本文選題：煤炭行業(yè) + 發(fā)債企業(yè)��； 參考：《北京交通大學(xué)》2017年碩士論文

【摘要】：2015年,26家上市的煤炭企業(yè)只取得15.43億元凈利潤(rùn),同比驟跌超過(guò)95%。其中有11家出現(xiàn)虧損,占比高達(dá)四成,煤炭行業(yè)呈整體虧損局面。2016年開(kāi)始觸底反彈,但情況仍然不容樂(lè)觀。同時(shí),銀行對(duì)煤炭行業(yè)的企業(yè)授信開(kāi)始縮緊,煤炭行業(yè)大部分企業(yè)融資渠道受限,資金面緊張。2014-2016年,中國(guó)債市爆發(fā)違約事件,剛性兌付神話不再,大眾開(kāi)始真正關(guān)注信用風(fēng)險(xiǎn),以探求信用風(fēng)險(xiǎn)的影響因素,通過(guò)因素的數(shù)據(jù)分析,對(duì)債券或企業(yè)信用風(fēng)險(xiǎn)進(jìn)行提前預(yù)測(cè),以進(jìn)行合理投資或提前采取措施減少損失。煤炭行業(yè)境況不樂(lè)觀,同時(shí)作為違約債券主體中占比較大行業(yè),受到投資者和債權(quán)人關(guān)注。因此,研究煤炭行業(yè)發(fā)債企業(yè)信用風(fēng)險(xiǎn)具有現(xiàn)實(shí)意義。本文通過(guò)地區(qū)宏觀經(jīng)濟(jì)水平、地區(qū)財(cái)政實(shí)力以及地區(qū)行業(yè)水平對(duì)煤炭行業(yè)發(fā)債企業(yè)信用風(fēng)險(xiǎn)進(jìn)行宏觀層面成因分析,從企業(yè)性質(zhì)、信用評(píng)級(jí)以及企業(yè)的財(cái)務(wù)狀況進(jìn)行微觀層面成因分析。本文以2016年具有存量債券的85家煤炭行業(yè)發(fā)債企業(yè)作為樣本,并根據(jù)現(xiàn)代信用風(fēng)險(xiǎn)定義,將樣本分為包括23家樣本企業(yè)的發(fā)生信用風(fēng)險(xiǎn)事件組和包括62家樣本企業(yè)的未發(fā)生信用風(fēng)險(xiǎn)事件組。在成因分析基礎(chǔ)上,分別從宏觀層面選取5個(gè)指標(biāo)和微觀層面選取10個(gè)指標(biāo),對(duì)各指標(biāo)進(jìn)行相關(guān)性分析,剔除具有共線性解釋變量。之后利用Logistic模型對(duì)煤炭行業(yè)發(fā)債企業(yè)的信用風(fēng)險(xiǎn)影響因素進(jìn)行實(shí)證分析,并得到預(yù)測(cè)煤炭行業(yè)發(fā)債企業(yè)信用風(fēng)險(xiǎn)大小的方程。通過(guò)實(shí)證分析,最后顯示宏觀層面的地區(qū)CPI及地區(qū)固定資產(chǎn)投資價(jià)格指數(shù)以及微觀層面信用評(píng)級(jí)、總資產(chǎn)報(bào)酬率、總資產(chǎn)周轉(zhuǎn)率、資產(chǎn)負(fù)債率以及流動(dòng)資產(chǎn)與總資產(chǎn)的比率共7個(gè)指標(biāo)對(duì)煤炭行業(yè)發(fā)債企業(yè)信用風(fēng)險(xiǎn)具有顯著影響。其中,資產(chǎn)負(fù)債率、流動(dòng)資產(chǎn)與總資產(chǎn)的比率對(duì)煤炭行業(yè)發(fā)債企業(yè)信用風(fēng)險(xiǎn)具有正相關(guān)影響,即該值越低,煤炭行業(yè)發(fā)債企業(yè)發(fā)生信用風(fēng)險(xiǎn)事件可能性越低。其余指標(biāo)對(duì)煤炭行業(yè)發(fā)債企業(yè)信用風(fēng)險(xiǎn)產(chǎn)生負(fù)方向影響,即指標(biāo)數(shù)值越低,煤炭行業(yè)發(fā)債企業(yè)信用風(fēng)險(xiǎn)事件發(fā)生可能性越高。通過(guò)以上7個(gè)指標(biāo),本文生成預(yù)測(cè)煤炭行業(yè)發(fā)債企業(yè)信用風(fēng)險(xiǎn)大小的方程,其預(yù)測(cè)正確率達(dá)到89.4%,具有實(shí)用價(jià)值。可幫助投資者提前預(yù)測(cè)煤炭行業(yè)發(fā)債企業(yè)信用風(fēng)險(xiǎn)大小,協(xié)助企業(yè)債權(quán)人、投資者以及管理者對(duì)企業(yè)未來(lái)信用風(fēng)險(xiǎn)進(jìn)行判斷。
[Abstract]:In 2015, 26 listed coal companies made only 1.543 billion yuan in net profits, down more than 95 percent from a year earlier. Eleven of them lost money, or as much as 40 percent, with the coal industry as a whole losing money. A bottoming out in 2016 is still not encouraging. At the same time, banks began to tighten credit to enterprises in the coal industry, and most of the enterprises in the coal industry were restricted in their financing channels. During the period 2014-2016, when China's bond market defaulted, the myth of rigid payment ceased, and the public began to pay real attention to credit risks. By exploring the influencing factors of credit risk and analyzing the data of the factors, the paper forecasts the credit risk of bond or enterprise in advance, so as to make reasonable investment or take measures to reduce the loss ahead of time. The coal sector is not optimistic, and as the main body of defaulting bonds, the larger sector, investors and creditors are concerned. Therefore, it is of practical significance to study the credit risk of bond issuing enterprises in coal industry. Based on the regional macroeconomic level, regional financial strength and regional industry level, this paper analyzes the causes of the credit risk of the coal industry bond issuing enterprises at the macro level, and analyzes the nature of the enterprises from the point of view of the nature of the enterprises. Credit rating and the financial situation of enterprises are analyzed at the micro level. This paper takes 85 coal industry bond issuers with stocks of bonds in 2016 as a sample, and defines the credit risk according to the modern credit risk definition. The samples are divided into two groups: the occurrence of credit risk event group which includes 23 sample enterprises and the non-occurrence credit risk event group which includes 62 sample enterprises. On the basis of cause analysis, 5 indexes were selected from macro level and 10 indexes were selected from micro level, and the correlation of each index was analyzed, and the co-linear explanatory variables were eliminated. Then the Logistic model is used to analyze the influencing factors of the credit risk of the bond issuing enterprises in coal industry, and the equation of predicting the credit risk of the bond issuing enterprises in the coal industry is obtained. Through the empirical analysis, it shows that the regional CPI and regional fixed asset investment price index, as well as the micro-level credit rating, total asset return rate, total asset turnover rate, The ratio of assets to liabilities and the ratio of current assets to total assets have a significant impact on the credit risk of bond issuing enterprises in coal industry. Among them, the ratio of assets and liabilities, the ratio of current assets to total assets has a positive correlation with the credit risk of bond issuing enterprises in coal industry, that is, the lower the value, the lower the probability of credit risk events. The other indicators have a negative impact on the credit risk of the bond issuing enterprises in coal industry, that is, the lower the index value, the higher the probability of the credit risk events of the bond issuing enterprises in the coal industry. Through the above seven indexes, this paper generates the equation of predicting the credit risk of the bond issuing enterprises in the coal industry, and its correct rate of prediction is up to 89.4, which is of practical value. It can help investors predict the credit risk of bond issuing enterprises in the coal industry in advance, and help the creditors, investors and managers to judge the future credit risk of enterprises.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：F426.21;F832.51

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