股票的交易数据拟合与聚类研究

摘要
摘要
随着中国股票市场的迅速发展，市场的规范化程度不断提高，股票品种也 有了向多元多层次化发展的趋势，吸引了越来越多投资者的目光。为减少投资 风险，获得丰厚的利润回报，理智的股票投资者将会更加重视投资对象的选择。 表达股票数据的真实意义对投资者来说是关键，而股票交易数据包含了大量的 信息，对股票交易数据的分析就显得特别重要。
硕士学位论文
股票的交易数据拟合与聚类研究 STUDY OF THE FITTING AND CLUSTERING ABOUT STOCK
TRANSACTION DATA
乔鑫
哈尔滨工业大学 2012 年 7 月
国内图书分类号：F224.9 国际图书分类号：336.7
学校代码：10213 密级：公开
管理学硕士学位论文
将函数性数据分析方法应用于分析研究函数性数据中，改善了传统分析方 法对数据要求的约束，这样不仅增加了可分析数据的范围，而且扩大了函数性 数据分析方法的应用领域。将该方法实际应用于现实股票交易数据的拟合和聚 类分析中，得到了非常理想的结果，表明了方法的有效性，该方法也能够为投 资者提供很好的决策依据。 关键词：股票；函数性数据；数据拟合；聚类
1.3 论文研究内容和结构 ............................................................................. 11 1.3.1 研究内容 ............................................................................................... 11 1.3.2 研究结构 ............................................................................................... 11
股票交易数据的表现受很多因素的影响，包含信息量较大，总体上体现出 函数性特征，采用传统的时间序列数据分析方法受到很多局限。为此，根据股 票交易数据的函数性特征，借助函数性数据分析方法，对股票交易数据进行了 有针对性的分析。主要内容是基于股票交易数据的函数性特征，对股票交易数 据进行预处理和曲线拟合，使得原始数据“抽象化”，进而得到统一的函数系 数矩阵，再借助系数矩阵对反映股票特性的函数进行聚类，得出相应的股票聚 类结果，并对结果进行了合理解释。
1.1 研究背景及目的意义 ...............................................................................1 1.1.1 研究背景 .................................................................................................1 1.1.2 研究目的 .................................................................................................2 1.1.3 研究意义 .................................................................................................3
-I-
Abstract
Abstract
With the rapid development of China's stock market and the high degree of standardization of the market, stock varieties have a tendency to become multi-typical and multi-level. So stock market could attract more and more investors. In order to reduce investment risk and obtain high profit return, rational investors will pay more attention to the choice of stock investments. Expressing real meaning of the stock data is critical for investors. Because of stock transaction data containing a lot of information, analysis of stock transaction data is particularly important.
Limitation of traditional analytical methods for functional data has been improved by using functional data analysis. This method not only increases the scope of the analysis of data, but also expands the applications of functional data analysis methods. In practically, fitting and cluster analysis about stock transaction data by this new method could obtain the ideal result, which reflect the validity of the method. It can provide a better basis to investors for decision.
1.2 国内外研究现状及评述 ...........................................................................4 1.2.1 股票数据分析研究现状 ..........................................................................4 1.2.2 时间序列与函数性数据聚类研究现状 ..................................................6 1.2.3 研究现状评述 ....................................................................................... 10
STUDY OF THE FITTING AND CLUSTERING ABOUT STOCK
TRANSACTION DATA
Candidate：
Qiao Xin
Supervisor：
Associate Prof. Li Baojia
Academic Degree Applied for： Master of Management
The performance of the stock transaction data is affected by a number of factors, including a large amount of information. The stock transaction data reflect the functional characteristics on the whole. There are many limitations to use traditional time-series data analysis methods. According to this kind of the stock transaction data, we analyses them by the functional data analysis method. The main content is preprocessing and curve fitting for stock transaction data based on the functional characteristics of these data. This method could make the original data "abstraction". So we can obtain a unified coefficient matrix, and then using the coefficient matrix of reflecting stock characteristics function to cluster. In the end, we draw the corresponding conclusion about stock clustering, and these conclusions were explained reasonably.
Speciality：
Tech-Economics and Management
Affiliation：
School of Management
Date of Defence：
July, 2012
Degree-Conferring-Institution： Harbin Institute of Technology
Keywords：stock, functional data, data fitting, clustering
- II -
目录
目录
摘 要 ....................................................................................................................... I ABSTRACT ............................................................................................................. II 第 1 章 绪 论 .........................................................................................................1