一种新的定价因子构建方法及在我国的应用
发布时间：2019-01-26 17:54
【摘要】：在我国,股票市场已成为重要的投资渠道。投资者选择不同的投资组合将面临不同的风险,风险补偿因此成为重要的定价因素,寻找有效的股票收益定价因子成为金融研究的热点和难点。本文提出了一种新的因子构建方法,以提高模型定价水平。具体来说,本文认为使用单一异象变量进行因子构建,会使其包含较多噪声,进而影响模型定价水平。基于这一分析,本文利用三个可以反映盈利能力的指标,构建得到综合盈利因子SPF;利用三个可以反映投资大小的指标,构建得到综合投资因子SIF,据此得到一种四因子模型SCM。利用该四因子模型对账面市值比因子HML和动量因子UMD进行定价,发现当考虑模型中四个因子之后,HML和UMD不再包含额外的定价信息。将SPF对市值因子MKT、规模因子SMB、HML、UMD和SIF进行回归,剔除因子共有信息之后,得到一种新的综合盈利因子SPFN。据此,本文构建了另一种四因子模型SCMN。基于理论分析,可以预期这两个因子模型的表现优于本文涉及的其它5个模型。在实证检验部分,本文首先对MKT、SMB、HML、UMD、盈利因子RMW、投资因子CMA、SPF和SIF这8个因子的定价能力进行了比较分析。结果表明,MKT表现最优,SMB和SPF次之,这表明本文构建得到的综合盈利因子优于RMW。接着,本文利用随机抽样的方法进行了组合构建,使得组合不包含任何先验信息,结果表明仅有MKT被选入模型。这一结果表明当利用异象变量进行组合构建时,其会包含先验信息,会对因子有所偏好,进而使得模型检验结果存在偏误。在模型检验部分,利用本文构建的18个检验组合对包含SCM和SCMN在内的7个定价模型进行了检验,结果表明本文构建的SCMN和SCM在表现最优次数和不被拒绝次数两个层面均优于其它5个模型。最后本文利用三种方法进行了稳健性分析,以实现两个目标:检验组合包含更多的先验信息,同时获得充分多的检验组合。检验结果表明,从表现最优数量来看,SCMN最多,SCM次之;从模型不被拒绝比例来看,SCMN最高;从模型稳定性来看,SCMN表现最稳定,SCM次之。利用上述稳健性分析,进一步证实本文构建的两个模型有更高的定价能力。这一结果表明本文提出的因子构建方法能够提高模型定价水平,而利用美国数据进行的实证检验同样证实了这一点。基于理论分析和实证研究,并结合最新文献成果,本文认为可以在如下两个方面对因子模型进行更为深入的研究:一,如何在定价模型中包含更多信息,进而能对足够多的异象变量进行解释,这一点是很有研究价值的。是否能够提出新的方法,使得定价模型在具有良好计量性质的前提下,包含更多的定价信息,值得进一步深入研究;二,对非嵌套因子模型的差异显著性进行分析研究,进而对定价模型含义有更深入的认知。
[Abstract]:In our country, the stock market has become an important investment channel. Investors will face different risks when they choose different portfolios, so risk compensation has become an important pricing factor. Finding an effective pricing factor of stock returns has become a hot and difficult point in financial research. In this paper, a new method of factor construction is proposed to improve the pricing level of the model. Specifically, this paper argues that the use of a single aberrant variable for factor construction will make it contain more noise, thus affecting the pricing level of the model. Based on this analysis, this paper constructs a comprehensive profit factor SPF; by using three indexes that can reflect profitability. Using three indexes which can reflect the investment size, we construct the comprehensive investment factor SIF, and get a four-factor model SCM.. The four-factor model is used to price the book-to-market ratio factor (HML) and momentum factor (UMD). It is found that HML and UMD no longer contain additional pricing information after considering the four factors in the model. The SPF regression is applied to the market value factor MKT, scale factor SMB,HML,UMD and SIF. After removing the common information of the factors, a new comprehensive profit factor SPFN. is obtained. Based on this, another four-factor model, SCMN., is constructed in this paper. Based on the theoretical analysis, it can be expected that the performance of these two models is better than the other five models. In the part of empirical test, this paper firstly analyzes the pricing ability of MKT,SMB,HML,UMD, profit factor, RMW, investment factor CMA,SPF and SIF. The results show that MKT is the best, SMB and SPF are the second, which indicates that the comprehensive profit factor constructed in this paper is superior to RMW.. Then, the method of random sampling is used to construct the combination so that the combination does not contain any prior information. The results show that only MKT is selected into the model. The results show that when the visionary variables are combined, they will contain prior information and have a preference for the factors, which will make the model test results biased. In the part of model checking, seven pricing models, including SCM and SCMN, are tested by using the 18 test combinations constructed in this paper. The results show that the SCMN and SCM constructed in this paper are superior to the other five models in terms of the optimal times of performance and the times of non-rejection. Finally, the robustness analysis is carried out by using three methods to achieve two objectives: the test combination contains more prior information and the sufficient number of test combinations is obtained at the same time. The test results show that, in terms of the optimal number of performance, SCMN is the most, SCM is the second, SCMN is the highest in terms of the proportion of model not rejected, SCMN is the most stable in terms of model stability, and SCM is the second. By using the above robust analysis, it is further proved that the two models constructed in this paper have higher pricing power. The results show that the proposed method of factor construction can improve the pricing level of the model, and the empirical test using American data also confirms this point. Based on the theoretical analysis and empirical research, combined with the latest literature, this paper thinks that we can do more in-depth research on the factor model in the following two aspects: first, how to include more information in the pricing model, It is very valuable to explain enough aberration variables. Whether we can put forward a new method to make the pricing model contain more pricing information under the premise of good metrological property is worthy of further study; Secondly, the significance of the non-nested factor model is analyzed, and the meaning of the pricing model is further recognized.
【学位授予单位】：南京大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：F224;F832.51
，
本文编号：2415751
[Abstract]:In our country, the stock market has become an important investment channel. Investors will face different risks when they choose different portfolios, so risk compensation has become an important pricing factor. Finding an effective pricing factor of stock returns has become a hot and difficult point in financial research. In this paper, a new method of factor construction is proposed to improve the pricing level of the model. Specifically, this paper argues that the use of a single aberrant variable for factor construction will make it contain more noise, thus affecting the pricing level of the model. Based on this analysis, this paper constructs a comprehensive profit factor SPF; by using three indexes that can reflect profitability. Using three indexes which can reflect the investment size, we construct the comprehensive investment factor SIF, and get a four-factor model SCM.. The four-factor model is used to price the book-to-market ratio factor (HML) and momentum factor (UMD). It is found that HML and UMD no longer contain additional pricing information after considering the four factors in the model. The SPF regression is applied to the market value factor MKT, scale factor SMB,HML,UMD and SIF. After removing the common information of the factors, a new comprehensive profit factor SPFN. is obtained. Based on this, another four-factor model, SCMN., is constructed in this paper. Based on the theoretical analysis, it can be expected that the performance of these two models is better than the other five models. In the part of empirical test, this paper firstly analyzes the pricing ability of MKT,SMB,HML,UMD, profit factor, RMW, investment factor CMA,SPF and SIF. The results show that MKT is the best, SMB and SPF are the second, which indicates that the comprehensive profit factor constructed in this paper is superior to RMW.. Then, the method of random sampling is used to construct the combination so that the combination does not contain any prior information. The results show that only MKT is selected into the model. The results show that when the visionary variables are combined, they will contain prior information and have a preference for the factors, which will make the model test results biased. In the part of model checking, seven pricing models, including SCM and SCMN, are tested by using the 18 test combinations constructed in this paper. The results show that the SCMN and SCM constructed in this paper are superior to the other five models in terms of the optimal times of performance and the times of non-rejection. Finally, the robustness analysis is carried out by using three methods to achieve two objectives: the test combination contains more prior information and the sufficient number of test combinations is obtained at the same time. The test results show that, in terms of the optimal number of performance, SCMN is the most, SCM is the second, SCMN is the highest in terms of the proportion of model not rejected, SCMN is the most stable in terms of model stability, and SCM is the second. By using the above robust analysis, it is further proved that the two models constructed in this paper have higher pricing power. The results show that the proposed method of factor construction can improve the pricing level of the model, and the empirical test using American data also confirms this point. Based on the theoretical analysis and empirical research, combined with the latest literature, this paper thinks that we can do more in-depth research on the factor model in the following two aspects: first, how to include more information in the pricing model, It is very valuable to explain enough aberration variables. Whether we can put forward a new method to make the pricing model contain more pricing information under the premise of good metrological property is worthy of further study; Secondly, the significance of the non-nested factor model is analyzed, and the meaning of the pricing model is further recognized.
【学位授予单位】：南京大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：F224;F832.51
，
本文编号：2415751
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