本文關(guān)鍵詞： 投資組合 Black-litterman模型 Bootstrap方法 BP神經(jīng)網(wǎng)絡(luò)　出處：《大連理工大學(xué)》2013年碩士論文　論文類型：學(xué)位論文

【摘要】：我國(guó)證券投資基金行業(yè)十五年來(lái)的發(fā)展令人矚目,然而最近幾年,受到次貸危機(jī)和歐債危機(jī)的影響,證券市場(chǎng)產(chǎn)生了大幅波動(dòng)。在風(fēng)險(xiǎn)增加的情況下,對(duì)證券投資組合進(jìn)行研究成為對(duì)基金管理的必然要求。有關(guān)研究表明,資產(chǎn)的有效配置對(duì)投資業(yè)績(jī)的貢獻(xiàn)率高達(dá)93.6%。近年來(lái),國(guó)內(nèi)基金行業(yè)開(kāi)始對(duì)數(shù)量化資產(chǎn)配置模型展開(kāi)研究,這些研究大多是吸收了國(guó)外的數(shù)量化模型,但中國(guó)市場(chǎng)畢竟有別于國(guó)外市場(chǎng),因此,有必要探索改進(jìn)國(guó)外成熟的數(shù)量化模型,并應(yīng)用于中國(guó)證券市場(chǎng)的方法。 Black-litterman模型是由高盛公司提出的,該模型從誕生之初就被實(shí)際應(yīng)用于基金投資決策中,經(jīng)過(guò)多年的發(fā)展已經(jīng)得到廣泛的認(rèn)可。本文首先對(duì)經(jīng)典的均值方差理論作了簡(jiǎn)單的回顧,然后對(duì)原始Black-litterman模型中各個(gè)復(fù)雜的輸入?yún)?shù)作了詳細(xì)的論述,并且使用了Bootstrap方法和神經(jīng)網(wǎng)絡(luò)對(duì)原模型做了改進(jìn),建立了新的ANNs-BLR模型。Bootstrap方法能夠很好地修正原模型在計(jì)算市場(chǎng)均衡收益率時(shí)產(chǎn)生的誤差；神經(jīng)網(wǎng)絡(luò)模型能夠很好地捕捉到股票市場(chǎng)復(fù)雜的波動(dòng)規(guī)律,以該模型的預(yù)測(cè)結(jié)果替代原模型中投資者的主觀觀點(diǎn),可以有效的提升模型績(jī)效。 本文選取了剔除數(shù)據(jù)不全股票后的上證50指數(shù)的41只權(quán)重股作為樣本。在采用了誤差修正模型,并使用BP神經(jīng)網(wǎng)絡(luò)的估計(jì)量替代模型觀點(diǎn)收益向量后,得到了下一期的模型收益和資產(chǎn)配置結(jié)果。在第一部分實(shí)證研究中,結(jié)果表明,ANNs-BLR模型無(wú)論是在有無(wú)賣(mài)空限制、有無(wú)配置權(quán)重上限的情況下,配置的穩(wěn)定性都優(yōu)于均值方差模型,模型收益率和夏普比率也都高于均值方差模型。在第二部分實(shí)證中,本文針對(duì)中國(guó)股票市場(chǎng)非流通股票普遍存在的情況,對(duì)分別采用總市值權(quán)重、流通市值權(quán)重以及經(jīng)過(guò)誤差調(diào)整的模型配置績(jī)效進(jìn)行了比較,結(jié)果顯示流通市值權(quán)重更加適合該模型,能夠取得更好的投資績(jī)效；而且誤差調(diào)整過(guò)程也能夠有效改善模型績(jī)效。
[Abstract]:The development of China's securities investment fund industry in the past 15 years has attracted great attention. However, in recent years, affected by the subprime mortgage crisis and the European debt crisis, the securities market has produced large fluctuations. Research on the portfolio of securities has become an inevitable requirement for fund management. Related studies show that the contribution rate of effective asset allocation to investment performance is as high as 93.6. In recent years, the domestic fund industry has begun to study the quantitative asset allocation model. Most of these studies have absorbed the quantitative models of foreign countries, but the Chinese market is different from foreign markets after all. Therefore, it is necessary to explore and improve the mature quantitative models abroad and apply them to the Chinese securities market. The Black-litterman model was put forward by Goldman Sachs, which was applied to fund investment decision from the beginning of its birth. After years of development, it has been widely accepted. In this paper, the classical mean-variance theory is reviewed briefly. Then the complicated input parameters in the original Black-litterman model are discussed in detail, and the Bootstrap method and neural network are used to improve the original model. A new ANNs-BLR model. Bootstrap method is established to correct the error of the original model in calculating the market equilibrium return rate, and the neural network model can capture the complex volatility law of the stock market. The prediction results of the model can effectively improve the performance of the model by replacing the subjective point of view of the investors in the original model. In this paper, 41 weight shares of Shanghai 50 index after eliminating incomplete data are selected as samples. After adopting the error correction model and using the BP neural network estimate to replace the model income vector, In the first part of the empirical study, the results show that the stability of the model is superior to that of the mean variance model, regardless of whether there is a short selling limit or not, with or without the upper limit of the allocation weight, and the results of the first part of the empirical study show that the stability of the model is better than that of the mean variance model. The return rate and Sharp ratio are also higher than the mean variance model. In the second part of the empirical analysis, this paper uses the total market value weight to deal with the prevailing situation of non-circulating stocks in Chinese stock market. The results show that the weight of circulation market value is more suitable for the model and can achieve better investment performance, and the error adjustment process can also effectively improve the model performance.
【學(xué)位授予單位】：大連理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：F832.51;F224

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