【摘要】：隨著行為金融學的蓬勃發(fā)展,投資者情緒受到了越來越高的關(guān)注。本文主要研究了投資者情緒與情緒資產(chǎn)定價模型。我們提出了一套構(gòu)建復(fù)合投資者情緒指標的方法,并說明這套方法可以建立出優(yōu)良的情緒指標。進一步地,將該指標作為情緒因素,引入CAPM和FF三因子模型,可以對現(xiàn)有的資產(chǎn)定價模型起到明顯的改善。首先,本文對情緒指標進行了界定,對原始的情緒指標進行了說明。情緒指標可以分為兩類,一類是反映投資者情緒的指標,另一類是影響投資者情緒的指標。投資者情緒是一個灰箱,影響投資者情緒的指標是輸入項,反映投資者情緒的指標是輸出項。本文中著重于反映投資者情緒的指標,這類指標包括換手率、封閉式基金折價率等,它們是構(gòu)建復(fù)合情緒指標的原始數(shù)據(jù)。同時,本文還建立了一套評價體系,來判斷情緒指標的優(yōu)劣。優(yōu)秀的情緒指標需要與股票價格趨勢有關(guān)聯(lián)性,情緒指標的變化需要對股票收益率產(chǎn)生影響,而且還需具有穩(wěn)健性。然后,我們建立了狀態(tài)空間模型來計算復(fù)合投資者情緒指標。由于模型中的參數(shù)是未知的,需要把該模型轉(zhuǎn)化為自適應(yīng)系統(tǒng)識別問題,即非線性狀態(tài)空間模型,之后再利用擴展的卡爾曼濾波來求解。雖然主成分分析法和TOPSIS法也可以構(gòu)建復(fù)合情緒指標,但是狀態(tài)空間模型法建立的指標與股票價格趨勢的因果性更強,其變化與股票收益率的因果性更強,而且穩(wěn)健性更好。所以,相比主成分分析法和TOPSIS法構(gòu)建的情緒指標,狀態(tài)空間模型法得到的復(fù)合投資者情緒指標是最優(yōu)的。最后,本文在現(xiàn)有的資產(chǎn)定價模型中引入了復(fù)合投資者情緒指標。我們發(fā)現(xiàn)在CAPM或FF三因子模型中添加情緒因子,均會使AIC下降、調(diào)整后的可決系數(shù)上升、擬合優(yōu)度提高,同時,引入情緒的資產(chǎn)定價模型有更強的預(yù)測能力。資產(chǎn)定價模型原有的單因子或三因子在解釋收益率時,還存在無法解釋的部分,情緒因子的引進,會對無法解釋的因素做出一部分合理解釋。這說明引入的情緒因子是有效的,它可以改善現(xiàn)有的資產(chǎn)定價模型。
[Abstract]:With the vigorous development of behavioral finance, investor sentiment has been paid more and more attention. This paper mainly studies investor sentiment and the pricing model of emotional assets. We put forward a set of methods to construct complex investor sentiment index, and show that this method can establish good emotion index. Furthermore, using this indicator as an emotional factor and introducing CAPM and FF three-factor model can improve the existing asset pricing model. First of all, this paper defines the emotional indicators and explains the original emotional indicators. Emotional indicators can be divided into two categories, one is an indicator of investor sentiment, the other is an index that affects investor sentiment. Investor sentiment is a gray box, the index that affects investor sentiment is input item, the index that reflects investor sentiment is output item. This paper focuses on the indicators that reflect investor sentiment, such as turnover rate, closed-end fund discount rate and so on, which are the original data of constructing compound emotion index. At the same time, this paper also established a set of evaluation system to judge the merits and demerits of emotional indicators. Excellent emotional indicators need to be related to the trend of stock prices, and the changes of emotional indicators need to have an impact on stock returns, and also need to be robust. Then, we establish a state space model to calculate the composite investor sentiment index. Because the parameters in the model are unknown, it is necessary to transform the model into an adaptive system identification problem, that is, the nonlinear state space model, and then use extended Kalman filter to solve the problem. Although principal component analysis (PCA) and TOPSIS method can also be used to construct composite emotional indicators, the state space model method has stronger causality to stock price trend, stronger causality to stock return and better robustness. Therefore, compared with the emotional indexes constructed by principal component analysis and TOPSIS method, the state space model method is the best one. Finally, this paper introduces the composite investor sentiment index into the existing asset pricing model. We find that adding emotional factors to the CAPM or FF three-factor model can decrease AIC, increase the resolution coefficient and improve the goodness of fit. At the same time, the asset pricing model with emotion has stronger predictive ability. When the original single or three factors of the asset pricing model explain the yield, there are still some parts that can not be explained. The introduction of the emotion factor will give some reasonable explanation to the unexplained factor. This shows that the introduced emotional factor is effective, it can improve the existing asset pricing model.
【學位授予單位】：吉林大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：C912.6;F832.51

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