【摘要】：證券市場是一個復雜的動態(tài)系統(tǒng),經(jīng)濟全球化、經(jīng)濟一體化加劇了證券市場的復雜性和波動性。本文把證券收益視為隨機模糊變量能夠同時反映證券市場隨機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,考慮投資者真實的心理偏好,構建加權極大-極小隨機模糊投資組合模型。為了求解模型在市場存在交易費用和最小交易單位情況下投資組合權重,對動態(tài)鄰居粒子群算法進行改進,提出改進的動態(tài)鄰居粒子群算法。在我國的經(jīng)濟環(huán)境下,利用改進動態(tài)鄰居粒子群算法對模型求解并檢驗模型的有效性。論文的研究主要包括 (1)針對證券市場同時存在隨機和模糊雙重不確定性因素,把證券收益視為隨機模糊變量,構建以財富變化量為基礎的期望收益隸屬度函數(shù)。利用加權極大-極小算子,同時考慮投資組合期望收益和目標概率滿足投資者期望值的隸屬度,構建加權極大-極小隨機模糊投資組合模型。利用Markowitz的歷史數(shù)據(jù)研究模型的有效邊界,結果表明把證券收益視為隨機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)針對動態(tài)鄰居粒子群算法所存在的不足,對算法的粒子群初始化方法和動態(tài)鄰居的拓撲結構進行改進,提出改進的動態(tài)鄰居粒子群算法。分別針對無權重約束和有權重約束投資組合對算法迭代尋優(yōu)能力進行檢驗,結果表明改進動態(tài)鄰居粒子群算法能夠有效求解投資組合有效邊界問題。 (3)隨機選取滬深300指數(shù)的50支股票作為研究樣本,對加權極大-極小隨機模糊投資組合模型進行實證檢驗。實證檢驗包括兩個部分。①在市場無摩擦的環(huán)境下,分別比較加權極大-極小隨機模糊投資組合、Markowitz均值-方差投資組合和Vercher模糊投資組合的投資績效,實證結果表明加權極大-極小隨機模糊投資組合的投資績效更優(yōu)。②在市場存在摩擦的環(huán)境下,對不同風險態(tài)度的投資者設置不同的投資組合參數(shù),并利用改進動態(tài)鄰居粒子群算法對模型求解,結果表明加權極大-極小隨機模糊投資組合模型能夠有效反映不同風險態(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.
【學位授予單位】：東北大學
【學位級別】：碩士
【學位授予年份】：2012
【分類號】：F830.59;F224

【參考文獻】

相關期刊論文 前10條

1 盧峰;高立群;;基于改進粒子群算法的優(yōu)化策略[J];東北大學學報(自然科學版);2011年09期

2 郭文旌,胡奇英;不確定終止時間的多階段最優(yōu)投資組合[J];管理科學學報;2005年02期

3 劉衍民;趙慶禎;隋常玲;邵增珍;;一種基于動態(tài)鄰居和變異因子的粒子群算法[J];控制與決策;2010年07期

4 肖迪;葛啟承;林錦國;程明;;一種雙種群遺傳粒子群算法及在SMB優(yōu)化中的應用[J];南京理工大學學報;2012年01期

5 周燕;劉培玉;趙靜;王乾龍;;基于自適應慣性權重的混沌粒子群算法[J];山東大學學報(理學版);2012年03期

6 李仲飛,汪壽陽;EaR風險度量與動態(tài)投資決策[J];數(shù)量經(jīng)濟技術經(jīng)濟研究;2003年01期

7 陸寶群,周雯;連續(xù)時間下的最佳投資組合和彈性[J];數(shù)學的實踐與認識;2004年06期

8 張海峰;張維;鄒高峰;熊熊;;中國市場條件下前景理論的實證分析[J];西安電子科技大學學報(社會科學版);2011年03期

9 劉黎黎;汪定偉;;復合粒子群算法及其在動態(tài)環(huán)境中的應用[J];系統(tǒng)工程學報;2011年02期

10 彭飛,賈建民,黃登仕;一個基于極大極小風險價值的組合投資模型[J];系統(tǒng)工程理論方法應用;2004年04期

，

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