【摘要】：證券市場(chǎng)是一個(gè)復(fù)雜的動(dòng)態(tài)系統(tǒng),經(jīng)濟(jì)全球化、經(jīng)濟(jì)一體化加劇了證券市場(chǎng)的復(fù)雜性和波動(dòng)性。本文把證券收益視為隨機(jī)模糊變量能夠同時(shí)反映證券市場(chǎng)隨機(jī)和模糊的雙重不確定性,另外利用現(xiàn)代行為金融理論的研究成果,考慮投資者真實(shí)的心理偏好,構(gòu)建加權(quán)極大-極小隨機(jī)模糊投資組合模型。為了求解模型在市場(chǎng)存在交易費(fèi)用和最小交易單位情況下投資組合權(quán)重,對(duì)動(dòng)態(tài)鄰居粒子群算法進(jìn)行改進(jìn),提出改進(jìn)的動(dòng)態(tài)鄰居粒子群算法。在我國(guó)的經(jīng)濟(jì)環(huán)境下,利用改進(jìn)動(dòng)態(tài)鄰居粒子群算法對(duì)模型求解并檢驗(yàn)?zāi)Ｐ偷挠行�。論文的研究主要包�?(1)針對(duì)證券市場(chǎng)同時(shí)存在隨機(jī)和模糊雙重不確定性因素,把證券收益視為隨機(jī)模糊變量,構(gòu)建以財(cái)富變化量為基礎(chǔ)的期望收益隸屬度函數(shù)。利用加權(quán)極大-極小算子,同時(shí)考慮投資組合期望收益和目標(biāo)概率滿足投資者期望值的隸屬度,構(gòu)建加權(quán)極大-極小隨機(jī)模糊投資組合模型。利用Markowitz的歷史數(shù)據(jù)研究模型的有效邊界,結(jié)果表明把證券收益視為隨機(jī)模糊變量的投資組合與Markowitz均值-方差投資組合有效邊界不一致。 (2)針對(duì)動(dòng)態(tài)鄰居粒子群算法所存在的不足,對(duì)算法的粒子群初始化方法和動(dòng)態(tài)鄰居的拓?fù)浣Y(jié)構(gòu)進(jìn)行改進(jìn),提出改進(jìn)的動(dòng)態(tài)鄰居粒子群算法。分別針對(duì)無(wú)權(quán)重約束和有權(quán)重約束投資組合對(duì)算法迭代尋優(yōu)能力進(jìn)行檢驗(yàn),結(jié)果表明改進(jìn)動(dòng)態(tài)鄰居粒子群算法能夠有效求解投資組合有效邊界問(wèn)題。 (3)隨機(jī)選取滬深300指數(shù)的50支股票作為研究樣本,對(duì)加權(quán)極大-極小隨機(jī)模糊投資組合模型進(jìn)行實(shí)證檢驗(yàn)。實(shí)證檢驗(yàn)包括兩個(gè)部分。①在市場(chǎng)無(wú)摩擦的環(huán)境下,分別比較加權(quán)極大-極小隨機(jī)模糊投資組合、Markowitz均值-方差投資組合和Vercher模糊投資組合的投資績(jī)效,實(shí)證結(jié)果表明加權(quán)極大-極小隨機(jī)模糊投資組合的投資績(jī)效更優(yōu)。②在市場(chǎng)存在摩擦的環(huán)境下,對(duì)不同風(fēng)險(xiǎn)態(tài)度的投資者設(shè)置不同的投資組合參數(shù),并利用改進(jìn)動(dòng)態(tài)鄰居粒子群算法對(duì)模型求解,結(jié)果表明加權(quán)極大-極小隨機(jī)模糊投資組合模型能夠有效反映不同風(fēng)險(xiǎn)態(tài)度投資者的心理偏好。
[Abstract]:The securities market is a complex dynamic system. Economic globalization and economic integration aggravate the complexity and volatility of the securities market. In this paper, the return of securities can be regarded as a random fuzzy variable which can reflect the double uncertainty of random and fuzzy in the stock market at the same time. In addition, by using the research results of modern behavioral finance theory, the real psychological preference of investors is considered. A weighted maximum-minimum stochastic fuzzy portfolio model is constructed. In order to solve the model with transaction cost and minimum trading unit in the market the dynamic neighbor particle swarm optimization algorithm is improved and an improved dynamic neighbor particle swarm algorithm is proposed. In the economic environment of our country, the improved dynamic neighbor particle swarm optimization algorithm is used to solve the model and verify the validity of the model. The main contents of this paper are as follows: (1) in view of the existence of both random and fuzzy uncertainties in the stock market, the securities return is regarded as a random fuzzy variable, and the expected return membership function based on the amount of wealth change is constructed. A weighted maximum-minimum random fuzzy portfolio model is constructed by using the weighted maximum-minimum operator and considering the membership degree of the expected return and target probability of the portfolio to satisfy the expected value of the investor. Using the historical data of Markowitz to study the effective boundary of the model, the results show that the portfolio which regards the return of securities as a random fuzzy variable is not consistent with the effective boundary of the Markowitz mean-variance portfolio. (2) aiming at the deficiency of the dynamic neighbor particle swarm optimization algorithm, the particle swarm initialization method and the topological structure of the dynamic neighbor particle swarm optimization algorithm are improved, and an improved dynamic neighbor particle swarm optimization algorithm is proposed. The iterative optimization ability of the algorithm is tested for unauthorized and weighted constrained portfolio respectively. The results show that the improved dynamic neighbor particle swarm optimization algorithm can effectively solve the portfolio efficient boundary problem. (3) 50 stocks of Shanghai-Shenzhen 300 index are randomly selected as research samples, and the weighted maximum-minimum stochastic fuzzy portfolio model is empirically tested. The empirical test consists of two parts. 1 under the condition of no friction in the market, we compare the investment performance of weighted maximum-minimum random fuzzy portfolio, Markowitz mean-variance portfolio and Vercher fuzzy portfolio, respectively, and compare the investment performance of weighted maximum-minimum random fuzzy portfolio, Markowitz mean-variance portfolio and Vercher fuzzy portfolio, respectively. The empirical results show that the investment performance of weighted maximum-minimum random fuzzy portfolio is better. 2 under the environment of market friction, different portfolio parameters are set for investors with different risk attitudes. The improved dynamic neighbor particle swarm optimization algorithm is used to solve the model. The results show that the weighted maximum-minimum stochastic fuzzy portfolio model can effectively reflect the psychological preferences of investors with different risk attitudes.
【學(xué)位授予單位】：東北大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：F830.59;F224

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