【作者】 龚惠群；

【导师】 彭江平；

【作者基本信息】 湖南大学 ， 管理科学与工程， 2003， 硕士

【摘要】 随着市场经济的发展，我国的股市正日益成熟和规范，投资者在进行投资决策时也愈加趋于理性化。目前可以运用许多统计分析方法来发现一些隐藏在股票信息中的规律，以帮助投资者对股票进行分析和预测。 然而，常用的这些统计分析方法无法发现出在股市中存在的这样一些带有时间约束的规律——在某个时间段W(如一天)内，如果股票A的收盘价上涨超过5％，那么间隔INT个时间段(如两天)后的那个时间段(即第三天)内股票B和股票C会以80％的可能性也上涨(或下跌)。因此，本文采用一种目前正在发展的新技术——数据挖掘技术来发现股市中存在的这类复杂的序列规则。这类具有时间段W和时间间隔INT两维约束的序列规则的挖掘无疑对于指导投资决策具有重要的意义。 本文主要有三个创新点。其中第一个创新点是在本文中建立了两个具有时间约束的股票序列模式挖掘模型：带有确定的时间段W约束的一维模型和带有确定的时间段W及时间间隔INT约束的二维模型。第二个创新点则是通过对关联规则的Apriori算法和FP_Growth算法进行扩展来实现一维股票序列规则的采掘。至于第三个创新之处就是通过设计一个全新的算法来实现二维股票序列规则的挖掘。在本文的最后一章通过一个实证研究对本文所提算法的可行性进行了验证。 本文一共分为四个部分：第一部分介绍了传统的股票分析方法及数据挖掘技术的基本概念；第二部分则建立了两个具有时间约束的股票序列模式挖掘模型；第三部分就对具有时间约束条件的股票序列规则采掘的一维和二维算法进行了实现，并且扩展讨论了在分布式环境下进行这类序列规则的挖掘所需注意的几个问题；最后一部分则进行了一个实证研究来对本文所提出算法的正确性进行验证。更多还原

【Abstract】 With the developing of market-directed economy, our stock market is becoming more mature and standardize day by day, and the investor’s decision is more rational. Nowadays, we can use lots of statistical method of analysis to discover some concealed rules in stock information, thereby help investors to analyze and forecast the stock.However, these common statistical method of analysis can’t be used to find out the rule with time constraint in stock market as follows, if the closing price of stock A is going up to 5% in a time-segment W (suck as one day), then those of stock B and C will also rise (or descent) in 80% probability in the time- segment (that is the third day) just after INT time-segments (such as two days). Therefore, in this paper a new developing technique-data mining (DM) is adopted to look for these compound sequence rules in stock market. No doubt, the mining of the sequence rule with two dimensions-time constraints has very important meaning in guiding investment decision.In this paper, there are three innovations. The first innovation is that we construct two stock sequence rule models with time constraint: the stock’ sequence rule model of one dimension with certain time-segment (represented by W) constraint and the stock’ sequence rule model of two dimensions with W and time-interval (represented by INT) constraints. And the second innovation is that we bring about the mining of the stock’ sequence rule of one dimension through extending the association rule algorithm - Apriori algorithm and FP_Growth algorithm. So as to the third innovation is designing a new algorithm to mine the stock’ sequence rule of two dimensions. And we also validate the feasibility of the algorithms given by the paper through a positive research in the last chapter of the paper.This paper includes four parts. In the first part we introduce some basic concepts of the technology of data mining and the traditional analytic methods of stock. In the second part we establish two models for mining the stock’ sequential scheme with time constraint. Then we accomplish the mining algorithm of stock’ sequence rule of one dimension and two dimensions with time constraint, and we also extendly discuss the problems that should be paid attention to in order to achieve the sequence rule in a distributed system in the third part. In the last part, we make a positive research to verify the correctness of the algorithms given by the paper.更多还原