本文關(guān)鍵詞：基于公因子提取法的股票定價有效性研究　出處：《浙江工商大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 資產(chǎn)定價 公共潛因子 可觀測變量 有效性檢驗(yàn)

【摘要】：Sharpe (1964)提出的資本資產(chǎn)定價模型(CAPM)是研究金融市場定價理論的里程碑,認(rèn)為在資產(chǎn)定價過程中,只有系統(tǒng)風(fēng)險起著不可替代的作用,而非系統(tǒng)風(fēng)險可以通過分散化投資得以消除。然而受不完全信息、交易成本等因素的影響,投資者未能持有多樣化的投資組合,非系統(tǒng)風(fēng)險固然存在,導(dǎo)致CAPM這一單因素模型的定價功能逐漸減弱。隨之,多變量資產(chǎn)定價模型成為現(xiàn)代金融界研究的熱點(diǎn)之一。 然而隨著對定價模型研究的發(fā)展和深入,越來越多的因素被納入到定價研究的范疇來解釋各種截面異象。實(shí)證中由于對這么多因素做回歸可能存在多重共線性而導(dǎo)致得不到真實(shí)的結(jié)果。本文嘗試基于漸近主成分分析的高維因子分析法,其核心思想是將高維時間序列轉(zhuǎn)換有幾個時間序列組成的低維時間序列,也即公因子序列,這幾只公因子序列反映了數(shù)據(jù)的絕大部分變動。然而,實(shí)際應(yīng)用中這些公因子往往是未知的,也叫潛因子,公因子的個數(shù)也是未知的需要估計。本文基于主成分分析方法估計出公因子及其個數(shù)后,將已知或可觀測到的因素與少量的潛因子建立線性回歸模型,通過一系列的統(tǒng)計量來判定真實(shí)的潛因子是否能表示實(shí)證因子(即可觀測因子),從而檢驗(yàn)實(shí)證因子的有效性。公因子提取方法的引入,為檢驗(yàn)眾多名目繁多的實(shí)證因子的有效性提供了思路。 本文以A股市場為研究對象(共計796只股票,樣本期間自2009年2月至2013年12月),首次從潛因子識別的角度對可觀測變量進(jìn)行有效性檢驗(yàn)。主要結(jié)論如下：第一,在潛因子估計階段,通過Bai和Ng(2002)所建立的面板準(zhǔn)則對超額收益率序列進(jìn)行潛因子個數(shù)的相合估計,將維度高達(dá)796的高維時間序列轉(zhuǎn)換為3維時間序列。第二,在對可觀測變量的構(gòu)造過程中發(fā)現(xiàn),中國A股在樣本期內(nèi)市場不存在規(guī)模效應(yīng),上市公司股票的平均月收益率隨著公司市值的上升而上升。在動量因子的研究中發(fā)現(xiàn)中國A股市場存在短中期動量效應(yīng),長期反轉(zhuǎn)效應(yīng)。第三,在可觀測變量與潛因子的回歸模型估計結(jié)果中,發(fā)現(xiàn)假設(shè)殘差具有條件異方差性條件下各個統(tǒng)計量估計結(jié)果要優(yōu)于獨(dú)立同分布假設(shè)條件下的結(jié)果。在對微觀經(jīng)濟(jì)變量與潛因子的關(guān)系研究中,市場溢酬因子和賬面市值比因子可以看成是潛因子的替代變量。在對宏觀經(jīng)濟(jì)變量與潛因子的關(guān)系研究中,發(fā)現(xiàn)宏觀變量都不及微觀變量對股票收益率的變動影響,但相對來說,消費(fèi)者滿意程度和通貨膨脹率與潛因子的相關(guān)性最強(qiáng)。第四,在分行業(yè)研究各可觀測變量對不同行業(yè)股票收益率的影響程度中,市場溢酬因子對各行業(yè)股票收益率變動的影響顯著,宏觀經(jīng)濟(jì)變量中的通貨膨脹率則對股票定價起了不可忽視的作用。
[Abstract]:Sharpe (1964) put forward the capital asset pricing model (CAPM) is a milepost on financial market pricing theory, in the process of asset pricing, only plays an irreplaceable role in the system risk and non system risk can be eliminated through portfolio investment. However due to incomplete information, transaction costs and other factors. Investors failed to hold a diversified portfolio of non system risk is exist, resulting in the pricing function model of CAPM the single factor decreases gradually. Then, the multi variable asset pricing model become a hot research topic in modern financial circles.
However, with the development of research on the pricing model and in-depth, more and more factors are incorporated into the scope of the study of pricing to explain various empirical section vision. Because of so many factors may do regression multicollinearity caused no real results. High dimensional factor this paper attempts based on the asymptotic analysis of principal component analysis method, its core idea is the high dimension time series conversion of low dimensional time series is composed of several time series, namely the common factor sequence, these factors reflect the sequence of most data changes. However, the practical application of these factors is often unknown, also called latent factors, need factors the number is unknown. Estimates based on principal component analysis method to estimate the common factor and the number after the factors known or observable and a latent factor to establish the linear regression model, by A series of statistics is used to determine whether the real latent factor can represent the empirical factor (the observation factor), so as to test the effectiveness of the empirical factor. The introduction of the common factor method provides a train of thought for testing the effectiveness of many various empirical factors.
In this paper, the A stock market as the research object (a total of 796 stocks, the sample period from February 2009 to December 2013), for the first time from the perspective of identifying latent factor to test the effectiveness of observable variables. The main conclusions are as follows: first, the latent factor estimation stage, by Bai and Ng (2002) Consistentestimate panel established guidelines a number of potential factors on excess return series, high dimensional time series will be as high as 796 dimensions into 3 dimensional time series. Second, found in the construction process of observable variables, China A shares in the sample period does not exist in the market, shares of listed companies the average monthly rate of return to rise with the market value of the company. In the study of the momentum factor found in the China A-share market there is A short term momentum effect, long-term reversal effect. Third, the regression model of observable variables and latent factor estimation result in false A residual with conditional heteroscedasticity under each statistic estimation results is better than the i.i.d. assumption results. In the study on the relationship between the micro economic variables and latent factor, market factor premium and book to market factor can be regarded as a substitute for latent factor variables. In the research on the relationship between macroeconomic variables and the latent factor, found that the influence of the macro variables are not macro variables on the stock changes in the rate of return, but relatively speaking, the strongest correlation between customer satisfaction and the rate of inflation and the latent factor. In fourth, the industry research variable degree of influence on the stock returns in different industries, the market premium of significant factor the industry rate of stock returns and macroeconomic variables in the inflation rate on stock pricing plays a role can not be ignored.

【學(xué)位授予單位】：浙江工商大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：F832.51;F224

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