本文選題：內(nèi)幕交易　切入點：資本市場　出處：《浙江大學》2017年碩士論文　論文類型：學位論文

【摘要】：內(nèi)幕交易這個話題一直都很熱門,近年來問題也層出不窮。內(nèi)幕交易行為破壞了金融市場和資本市場的秩序,嚴重影響了廣大投資者的應(yīng)有權(quán)益。中國證監(jiān)會正在加大力度,力求打擊內(nèi)幕信息交易、關(guān)聯(lián)方關(guān)聯(lián)交易以及市場操縱等違法違規(guī)的行為。我國的資本市場還不夠完善,法律法規(guī)等配套措施還跟不上金融創(chuàng)新的速度。各種內(nèi)幕交易違規(guī)行為采用了種種高明的手段掩蓋過去,證監(jiān)會就如何監(jiān)管,如何有效的監(jiān)控這些違法違規(guī)行為,一直不停的在投入。近年來,大數(shù)據(jù)系統(tǒng)已經(jīng)運用到了內(nèi)幕交易監(jiān)管上來。這些手段能夠提供比傳統(tǒng)手段更為有效的監(jiān)管,可以挖掘出無法直接觀察的內(nèi)幕交易行為。但是這些措施還不夠。研究內(nèi)幕交易行為仍然有十分重要的意義。以往的理論研究往往注重定性方面,運用PIN模型(知情者交易概率模型)對內(nèi)幕交易進行研究也是近年來才增加起來的,相關(guān)的理論還需要改進和完善。本文考察梳理了內(nèi)幕交易研究的相關(guān)文獻綜述,發(fā)現(xiàn)PIN模型在研究我國的內(nèi)幕交易案件的時候,還有所缺陷。因此筆者提出了修正PIN模型。本文選取了證監(jiān)會截止到2015年6月30日為止的37個內(nèi)幕交易案件樣本數(shù)據(jù)。發(fā)現(xiàn)傳統(tǒng)的PIN模型,內(nèi)幕交易期間的PIN值雖然有所上升,但是在統(tǒng)計上不顯著,傳統(tǒng)的PIN模型對內(nèi)幕交易的識別效果不佳。這可能是因為有些內(nèi)幕案件的內(nèi)幕交易時間過短,傳統(tǒng)的PIN模型無法判斷識別。本文提出采用修正PIN模型對內(nèi)幕交易行為進行檢驗,與傳統(tǒng)PIN模型相比,修正PIN模型采用連續(xù)變化的時間段來測量內(nèi)幕交易期間內(nèi)的PIN指標變化,采用此方法可以更有效的觀察到短期內(nèi)幕交易的信息不對稱的程度。統(tǒng)計結(jié)果表明內(nèi)幕交易期修正PIN值出現(xiàn)峰值,在公告日之后修正PIN指標有所下降,這解釋了內(nèi)幕交易期間顯著上升的信息不對稱程度。與傳統(tǒng)的PIN模型相比,修正的PIN模型在統(tǒng)計上是顯著的。通過傳統(tǒng)PIN模型和修正PIN模型的對比,驗證了修正PIN模型是更有效的,這也是本文對內(nèi)幕交易研究提供了改進和補充。
[Abstract]:The subject of insider trading has been very popular in recent years, problems also emerge in an endless stream. Insider trading undermined the financial market and the capital market order, seriously affecting the rights and interests of investors. Chinese Commission is to increase efforts to combat insider trading information, related transactions and related party market manipulation and other illegal behavior. China's capital market is still not perfect, laws and regulations also keep up with the pace of financial innovation. All kinds of violations of insider trading using all kinds of clever means to cover up the past, the Commission on how to supervise the illegal behavior, how to effectively control these illegal, and kept pouring in. In recent years, big data system has applied to the insider trading regulation. These methods can provide more effective supervision than traditional means, you can dig out can not be directly observed within the Insider trading behavior. But these measures are not enough. The study of insider trading is still very important. Previous researches often focus on qualitative aspect, using the PIN model (insider trading probability model) research in recent years is also added up to insider trading, related theories need to be improved and perfected. This paper examines combing the relevant literature review on insider trading, found that when PIN model in our research on insider trading cases, there are defects. Therefore the author puts forward the modified PIN model. This paper selects the Commission by June 30, 2015 37 insider trading cases found the sample data. The traditional PIN model, insider trading period although the PIN value increased, but not statistically significant, recognition effect of the traditional PIN model of insider trading is poor. This may be because some insider case The insider trading time is too short, the traditional PIN model can not identify. In this paper, the modified PIN model to test the behavior of insider trading, compared with the traditional PIN model, PIN index time of the modified PIN model with continuous variation to measure the insider trading during the period of change, this method can be used more effectively to observe the information asymmetry of short-term insider trading level. The statistical results show that insider trading period correction value of PIN peak, the modified PIN index declined after the announcement date, which explains the degree of information asymmetry increased significantly during insider trading. Compared with the traditional PIN model, modified PIN model is significant in statistics by comparison. The traditional PIN model and modified PIN model, verify the modified PIN model is more effective, which is provided in this paper the improvement and supplement of insider trading.

【學位授予單位】：浙江大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：F832.51

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本文編號：1572039