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

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

【摘要】：Sharpe (1964)提出的資本資產(chǎn)定價(jià)模型(CAPM)是研究金融市場(chǎng)定價(jià)理論的里程碑,認(rèn)為在資產(chǎn)定價(jià)過(guò)程中,只有系統(tǒng)風(fēng)險(xiǎn)起著不可替代的作用,而非系統(tǒng)風(fēng)險(xiǎn)可以通過(guò)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(xiǎn)固然存在,導(dǎo)致CAPM這一單因素模型的定價(jià)功能逐漸減弱。隨之,多變量資產(chǎn)定價(jià)模型成為現(xiàn)代金融界研究的熱點(diǎn)之一。 然而隨著對(duì)定價(jià)模型研究的發(fā)展和深入,越來(lái)越多的因素被納入到定價(jià)研究的范疇來(lái)解釋各種截面異象。實(shí)證中由于對(duì)這么多因素做回歸可能存在多重共線(xiàn)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(shí)間序列轉(zhuǎn)換有幾個(gè)時(shí)間序列組成的低維時(shí)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(dòng)。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(gè)數(shù)也是未知的需要估計(jì)。本文基于主成分分析方法估計(jì)出公因子及其個(gè)數(shù)后,將已知或可觀(guān)測(cè)到的因素與少量的潛因子建立線(xiàn)性回歸模型,通過(guò)一系列的統(tǒng)計(jì)量來(lái)判定真實(shí)的潛因子是否能表示實(shí)證因子(即可觀(guān)測(cè)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(chǎng)為研究對(duì)象(共計(jì)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(shí)別的角度對(duì)可觀(guān)測(cè)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(jì)階段,通過(guò)Bai和Ng(2002)所建立的面板準(zhǔn)則對(duì)超額收益率序列進(jìn)行潛因子個(gè)數(shù)的相合估計(jì),將維度高達(dá)796的高維時(shí)間序列轉(zhuǎn)換為3維時(shí)間序列。第二,在對(duì)可觀(guān)測(cè)變量的構(gòu)造過(guò)程中發(fā)現(xiàn),中國(guó)A股在樣本期內(nèi)市場(chǎng)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(dòng)量因子的研究中發(fā)現(xiàn)中國(guó)A股市場(chǎng)存在短中期動(dòng)量效應(yīng),長(zhǎng)期反轉(zhuǎn)效應(yīng)。第三,在可觀(guān)測(cè)變量與潛因子的回歸模型估計(jì)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(gè)統(tǒng)計(jì)量估計(jì)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(duì)微觀(guān)經(jīng)濟(jì)變量與潛因子的關(guān)系研究中,市場(chǎng)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(duì)宏觀(guān)經(jīng)濟(jì)變量與潛因子的關(guān)系研究中,發(fā)現(xiàn)宏觀(guān)變量都不及微觀(guān)變量對(duì)股票收益率的變動(dòng)影響,但相對(duì)來(lái)說(shuō),消費(fèi)者滿(mǎn)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(guān)測(cè)變量對(duì)不同行業(yè)股票收益率的影響程度中,市場(chǎng)溢酬因子對(duì)各行業(yè)股票收益率變動(dòng)的影響顯著,宏觀(guān)經(jīng)濟(jì)變量中的通貨膨脹率則對(duì)股票定價(jià)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(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：F832.51;F224

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