基于股票价格波动问题的消除系统风险的投资组合对策研究

发布时间：2018-12-21 07:13

【摘要】：在我国股票市场，股价常常在一定时期内发生剧烈波动，给个人投资者带来不可预料的损失。Markowitz投资组合理论奠定了现代投资组合理论的基础，然而投资组合只能有效降低非系统风险，对系统风险的规避却无能为力。本论文以中小企业板为研究对象，对股票价格波动过程中低点、高点的出现及出现的规律进行了系统分析和深入研究。首先在对Markowitz投资组合理论和股票价格波动理论的研究基础上，以不同时长股票阶段成本作为参考值，在前提条件约束下，设计了一系列股票价格波动过程中阶段周期低点、高点回归预测模型。然后采用点估计、区间估计等对符合采样条件的样本进行检验和比较，验证不同模型对价格极值进行预测的真实性并得到最优模型。最后应用该模型进行股票投资的模拟仿真操作，，对股票选择和投资组合管理进行对策研究，并分析了这种操作对系统风险的规避能力。在投资组合基础上应用该模型进行股票处理的对策研究，对个人投资者有很好的指导意义。本论文的主要研究内容概括如下： 1．考虑到Markowitz投资组合理论在我国的实用性和它在系统风险降低方面的限制性，提出了基于股票价格波动问题的阶段周期低点、高点回归模型。 2．以中小企业板为背景，设计六种基于不同解释变量的阶段周期回归预测分析模型。通过t检验、F检验、拟合优度分析和残差分析等各种统计检验初步确定有效模型，然后针对多重共线性问题对阶段周期多元回归预测模型进行修正。 3．应用不同预测方式，使用历史数据对不同模型有效性和精确性进行验证和分析，并得出修正后的阶段周期多元回归预测模型拟合效果最优的结论。 4．针对最优的阶段周期回归预测模型，提出三种不同模式的投资操作来进行投资组合的实证研究，包括基于阶段周期回归预测模型的投资、不遵循建议的无作为投资和理想化的指数化投资。实证研究结果显示，利用阶段周期低点、高点回归预测模型进行选股和投资操作的投资组合，收益率远高于其他模式，并且能有效地规避系统风险。在基于阶段周期回归预测模型的操作过程中，对股票的选购和抛售提出对策建议。
[Abstract]:In the stock market of our country, the stock price often fluctuates violently in a certain period, which brings unexpected losses to individual investors. Markowitz portfolio theory has laid the foundation of modern portfolio theory. However, the portfolio can effectively reduce the non-system risk, but can not avoid the system risk. This paper takes the SME board as the research object, carries on the systematic analysis and the thorough research to the stock price fluctuation process low point, the high point appearance and the appearance rule. Firstly, based on the research of Markowitz portfolio theory and stock price fluctuation theory, taking the stage cost of stock with different time and length as reference value, a series of periodic low points in the process of stock price fluctuation are designed under the constraints of preconditions. High point regression prediction model. Then point estimation and interval estimation are used to test and compare the samples that meet the sampling conditions to verify the authenticity of price extremum prediction by different models and to obtain the optimal model. Finally, the model is used to simulate and simulate the stock investment, and the countermeasures of stock selection and portfolio management are studied, and the ability of this operation to avoid the system risk is analyzed. On the basis of investment portfolio, the research on the countermeasures of stock processing based on this model has a good guiding significance for individual investors. The main research contents of this thesis are summarized as follows: 1. Considering the practicability of Markowitz portfolio theory in our country and its limitation in reducing system risk, a phase cycle low based on stock price volatility is proposed. High point regression model. 2. Taking the SME board as the background, six models of stage cycle regression analysis based on different explanatory variables are designed. Through t test, F test, goodness of fit analysis, residual analysis and other statistical tests, the effective model is preliminarily determined, and then the stage periodic multivariate regression prediction model is modified for multi-multiple collinear problems. 3. The validity and accuracy of different models are verified and analyzed by using different forecasting methods and historical data, and the conclusion that the fitting effect of the modified multivariate regression prediction model is optimal is obtained. 4. In view of the optimal stage cycle regression forecasting model, three different investment operations are proposed to carry out portfolio empirical research, including investment based on the stage cycle regression prediction model. Do not follow recommendations for inaction and idealized indexed investments. The empirical results show that the portfolio of stock selection and investment operation by using stage cycle low and high point regression forecasting model is much higher than other models and can effectively avoid systemic risk. In the course of the operation of the forecasting model based on the stage cycle regression, the paper puts forward some countermeasures and suggestions for the stock selection and selling.
【学位授予单位】：天津理工大学
【学位级别】：硕士
【学位授予年份】：2012
【分类号】：F832.51;F224

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