【摘要】：在我國(guó),股票市場(chǎng)已成為重要的投資渠道。投資者選擇不同的投資組合將面臨不同的風(fēng)險(xiǎn),風(fēng)險(xiǎn)補(bǔ)償因此成為重要的定價(jià)因素,尋找有效的股票收益定價(jià)因子成為金融研究的熱點(diǎn)和難點(diǎn)。本文提出了一種新的因子構(gòu)建方法,以提高模型定價(jià)水平。具體來(lái)說(shuō),本文認(rèn)為使用單一異象變量進(jìn)行因子構(gòu)建,會(huì)使其包含較多噪聲,進(jìn)而影響模型定價(jià)水平�；谶@一分析,本文利用三個(gè)可以反映盈利能力的指標(biāo),構(gòu)建得到綜合盈利因子SPF;利用三個(gè)可以反映投資大小的指標(biāo),構(gòu)建得到綜合投資因子SIF,據(jù)此得到一種四因子模型SCM。利用該四因子模型對(duì)賬面市值比因子HML和動(dòng)量因子UMD進(jìn)行定價(jià),發(fā)現(xiàn)當(dāng)考慮模型中四個(gè)因子之后,HML和UMD不再包含額外的定價(jià)信息。將SPF對(duì)市值因子MKT、規(guī)模因子SMB、HML、UMD和SIF進(jìn)行回歸,剔除因子共有信息之后,得到一種新的綜合盈利因子SPFN。據(jù)此,本文構(gòu)建了另一種四因子模型SCMN�；诶碚摲治�,可以預(yù)期這兩個(gè)因子模型的表現(xiàn)優(yōu)于本文涉及的其它5個(gè)模型。在實(shí)證檢驗(yàn)部分,本文首先對(duì)MKT、SMB、HML、UMD、盈利因子RMW、投資因子CMA、SPF和SIF這8個(gè)因子的定價(jià)能力進(jìn)行了比較分析。結(jié)果表明,MKT表現(xiàn)最優(yōu),SMB和SPF次之,這表明本文構(gòu)建得到的綜合盈利因子優(yōu)于RMW。接著,本文利用隨機(jī)抽樣的方法進(jìn)行了組合構(gòu)建,使得組合不包含任何先驗(yàn)信息,結(jié)果表明僅有MKT被選入模型。這一結(jié)果表明當(dāng)利用異象變量進(jìn)行組合構(gòu)建時(shí),其會(huì)包含先驗(yàn)信息,會(huì)對(duì)因子有所偏好,進(jìn)而使得模型檢驗(yàn)結(jié)果存在偏誤。在模型檢驗(yàn)部分,利用本文構(gòu)建的18個(gè)檢驗(yàn)組合對(duì)包含SCM和SCMN在內(nèi)的7個(gè)定價(jià)模型進(jìn)行了檢驗(yàn),結(jié)果表明本文構(gòu)建的SCMN和SCM在表現(xiàn)最優(yōu)次數(shù)和不被拒絕次數(shù)兩個(gè)層面均優(yōu)于其它5個(gè)模型。最后本文利用三種方法進(jìn)行了穩(wěn)健性分析,以實(shí)現(xiàn)兩個(gè)目標(biāo):檢驗(yàn)組合包含更多的先驗(yàn)信息,同時(shí)獲得充分多的檢驗(yàn)組合。檢驗(yàn)結(jié)果表明,從表現(xiàn)最優(yōu)數(shù)量來(lái)看,SCMN最多,SCM次之;從模型不被拒絕比例來(lái)看,SCMN最高;從模型穩(wěn)定性來(lái)看,SCMN表現(xiàn)最穩(wěn)定,SCM次之。利用上述穩(wěn)健性分析,進(jìn)一步證實(shí)本文構(gòu)建的兩個(gè)模型有更高的定價(jià)能力。這一結(jié)果表明本文提出的因子構(gòu)建方法能夠提高模型定價(jià)水平,而利用美國(guó)數(shù)據(jù)進(jìn)行的實(shí)證檢驗(yàn)同樣證實(shí)了這一點(diǎn)�；诶碚摲治龊蛯�(shí)證研究,并結(jié)合最新文獻(xiàn)成果,本文認(rèn)為可以在如下兩個(gè)方面對(duì)因子模型進(jìn)行更為深入的研究:一,如何在定價(jià)模型中包含更多信息,進(jìn)而能對(duì)足夠多的異象變量進(jìn)行解釋,這一點(diǎn)是很有研究?jī)r(jià)值的。是否能夠提出新的方法,使得定價(jià)模型在具有良好計(jì)量性質(zhì)的前提下,包含更多的定價(jià)信息,值得進(jìn)一步深入研究;二,對(duì)非嵌套因子模型的差異顯著性進(jìn)行分析研究,進(jìn)而對(duì)定價(jià)模型含義有更深入的認(rèn)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.
【學(xué)位授予單位】：南京大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：F224;F832.51
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本文編號(hào)：2415751