The Study Of The Impact Factors About Credit Spread Of Real Estate Industry Corporate Bonds

The China Securities Regulatory Commission has issued "Administrative Measures for the Issuance and Transaction of Corporate Bonds" in 2015,The issuance of corporate bonds was expanded from listed companies to all legal entities.At the same time,it has relaxed the restrictions on the issuance of shares,and the issuance period of their own circulation sites.This made the corporate bond issuance market flourish.The total issuances in 2015 and 2016 exceeded 100%year-on-year.At the same time,the bond market is also turbulent.Since the first public bond default event occurred in China's bond market in 2014,public-bond market bonds have frequently been breached.In addition,many corporate bonds issuance period are 3 years or "3+2" years,so that corporate bonds issued in large-scale in 2015 will be concentrated in 2018.The debtor's repayment pressure can be imagined,which is inevitable some companies may have technical breaches or even substantive breaches due to poor operating performance or cash flow.In this context,the importance of corporate credit spread research work is self-evident.The issuance scale of corporate bonds in the real estate industry accounts for a very large proportion.Therefore,the research on the credit spread of corporate bonds in the real estate industry is even more important.In order to study the influencing factors of corporate bond credit spreads in the real estate industry,this paper first combines the existing research results with the characteristics of corporate bonds in the real estate industry;and then theoretically classifies the various influencing factors into three parts:macro factors of environment level,of company level and of single bond level.Then the general corporate bond issued by the real estate industry from 2015 to 2017 is taken as the research object.A multiple linear regression model is used for analysis,and a stepwise regression method and Lasso are used for variable selection.Multiplication is optimized;finally,use the data from January to March in 2018 to predict,and compare the predicted value with the actual value.