【摘要】：公司債券市場(chǎng)是債券市場(chǎng)的重要組成部分,公司債券市場(chǎng)的發(fā)展與完善直接關(guān)系著債券市場(chǎng)甚至資本市場(chǎng)的運(yùn)行效率,因此,完善中國(guó)公司債券市場(chǎng)是我國(guó)資本市場(chǎng)健康發(fā)展的根本所在。然而,我國(guó)債券市場(chǎng)尤其是公司債券市場(chǎng)已經(jīng)遠(yuǎn)遠(yuǎn)落后于股票市場(chǎng)；除去制度因素之外,我國(guó)公司債券市場(chǎng)落后的主要技術(shù)根源便是公司債券評(píng)級(jí)方法的落后。 本文在國(guó)內(nèi)外債券評(píng)級(jí)的研究基礎(chǔ)之上,選用MDA、Logistic模型、Probit模型以及神經(jīng)網(wǎng)絡(luò)四種債券評(píng)級(jí)方法,結(jié)合中國(guó)上市公司的風(fēng)險(xiǎn)特征,從變量甄選的角度對(duì)債券評(píng)級(jí)方法進(jìn)行優(yōu)化,除選取部分國(guó)內(nèi)外公認(rèn)的財(cái)務(wù)指標(biāo)外,還選取了公司控股性質(zhì),Tobin q,β以及EBIT/流動(dòng)負(fù)債四個(gè)指標(biāo)；同時(shí)采用中國(guó)上市公司數(shù)據(jù)對(duì)評(píng)級(jí)方法的應(yīng)用能力進(jìn)行實(shí)證檢驗(yàn),并基于評(píng)級(jí)結(jié)果,從資產(chǎn)定價(jià)理論出發(fā)構(gòu)建出債券組合的投資策略。實(shí)證結(jié)論表明：本文甄選出的評(píng)級(jí)變量較國(guó)外常用的評(píng)級(jí)指標(biāo)更好的刻畫了中國(guó)上市公司的風(fēng)險(xiǎn)特征；Logistic模型、Probit模型和神經(jīng)網(wǎng)絡(luò)方法都對(duì)中國(guó)上市公司的債券有較高的評(píng)級(jí)分類能力,對(duì)于訓(xùn)練樣本,這三種債券評(píng)級(jí)方法都能夠?qū)?5%以上的債券類型正確區(qū)分,尤其是Probit模型,能夠?qū)⒂?xùn)練樣本中的所有上市公司正確分類,對(duì)于測(cè)試樣本,這三種評(píng)級(jí)模型均能夠?qū)?3%的公司債券正確分類。綜合考察訓(xùn)練樣本和測(cè)試樣本,Probit模型和BP神經(jīng)網(wǎng)絡(luò)方法的評(píng)級(jí)結(jié)果非常準(zhǔn)確,債券評(píng)級(jí)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(xué)位授予單位】：東北財(cái)經(jīng)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：F832.51;F224

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本文編號(hào)：2170049