本文選題：投資者行為 + 高頻數(shù)據(jù)��； 參考：《首都經(jīng)濟貿(mào)易大學(xué)》2014年博士論文

【摘要】：伴隨著證券市場的蓬勃發(fā)展以及信息技術(shù)、數(shù)據(jù)處理技術(shù)的飛速進步，金融數(shù)據(jù)由原來月、周以及日數(shù)據(jù)逐步細(xì)到小時、分鐘甚至秒的數(shù)據(jù)，這種超高頻數(shù)據(jù)的采集對于我們金融領(lǐng)域研究，尤其是當(dāng)今行為金融研究盛行的時代，顯得異常重要。 行為金融將心理學(xué)和金融學(xué)結(jié)合起來進行研究，認(rèn)為投資者的決策不僅受到客觀市場的制約，還會受到來自自身或周圍群體的主觀方面的影響，因而投資者會表現(xiàn)出各種異常，在證券市場上相應(yīng)地體現(xiàn)為各種異象，對于這些異象的解釋也就層出不窮。 本文針對人們聚焦的投資者行為，從行為金融理論出發(fā)，運用適合（超）高頻數(shù)據(jù)的自回歸條件持續(xù)期模型（ACD model），通過對它基本形式的合理擴展，得到了相應(yīng)的不同形式。具體針對市場微觀結(jié)構(gòu)、事件沖擊以及投資者情緒幾個方面，不僅做了理論分析，而且結(jié)合滬深300樣本股中的平安銀行股票分筆數(shù)據(jù)一一做了實證分析。首先，通過構(gòu)建價格持續(xù)期，在基本ACD模型中引入三個代表市場微觀結(jié)構(gòu)的變量——買賣價差、每筆平均交易量和交易密度，，分別和同時分析了它們與持續(xù)期（久期）之間的關(guān)系。發(fā)現(xiàn)三者與久期均具有顯著的負(fù)相關(guān)關(guān)系，并根據(jù)微觀結(jié)構(gòu)理論對此進行了解釋，認(rèn)為這是由于信息交易者的存在導(dǎo)致，而非流動性造成。其次，通過構(gòu)建交易量持續(xù)期，在基本ACD模型中考慮了非對稱效應(yīng)，分析了事件沖擊對投資者行為的影響。發(fā)現(xiàn)在預(yù)期事件沖擊下，非對稱效應(yīng)并不大，但在非預(yù)期事件（以2013年8月16日光大事件為例）沖擊下，非對稱效應(yīng)則較大，據(jù)此得到了投資者行為針對事件沖擊的前后反應(yīng)過程。最后，通過對衡量投資者情緒指標(biāo)的分析，選擇了換手率作為分筆數(shù)據(jù)中代表投資者情緒的變量，并且買賣方向分別代表了投資者的樂觀和悲觀情緒，實證發(fā)現(xiàn)，無論是收益率，還是波動性，都與投資者情緒呈現(xiàn)正相關(guān)關(guān)系。 通過理論及實證研究，進而提出一些對策建議，即投資者和政府通過對持續(xù)期的觀察，在面對不同大小的久期時，也即面臨不同大小的風(fēng)險時，應(yīng)該改變交易頻率，并且采取措施規(guī)避風(fēng)險。另外，為了有效解決市場流動性問題，減少甚至消除信息交易帶來的噪聲影響，市場監(jiān)管者應(yīng)該著力改善市場機制，讓信息更為透明，有效解決信息不對稱問題，唯有這樣，才能使我國證券市場更好更快地發(fā)展。
[Abstract]:With the rapid development of the securities market and information technology, data processing technology, financial data from the original month, week and day data gradually fine to hours, minutes or even seconds of data,The acquisition of UHF data is very important for our financial research, especially in the era of behavioral finance.Behavioral finance combines psychology with finance. It is believed that investors' decisions are not only restricted by the objective market, but also influenced by the subjective aspects from themselves or the surrounding groups, so the investors will show various anomalies.In the securities market, they are reflected in various visions, and the explanations of these anomalies emerge endlessly.In this paper, based on the theory of behavioral finance, the autoregressive conditional duration model (ACD model) suitable for ultra-high frequency data is applied to the focused investor behavior. Through the reasonable expansion of its basic form, the corresponding different forms are obtained.Aiming at the market microstructure, event impact and investor sentiment, this paper not only makes a theoretical analysis, but also makes an empirical analysis based on the data of Ping an Bank in Shanghai and Shenzhen 300 sample stocks.First of all, by constructing the price duration, we introduce into the basic ACD model the variation of the market microstructure of the three represents-the price spread, the average transaction volume and the transaction density per transaction.The relationship between them and duration (duration) was analyzed separately and simultaneously.It is found that there is a significant negative correlation between the three factors and duration, which is explained by the theory of microstructure, which is due to the existence of information traders rather than liquidity.Secondly, the asymmetric effect is considered in the basic ACD model, and the impact of event shock on investor behavior is analyzed by constructing the duration of trading volume.It is found that the asymmetric effect is not large under the shock of the expected event, but the asymmetric effect is larger under the shock of the unexpected event (as in the case of the Everbright event on August 16, 2013).Based on this, the reaction process of investor behavior in response to event shock is obtained.Finally, through the analysis of the measure of investor sentiment, the paper selects turnover rate as the variable representing investor sentiment in the split data, and the direction of trading represents the optimism and pessimism of investors respectively.Both yield and volatility are positively correlated with investor sentiment.Through theoretical and empirical research, this paper puts forward some countermeasures and suggestions, that is, investors and governments should change the trading frequency when facing different length of time, that is, risk of different size, by observing the duration of the period.And take measures to avoid risk.In addition, in order to effectively solve the market liquidity problem and reduce or even eliminate the impact of noise caused by information transactions, market regulators should focus on improving the market mechanism, making information more transparent, and effectively solving the problem of information asymmetry. Only in this way,Only then can our country's securities market develop better and faster.
【學(xué)位授予單位】：首都經(jīng)濟貿(mào)易大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2014
【分類號】：F224;F832.51

【引證文獻】

相關(guān)博士學(xué)位論文 前2條

1 王芳;基于市場微觀結(jié)構(gòu)噪聲和跳躍的金融高頻數(shù)據(jù)波動研究[D];西南財經(jīng)大學(xué);2011年

2 李靜;基于行為金融學(xué)的股票市場投資者行為研究[D];中國社會科學(xué)院研究生院;2012年

本文編號：1745208