基于数据挖掘技术的股票投资实证研究

发布时间：2018-05-23 11:27

本文选题：股票分析 + 关联规则　；参考：《重庆大学》2013年硕士论文

【摘要】：股市发展至今，股票分析始终是众多学者研究的热点。股市每天都会产生海量数据，很多业务都需要依赖对这些数据的分析，从中挖掘出有用的信息。利用数据挖掘技术建立有效的组合投资模型，寻找出最具投资价值的股票信息，以便投资者能够做出最佳决策，有效提高投资的回报率，具有重要的理论和现实意义。本文的主要研究工作：首先对股票市场进行了现状分析，，并总结了国内外将数据挖掘技术运用到股票分析的研究现状；其次介绍了数据挖掘技术，并着重介绍了其中的关联规则算法、决策树分类算法及马克维茨模型；然后运用以上数据挖掘技术进行了实例仿真，选取上证和深证股票市场2012年中的2000多只股票进行了实证分析。从股票的众多属性中用关联规则挖掘出影响股票走势的关键属性指标，并由关键属性指标对两千多只股票按决策树算法进行分类，实现策略选股。最后运用马克维茨模型对选定的投资组合进行风险评估，并决定各股票在投资组合中的最佳比例。
[Abstract]:Up to now, stock analysis has always been a hot research topic for many scholars. The stock market produces huge amounts of data every day, and many businesses rely on the analysis of these data to extract useful information from it. It is of great theoretical and practical significance to establish an effective portfolio investment model by using data mining technology to find out the most valuable stock information so that investors can make the best decision and improve the return rate of investment effectively. The main research work of this paper is as follows: Firstly, the paper analyzes the current situation of stock market, and summarizes the research status of applying data mining technology to stock analysis at home and abroad, then introduces data mining technology, and emphasizes on the association rules algorithm. The decision tree classification algorithm and Markowitz model are used to simulate the above data mining technology and more than 2000 stocks in the stock market of Shanghai Stock Exchange and Shenzhen Stock Exchange in 2012 are selected for empirical analysis. The key attribute indexes which affect the trend of stocks are mined by association rules from many attributes of stocks, and more than 2,000 stocks are classified according to decision tree algorithm by key attribute indexes, and the strategy of stock selection is realized. Finally, we use Markowitz model to evaluate the risk of the selected portfolio, and determine the optimal proportion of each stock in the portfolio.
【学位授予单位】：重庆大学
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：F832.51;F224;TP311.13

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