本文選題：投資者　切入點(diǎn)：情緒　出處：《華僑大學(xué)》2017年碩士論文

【摘要】：行為金融學(xué)的研究表明,情緒對投資者的決策有顯著的影響,在積極的情緒下,投資者傾向于高估投資機(jī)會、低估投資風(fēng)險(xiǎn),從而交易頻繁;在消極的情緒下,則反之。這樣的投資行為最終使得股票市場產(chǎn)生了超乎尋常的波動,不利于資本市場的穩(wěn)健發(fā)展。若能從“源頭”著手,把投資者的情緒充分運(yùn)用起來,在研究股票市場的漲跌態(tài)勢方面將會開創(chuàng)新的思路。本文擬對投資者情緒進(jìn)行量化,再構(gòu)建隱馬爾科夫模型對股票市場進(jìn)行預(yù)測。本文首先借用情緒-金融決策模型分析了投資者情緒影響股票市場的兩條路徑:“情緒-認(rèn)知-行為”路徑與“情緒-行為”路徑。然后,從理論層面分析了如何以投資者情緒為基礎(chǔ),運(yùn)用隱馬爾科夫模型對股票市場進(jìn)行預(yù)測。在實(shí)證分析部分,本文首先對投資者情緒進(jìn)行測量,運(yùn)用偏最小二乘法(PLS)構(gòu)建出了綜合指標(biāo),該指標(biāo)表明:消費(fèi)者信心指數(shù)、新增股票投資者開戶數(shù)、股票交易量和換手率與投資者情緒正相關(guān),封閉式基金折價(jià)率與投資者情緒負(fù)相關(guān)。然后,考慮到我國股票市場“政策市”的實(shí)際情況,本文對九次利好政策事件、九次利空政策事件進(jìn)行了研究,結(jié)果表明:政策因素對股票市場的影響已經(jīng)被包含在了投資者情緒之中,運(yùn)用投資者情緒對股票市場進(jìn)行預(yù)測無需再單獨(dú)考慮政策因素的影響。接著,本文根據(jù)2007年1月-2015年12月的投資者情緒綜合指標(biāo)數(shù)值,計(jì)算出了隱馬爾科夫模型的關(guān)鍵參數(shù),再根據(jù)預(yù)測原理運(yùn)用MATLAB軟件實(shí)現(xiàn)了對股票市場的預(yù)測,結(jié)果表明:所預(yù)測的時(shí)間段越長、期數(shù)越多,預(yù)測結(jié)果出現(xiàn)的概率越小。進(jìn)一步地,為了突出投資者情緒的作用,本文還運(yùn)用馬爾科夫鏈進(jìn)行了預(yù)測,并對兩種模型的預(yù)測結(jié)果進(jìn)行了檢驗(yàn)和對比,分析表明:隱馬爾科夫模型產(chǎn)生的預(yù)測結(jié)果更加準(zhǔn)確,也就是說,投資者情緒是有助于預(yù)測股票市場的。
[Abstract]:Behavioral finance studies have shown that emotions have a significant impact on investors' decisions. In positive emotions, investors tend to overestimate investment opportunities and underestimate investment risks, thus trading frequently; in negative emotions, On the other hand, this kind of investment behavior ultimately makes the stock market have extraordinary fluctuations, which is not conducive to the steady development of the capital market. If we can start from the "source", we can make full use of the investor's sentiment. This paper intends to quantify investor sentiment, which will lead to a new way of thinking in studying the ups and downs of the stock market. Then we construct a hidden Markov model to predict the stock market. Firstly, we use the mood-financial decision model to analyze the two ways that investor emotion affects the stock market: "emotion, cognition and behavior" and "emotion". -behavior "path. Then, This paper analyzes how to predict the stock market with hidden Markov model on the basis of investor sentiment. In the part of empirical analysis, this paper first measures investor sentiment. The synthetic index is constructed by using partial least square method (PLS). The index shows that the index of consumer confidence, the number of new stock investors' account opening, the trading volume of stocks and the turnover rate are positively related to investor sentiment. The discount rate of closed-end funds is negatively correlated with investor sentiment. Then, considering the actual situation of "policy market" in China's stock market, this paper studies nine good policy events and nine bearish policy events. The results show that the influence of the policy factors on the stock market has been included in the investor sentiment, and the influence of the policy factors should not be considered separately when using the investor sentiment to predict the stock market. In this paper, the key parameters of Hidden Markov Model are calculated according to the comprehensive index of investor sentiment from January 2007 to December 2015, and the prediction of stock market is realized by using MATLAB software according to the prediction principle. The results show that the longer the time period, the more the number of periods, the smaller the probability of the predicted results. Further, in order to highlight the role of investor sentiment, this paper also uses Markov chain to predict. The results of the two models are tested and compared. The results show that the hidden Markov model is more accurate, that is, investor sentiment is helpful to predict the stock market.
【學(xué)位授予單位】：華僑大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：B842.6;F832.51

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