本文選題：市場波動 + 復(fù)雜網(wǎng)絡(luò)�。� 參考：《南京航空航天大學(xué)》2012年博士論文

【摘要】：證券市場無疑是一個復(fù)雜系統(tǒng)，至少包括三個基本構(gòu)成成份：交易者（Actors）、股票（Stocks），以及影響投資者交易行為和股價波動的信息（Event或Rumors）。這三者之間不是相互孤立的，而是相互作用、相互影響，使證券市場表現(xiàn)出復(fù)雜的運(yùn)行行為。 交易者、股票以及市場信息之間存在著錯綜復(fù)雜的聯(lián)系，從而構(gòu)成了各種各樣的網(wǎng)絡(luò)形式。本文構(gòu)建了四種類型的網(wǎng)絡(luò)分別研究傳聞在市場中的擴(kuò)散規(guī)律和羊群行為的生成機(jī)理：交易者網(wǎng)絡(luò)、股票相似性網(wǎng)絡(luò)、交易者-股票的二分網(wǎng)絡(luò)以及交易行為網(wǎng)絡(luò)。這些網(wǎng)絡(luò)都是復(fù)雜的，因此本文采用復(fù)雜網(wǎng)絡(luò)的理論與方法，不但分析網(wǎng)絡(luò)的拓?fù)浣Y(jié)構(gòu)，而且分析網(wǎng)絡(luò)上的動態(tài)演化行為。 論文首先采用系統(tǒng)動力學(xué)模型分析了證券市場波動性影響因素，以及證券市場波動性與傳聞擴(kuò)散、羊群行為的關(guān)系；然后，構(gòu)建了基于交易行為的證券市場波動模型，并分析了賣空限制交易和事件驅(qū)動機(jī)制對于市場波動性的影響；其次，通過構(gòu)建交易者網(wǎng)絡(luò)分析市場傳聞的博弈擴(kuò)散過程與深度，通過構(gòu)建股票相似性網(wǎng)絡(luò)分析傳聞在市場中的擴(kuò)散分布；再次，通過構(gòu)建交易者-股票的二分網(wǎng)絡(luò)模型，分別采用理論推導(dǎo)和仿真實驗的方法分析了市場波動性以及羊群行為；通過構(gòu)建以股票為節(jié)點(diǎn)的交易行為網(wǎng)絡(luò)，分別采用理論推導(dǎo)和仿真實驗的方法分析了羊群行為的生成機(jī)制；最后，給出了研究結(jié)論與進(jìn)一步研究展望。 全文共分為5個部分。第一部分給出了論文的研究背景、研究問題、以及研究的目的和意義，對證券市場的信息不對稱性、投資者羊群行為、以及復(fù)雜網(wǎng)絡(luò)的理論與方法等的研究現(xiàn)狀進(jìn)行了綜述，確定了本文的研究方向與研究重點(diǎn)；最后，給出了本文的研究方法、技術(shù)路線、體系結(jié)構(gòu)和創(chuàng)新點(diǎn)。 第二部分采用系統(tǒng)動力學(xué)方法分析了證券市場波動性的影響因素。首先，對證券市場波動性的影響因素進(jìn)行了分類，共分為五種類型的影響因素：宏觀基本面、中觀基本面、微觀基本面、技術(shù)面和隱形面；然后，構(gòu)建了證券市場波動性影響因子系統(tǒng)的動力學(xué)模型，通過Vensim仿真發(fā)現(xiàn)，證券市場波動性直接受到技術(shù)面和隱形面因素的影響，與其同步變化，而三類基本面因素則稍微滯后于波動性的變化；最后，構(gòu)建了證券市場波動性與傳聞擴(kuò)散、羊群行為的系統(tǒng)動力學(xué)概念模型，通過正負(fù)反饋回路的分析發(fā)現(xiàn)，傳聞擴(kuò)散是各種因素影響波動性的集中體現(xiàn)與重要方式，羊群行為則是市場波動性在交易者群體上涌現(xiàn)出的一種集體行為方式。 第三部分從限制賣空交易和事件驅(qū)動兩個角度分析了證券市場的波動性。對于限制賣空交易的研究發(fā)現(xiàn)，賣空限制降低了了市場效率，增強(qiáng)了波動性；賣空限制強(qiáng)化了單邊市場，降低了市場流動性；賣空限制阻礙了價格發(fā)現(xiàn)，助長了羊群效應(yīng)。對于事件驅(qū)動機(jī)制的研究發(fā)現(xiàn)，股票價格波動率的變化與作用強(qiáng)度成正比，與當(dāng)前價格波動率成正比，與未來增值空間成正比。在一定時間內(nèi)股票價格走勢曲線在事件的驅(qū)動下近似服從于S-曲線。在投資資金約束下，股票價格波動率變化曲線從單調(diào)遞增的S-曲線變?yōu)榫哂凶畲笾档膯畏迩�；在市場走勢與事件驅(qū)動方向相同的情況下，市場指數(shù)將放大事件驅(qū)動的影響，加速股票價格波動率的上升；在市場走勢與事件驅(qū)動方向相反的情況下，市場指數(shù)將打壓事件驅(qū)動的影響，使股票價格波動率變化曲線成為具有最大值的單峰曲線。最后，本文提出了一個“矩形窗口”的方法指導(dǎo)投資者判斷潛在事件對股票價格波動率的影響。 第四部分從交易者網(wǎng)絡(luò)博弈和金融相似性網(wǎng)絡(luò)等兩個角度分析了傳聞在市場中的擴(kuò)散規(guī)律及其影響。對于傳聞的博弈擴(kuò)散研究發(fā)現(xiàn)，在Nash均衡點(diǎn)，知情者采取擴(kuò)散策略的概率與不知情者的接受成本和拒絕代價之差成正比；不知情者采取接受策略的概率不但與知情者的封鎖成本成正比，而且與網(wǎng)絡(luò)中不知情者的平均度成正比；從轉(zhuǎn)移矩陣可知，市場傳聞擴(kuò)散的馬爾科夫鏈?zhǔn)且粋�吸收鏈，從而市場傳聞的擴(kuò)散狀態(tài)將最終變?yōu)榉怄i或拒絕狀態(tài)，并且市場傳聞擴(kuò)散的平均步數(shù)與網(wǎng)絡(luò)的平均鄰接不知情者的數(shù)目成正比�；诮鹑谙嗨菩跃W(wǎng)絡(luò)的傳聞擴(kuò)散研究發(fā)現(xiàn)，由股票時間序列的相關(guān)系數(shù)矩陣，，得到了兩類網(wǎng)絡(luò)：完全加權(quán)相似性網(wǎng)絡(luò)和正負(fù)加權(quán)相似性網(wǎng)絡(luò)。完全加權(quán)相似性網(wǎng)絡(luò)具有全連通的拓?fù)浣Y(jié)構(gòu)，是一個規(guī)則網(wǎng)絡(luò)。其節(jié)點(diǎn)連通度分布屬于單點(diǎn)分布，而節(jié)點(diǎn)連通度相關(guān)的條件概率等價于從所有邊中隨機(jī)選取一條的概率；正負(fù)加權(quán)相似性網(wǎng)絡(luò)是無標(biāo)度網(wǎng)絡(luò)，其拓?fù)浣Y(jié)構(gòu)是不均勻的。將傳聞擴(kuò)散模型應(yīng)用于完全加權(quán)相似性網(wǎng)絡(luò)，其結(jié)果恰好與經(jīng)典的傳染病擴(kuò)散模型SI相同；將傳聞擴(kuò)散模型應(yīng)用于正負(fù)相似性網(wǎng)絡(luò)，其結(jié)果與網(wǎng)絡(luò)的復(fù)雜性密切相關(guān)。 第五部分從股票-投資者的二分網(wǎng)絡(luò)和交易行為網(wǎng)絡(luò)研究了羊群行為的生成機(jī)理。在二分網(wǎng)絡(luò)上研究發(fā)現(xiàn)，隨著交易者之間學(xué)習(xí)概率的不同，羊群行為表現(xiàn)為交易者持股情況的三種分布：當(dāng)交易者之間的學(xué)習(xí)概率p p很大，交易者持股近似服從脈沖分布，股票市場表現(xiàn)出很強(qiáng)的羊群行為；當(dāng)交易者之間的學(xué)習(xí)概率p p較小，交易者的持股近似服從二項分布，股票市場表現(xiàn)出非常弱的羊群行為；當(dāng)交易者之間的學(xué)習(xí)概率p p處于一個適當(dāng)?shù)姆秶鷷r，交易者的持股服從不同的具有指數(shù)截斷的冪律分布，股票市場表現(xiàn)出不同程度的羊群行為。在交易者網(wǎng)絡(luò)上研究發(fā)現(xiàn)，當(dāng)交易者之間的學(xué)習(xí)模仿概率滿足01時，市場中的羊群行為服從冪指數(shù)為3的冪律分布；當(dāng)交易者之間的學(xué)習(xí)模仿概率滿足0時，市場中交易行為分布服從指數(shù)分布，不存在羊群行為。 最后，總結(jié)了全文的研究工作，并就進(jìn)一步研究的方向進(jìn)行了簡要的討論。
[Abstract]:The securities market is undoubtedly a complex system, at least three basic components: the Actors, the Stocks, and the information (Event or Rumors) affecting the trading behavior and the volatility of the stock. These three are not isolated from each other, but interact and interact with each other to make the securities market a complex running line. Yes.
There are intricate links between traders, stock and market information, which constitute a variety of network forms. This paper constructs four types of networks to study the spread of rumours in the market and the generation mechanism of herd behavior: the trader network, the share ticket similarity network, the trader and the stock's two point network. And the transaction behavior of the network. These networks are complex, so this paper uses the theory and method of complex network, not only the analysis of the topology of the network, and the analysis of dynamic evolution behavior on the network.
Firstly, the paper analyzes the influence factors of volatility in securities market, and the relationship between the volatility of securities market and the rumor diffusion and the herding behavior. Then, the volatility model of securities market based on transaction behavior is constructed, and the effect of short selling limited transaction and part driving mechanism on the market volatility is analyzed. Second, we analyze the diffusion process and depth of the game by constructing the trader network and analyze the spread distribution in the market by constructing the stock similarity network. Thirdly, the market volatility and the herd line are analyzed by the method of theoretical deduction and simulation experiment by constructing the two point network model of the trader and stock. For; by building a stock for the network transaction behavior of nodes, methods, theoretical derivation and simulation analysis of the formation mechanism of the herd behavior; finally, the prospect of further research and conclusions are given.
The full text is divided into 5 parts. The first part gives the research background, the research problem, the purpose and the significance of the research, the information asymmetry of the securities market, the herd behavior of the investor, the theory and method of the complex network, and determine the research direction and the key point of this paper. Finally, The research method, technical route, system structure and innovation point are given.
The second part analyzes the influence factors of the volatility of the securities market by using the system dynamics method. Firstly, it classifies the factors affecting the volatility of the securities market, which are divided into five types of influencing factors: macro fundamentals, meso fundamentals, micro fundamentals, technical and invisible surfaces; then, the volatility shadow of the securities market is constructed. The dynamic model of the sound factor system, through the Vensim simulation, shows that the volatility of the securities market is directly influenced by the technical and invisible factors, and the three basic factors are slightly lagging behind the volatility. Finally, the system dynamics of the volatility of the stock market and the rumor diffusion and the herding behavior are constructed. Read model, through the analysis of positive and negative feedback loop that rumors diffusion is the various factors affecting embodied volatility and an important way of herd behavior is a kind of collective behavior of the emergence of market volatility in the group of traders.
The third part analyzes the volatility of the stock market from two aspects of limited short selling and event driven. For the study of short selling, the short selling limit reduces the market efficiency and increases the volatility; short selling limit strengthens the unilateral market and reduces the liquidity of the market; short selling limit hinders the price discovery and encourages sheep. Group effect. The study of event driven mechanism found that the change of stock price volatility is proportional to the intensity of action, proportional to the current price volatility, and proportional to the future value added space. In a certain time, the trend curve of stock price is approximately subject to the S- curve driven by events. Under the constraint of investment funds, the stock price wave When the market trend is the same as the event driven direction, the market index will enlarge the impact of the event driven and accelerate the rise of the volatility of the stock price. In the case of the opposite trend of the market trend and the event driving direction, the market index will press things under the situation of the same market trend and the event driven direction. The influence of driving, the volatility of the stock price curve has become a unimodal curve maximum. Finally, this paper puts forward a method of a rectangular window "to guide the investors to judge the impact of potential events on the rate of stock price volatility.
The fourth part analyzes the spread and influence of hearsay in the market from two angles, such as the network game of traders and the network of financial similarity. In the study of the rumor game diffusion, it is found that in the Nash equilibrium point, the probability of adopting the diffusion strategy by the insider is proportional to the difference between the acceptance of the unaware person and the rejection price; The probability of taking the acceptance strategy is not only proportional to the blocking cost of the insider, but also proportional to the average degree of the unknown person in the network. From the transfer matrix, the Markoff chain of the market rumor diffusion is an absorption chain, and the diffusion state of the market hearsay will eventually become blockade or refusal, and the market rumor is spread. The mean step is proportional to the number of the average neighbor of the network. The study of the rumor diffusion based on the financial similarity network finds that the two types of networks are obtained from the correlation coefficient matrix of the stock time series: the fully weighted similarity network and the positive and negative weighted similarity network. Structure is a regular network. The node connectivity distribution belongs to the single point distribution, and the conditional probability of the node connectivity is equivalent to the probability of random selection from all sides; the positive and negative weighted similarity network is a scale-free network and its topology is not uniform. The spread model is applied to the fully weighted similarity network. As a result, coincided with the classical infectious disease diffusion model SI will be the same; rumor spreading model is applied to the positive and negative similarity network, which results in complexity and network are closely related.
The fifth part studies the generation mechanism of herd behavior from the stock investor's two division network and the transaction behavior network. On the two point network, it is found that with the difference of the learning probability between traders, the herd behavior is the three distribution of the trader's Stock Ownership: the learning probability of the trader is p p, and the trader holds the stock close. Similar to the pulse distribution, the stock market shows a strong herd behavior; when the learning probability of p p between traders is smaller, the stock market shares approximately two distribution, and the stock market shows very weak herding behavior; when the learning probability p p between traders is in a proper range, the stock ownership of the traders is different from that of the traders. A power law distribution with exponential truncation shows a different degree of herding behavior in the stock market. On the trader's network, it is found that when the probability of learning imitation between the traders satisfies 01, the herd behavior in the market obeys a power law distribution of 3. When the learning imitation probability of the trader meets 0, the market transaction is in the market. The behavior distribution obeys exponential distribution, and there is no herding behavior.
Finally, the research work is summarized, and the direction of further research is briefly discussed.
【學(xué)位授予單位】：南京航空航天大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2012
【分類號】：F224;F830.91

【引證文獻(xiàn)】

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