基于改进Black-Litterman模型的证券资产配置研究

发布时间：2018-02-16 13:55

  本文关键词： 投资组合 Black-litterman模型 Bootstrap方法 BP神经网络　出处：《大连理工大学》2013年硕士论文　论文类型：学位论文

【摘要】：我国证券投资基金行业十五年来的发展令人瞩目,然而最近几年,受到次贷危机和欧债危机的影响,证券市场产生了大幅波动。在风险增加的情况下,对证券投资组合进行研究成为对基金管理的必然要求。有关研究表明,资产的有效配置对投资业绩的贡献率高达93.6%。近年来,国内基金行业开始对数量化资产配置模型展开研究,这些研究大多是吸收了国外的数量化模型,但中国市场毕竟有别于国外市场,因此,有必要探索改进国外成熟的数量化模型,并应用于中国证券市场的方法。 Black-litterman模型是由高盛公司提出的,该模型从诞生之初就被实际应用于基金投资决策中,经过多年的发展已经得到广泛的认可。本文首先对经典的均值方差理论作了简单的回顾,然后对原始Black-litterman模型中各个复杂的输入参数作了详细的论述,并且使用了Bootstrap方法和神经网络对原模型做了改进,建立了新的ANNs-BLR模型。Bootstrap方法能够很好地修正原模型在计算市场均衡收益率时产生的误差；神经网络模型能够很好地捕捉到股票市场复杂的波动规律,以该模型的预测结果替代原模型中投资者的主观观点,可以有效的提升模型绩效。 本文选取了剔除数据不全股票后的上证50指数的41只权重股作为样本。在采用了误差修正模型,并使用BP神经网络的估计量替代模型观点收益向量后,得到了下一期的模型收益和资产配置结果。在第一部分实证研究中,结果表明,ANNs-BLR模型无论是在有无卖空限制、有无配置权重上限的情况下,配置的稳定性都优于均值方差模型,模型收益率和夏普比率也都高于均值方差模型。在第二部分实证中,本文针对中国股票市场非流通股票普遍存在的情况,对分别采用总市值权重、流通市值权重以及经过误差调整的模型配置绩效进行了比较,结果显示流通市值权重更加适合该模型,能够取得更好的投资绩效；而且误差调整过程也能够有效改善模型绩效。
[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.
【学位授予单位】：大连理工大学
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：F832.51;F224

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