本文關(guān)鍵詞：基于雙重期望效用的投資組合模型及其智能算法研究　出處：《寧夏大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 投資組合 雙重期望效用 教與學(xué)算法 混沌鳥(niǎo)群算法 粒子群算法

【摘要】：一般的效用函數(shù)沒(méi)有考慮歷史事件在將來(lái)出現(xiàn)的可能性差異,即認(rèn)為在歷史中發(fā)生的概率也會(huì)以相同的概率在未來(lái)時(shí)間發(fā)生,為了避免這種很強(qiáng)的假設(shè),本文研究了基于雙重期望效用下的投資組合模型,并應(yīng)用人工智能算法對(duì)其求解.因此,基于雙重期望效用理論的效用函數(shù)來(lái)度量投資行為的效用思想下,本文的主要工作如下:1根據(jù)實(shí)際金融市場(chǎng)的需求,以證券的收益率服從正態(tài)分布作為前提,引入可投資數(shù)目最大上限的約束,建立相應(yīng)的多目標(biāo)投資組合優(yōu)化模型,采用罰函數(shù)法將多目標(biāo)投資組合模型轉(zhuǎn)化為單目標(biāo)模型,同時(shí)構(gòu)造出符合該模型的教與學(xué)算法對(duì)其求解,并選取10支股票進(jìn)行仿真實(shí)驗(yàn).2引入限制性賣空的約束條件,建立了投資組合優(yōu)化模型,同時(shí)引用權(quán)重系數(shù)作為風(fēng)險(xiǎn)厭惡因子,使得模型更加符合投資者的決策心理,從而確保了決策方案的可行性.此外,本文設(shè)計(jì)了適合模型的混沌鳥(niǎo)群算法求解模型,并與粒子群、教與學(xué)算法作了比較,得到了當(dāng)風(fēng)險(xiǎn)厭惡因子A取不同值時(shí)混沌鳥(niǎo)群算法具有更好的優(yōu)化效果,同時(shí)為決策者們提供了更佳的投資方案.3考慮到我國(guó)真實(shí)的證券市場(chǎng)存在一些摩擦因素,會(huì)受到最小交易量及交易費(fèi)用的約束,同時(shí)站在投資者角度,必須以分散風(fēng)險(xiǎn)為一目標(biāo),設(shè)置了投資上限限制,使得本文所給出的模型更貼近金融市場(chǎng)中投資者們的決策行為,這一模型方案合理、可行,使投資者多一種選擇方案;此外,設(shè)計(jì)了求解模型的粒子群算法,得到符合理論依據(jù)的數(shù)值結(jié)果,為投資者們提供一種最優(yōu)選擇.
[Abstract]:The general utility function does not take into account the possibility of historical events in the future, that is, the probability of occurrence in history will also occur in the future with the same probability, in order to avoid this strong hypothesis. In this paper, the portfolio model based on dual expected utility is studied and solved by artificial intelligence algorithm. Therefore, the utility function based on dual expected utility theory is used to measure the utility of investment behavior. The main work of this paper is as follows: 1. According to the demand of the actual financial market, taking the yield of securities as the premise of normal distribution, we introduce the constraint of the maximum upper limit of the number of investments that can be invested. The corresponding multi-objective portfolio optimization model is established and the penalty function method is used to transform the multi-objective portfolio model into a single-objective model. At the same time, a teaching and learning algorithm is constructed to solve the model. At the same time, 10 stocks are selected to carry on the simulation experiment. 2. The restrictive short selling constraint condition is introduced, and the portfolio optimization model is established. At the same time, the weight coefficient is used as the risk aversion factor. Make the model more in line with investors' decision-making psychology, so as to ensure the feasibility of the decision-making scheme. In addition, this paper designed a model suitable for the model of chaotic bird swarm algorithm to solve the model, and particle swarm optimization. Compared with the learning algorithm, the chaotic bird swarm algorithm has better optimization effect when the risk aversion factor A takes different values. At the same time, it provides policy makers with a better investment scheme .3 considering that there are some frictional factors in the real securities market in China, it will be constrained by the minimum transaction volume and transaction costs, and at the same time, it will stand in the perspective of investors. In order to make the model more close to the decision behavior of investors in the financial market, we must set the upper limit of investment with the goal of dispersing risk. This model is reasonable and feasible. One more option for investors; In addition, a particle swarm optimization algorithm is designed to solve the model, and the numerical results are obtained according to the theoretical basis, which provides an optimal choice for investors.
【學(xué)位授予單位】：寧夏大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：F832.51;TP18

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