混合型模糊聚类分析方法及其应用

在动态聚类方法和模糊ISODATA方法的基础上,提出了混合型模糊聚类分析方法.该方法首先利用传统的传递闭包方法得到1个初始分类,并在此基础上提出初始分划矩阵,根据考虑权重因子的模糊ISODATA方法对相关数据进行迭代计算,从而对数据进行有效分类.以股票分类为例对该方法进行实证分析,分析结果表明,应用该方法可以对股票进行有效分类优选.     

例析数学分析习题课中无穷小量结论的应用

借助讲授数学分析教材课后习题的过程中总结出的无穷小量间的等价关系的结论,示范解答若干极限问题,以阐释数学分析课程的习题课对该门课程的教与学都有重要助益.     

中国股市的拓扑结构及其复杂性质研究

在计算中国股市各股票之间的关联函数的基础上,利用最小生成树方法构建了股市的拓扑结构,从而验证了股市板块内部的共振效应,并发现股市的拓扑结构的连通性分布满足幂律关系。为此,本文从自组织临界性的角度研究了股市的复杂性质,其所得到的股市整体结构的分形性,将成为股市复杂系统鲁棒性研究的基础。     

"T+0"和"T+1"制度下投资者交易风险对比

从投资者当日(T)投资所面临的交易风险出发,运用数据客观地对比分析了在“T 0”和“T 1”两种交易制度下带给投资者的交易风险,阐明在保护投资者利益方面两种交易制度的优劣性,通过数据分析,认为“T 1”交易制度并不能更好地保护下跌行情中从事股票交易的投资者利益．甚至,在上海证券市场上“T 1”交易制度下的交易令投资者不能及时纠正交易错误,导致投资者的交易风险有扩大趋势．其实,投资者面临的最大交易风险并不是“T 0”和“T 1”这两种交易制度所带来的,而是股票市场的下跌造成的．因此,如果综合考虑我国股票市场的未来发展以及两种交易制度带给投资者的交易风险,实施“T 0”交易制度是可行的．     

一种确定IFSP中迭代次数下限的算法

提出了一种求解带概率的迭代函数系统（IFSP）中迭代次数下限的自动算法,该算法基于一个基本假定,从给定的多个压缩仿射变换矩阵的谱半径入手,先分别求出每一个压缩仿射变换收敛到其对应的不动点时的迭代次数,然后根据每一个压缩仿射变换使用的概率即可计算出IFSP中迭代次数的下限．理论分析和实验计算结果表明,提出的算法能有效地确定IFSP中迭代次数的下限,且在保证分形图质量的同时避免了不必要的计算开销,为快速生成高质量的分形图提供了一种有效的方法．     

Randomized unit root processes for modelling and forecasting financial time series: Theory and applications

This paper considers the problems of statistically analysing the levels of financial time series rather than their differences, which are often equivalent to returns and which are traditionally analysed in econometric modelling. This focus on differences is a consequence of the inherent nonstationarity of the levels, and hence analysing the latter requires introducing an alternative framework for modelling nonstationary behaviour. We do this by considering randomized unit root processes, arguing that these can have a natural interpretation in the financial context. The paper thus develops methods for testing for randomized unit roots and for modelling such processes. It then applies these techniques to various financial time series, so as to ascertain their potential usefulness, particularly for forecasting.     

一类流行病数学模型的Hopf分支

利用平面向量场极限环分支的Hopf分支理论．研究了一类具有非线性传染率kI^p-1S^q的SIRS流行病传播动力学模型．首次给出了模型中指教为p≥2,q≥1的一般整数时,系统正平衡点的精确表达式,证明了此类系统至少可以存在两个极限环,并给出了Hopf分支的数值计算及模拟结果．该简化平衡点坐标表达式的方法适用于一般情形,从而使奇点焦点量的计算简洁、可行．     

非线性阻尼下光滑不连续振子极限环的全局演化

针对光滑不连续振子,提出了一种优化的广义谐波函数摄动法,得到其极限环的振幅与系统参数之间的解析关系式以及极限环的解析近似解。同时,基于微分方程定性理论,建立了该振子极限环特征量的解析计算公式。利用上述结果,可围绕极限环何时产生、如何分岔、在何处消失以及稳定性如何等问题,对具有复杂非线性阻尼项的光滑不连续振子极限环的全局演化过程展开定量分析。通过将本文所得之结果与龙格-库塔法之结果进行对比,验证了所提优化方法的可行性和可靠性,为研究强非线性振动系统解的全局演化问题,提供了新的参考方法。     

Forecasting stock volatility in the presence of extreme shocks: Short-term and long-term effects

This paper introduces a novel generalized autoregressive conditional heteroskedasticity–mixed data sampling–extreme shocks (GARCH-MIDAS-ES) model for stock volatility to examine whether the importance of extreme shocks changes in different time ranges. Based on different combinations of the short- and long-term effects caused by extreme events, we extend the standard GARCH-MIDAS model to characterize the different responses of the stock market for short- and long-term horizons, separately or in combination. The unique timespan of nearly 100 years of the Dow Jones Industrial Average (DJIA) daily returns allows us to understand the stock market volatility under extreme shocks from a historical perspective. The in-sample empirical results clearly show that the DJIA stock volatility is best fitted to the GARCH-MIDAS-SLES model by including the short- and long-term impacts of extreme shocks for all forecasting horizons. The out-of-sample results and robustness tests emphasize the significance of decomposing the effect of extreme shocks into short- and long-term effects to improve the accuracy of the DJIA volatility forecasts.     

The industrial asymmetry of the stock price prediction with investor sentiment: Based on the comparison of predictive effects with SVR

As a representative emerging financial market, the Chinese stock market is more prone to volatility because of investor sentiment. It is reasonable to use efficient predictive methods to analyze the influence of investor sentiment on stock price forecasting. This paper conducts a comparative study about the predictive performance of artificial neural network, support vector regression (SVR) and autoregressive integrated moving average and selects SVR to study the asymmetry effect of investor sentiment on different industry index predictions. After studying the relevant financial indicators, the results divide the Shenwan first-class industries into two types and show that the industries affected by investor sentiment are composed of young companies with high growth and high operative pressure and there are a great number of investment bubbles in those companies.