwardness of China's corporate bond market is the backwardness of corporate bond rating methods.
Based on the study of bond rating at home and abroad, this paper selects four bond rating methods, MDA, Logistic model, Probit model and neural network. It combines the risk characteristics of Chinese listed companies and optimizes the bond rating method from the angle of variable selection. Holding nature, Tobin Q, beta and EBIT/ mobile liabilities four indicators, and using the data of Chinese listed companies to test the application capacity of the rating method, and based on the rating results, the investment strategy of the bond portfolio is constructed from the asset pricing theory. The empirical conclusion shows that the rating variables selected in this paper are more commonly used than the foreign countries. The rating indicators better depict the risk characteristics of Chinese listed companies; the Logistic model, Probit model and neural network approach have higher rating classification ability for Chinese listed companies. For training samples, these three bond rating methods can correctly distinguish over 95% of the bond types, especially the Probit model, All listed companies in the training sample can be correctly classified. For the test samples, the three rating models can correctly classify 93% of the corporate bonds. The comprehensive inspection of training samples and test samples, the Probit model and the BP neural network method are very accurate, and the rate of miscarriage of debt vouchers is almost 0..
【學位授予單位】：東北財經大學
【學位級別】：碩士
【學位授予年份】：2012
【分類號】：F832.51;F224

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本文編號：2170049