一种基于可辨识矩阵与决策规则独立原则的缺损数据知识发现方法

该文在对粗集理论进行深入研究的基础上，依据决策规则独立原则，提出了一种改进的ROUSTIDA算法，有效地解决了原算法可能存在的决策规则矛盾的问题，实例表明此方法是行之有效的。     

数据库中挖掘决策偏好信息的粗糙集方法研究

获取决策者的决策偏好信息是多属性决策问题的关键所在，这种偏好信息往往隐藏在大量历史数据中。数据挖掘技术是知识自动获取的一个重要手段。该文基于粗糙集理论提出了一个自动发现决策偏好信息的算法，利用该算法挖掘出的偏好信息采用if …then 规则的形式，因此更容易被决策者所理解。     

一种对象完备度优先填补的决策树规则提取算法

不完备信息系统中决策规则的提取是数据挖掘领域的重要研究问题。对不完备信息系统中决策规则的主要获取方法进行分析,以决策属性具有缺失值的不完备决策表为研究对象,提出一种基于数据优先填补的决策树规则提取算法。针对ROUSTIDA算法在数据填补时运算量较大且容易导致决策规则冲突这一问题,算法采用决策属性优先填补的思想,引入对象完备度概念对其进行改进,使用改进的ROUSTIDA算法对不完备决策表进行一次性数据填补预处理,并在限制容差关系下采用属性重要性为启发函数构建决策树,从而获得决策规则。实例表明该方法是有效的,生成的决策规则简单,且具有较高的精确度。     

数据挖掘在教学业绩考核中的应用研究

数据挖掘是致力于数据分析和理解、揭示数据内部潜在联系的技术,关联规则是数据挖掘中最活跃的研究方法之一。高校教学管理者从诸多方面对教师教学业绩进行考核,该文针对某高校教师教学业绩考核数据集,采用关联规则中的Apriori算法,挖出数据集中某些数据项之间的关联规则,通过对关联规则的分析找出它们之间隐藏的信息,为高校教学管理者提供决策支持,同时指导教师的教学。     

基于粗糙集的决策规则约简

粗糙集理论是一个新的数据挖掘方法，正越来越被人们所重视。其主要思想是保持分类能力不变的情况下，利用等价类，通过属性约简和决策规则约简，达到发掘知识并简化知识的目的。但是属性约简是一个NP问题，对属性的约简和决策规则的约简只能通过启发式算法实现。该文针对属性约简和决策规则约简，各提出了一个启发式算法。     

决策信息系统中挖掘全部决策规则的算法

在粗糙集理论的基础上,对决策信息系统中边界区域的数据进行研究,提出一种从边界区域数据中挖掘决策规则的算法——近似序列决策规则挖掘算法。在16个UCI数据集上的测试表明,该算法在规则的准确度和平均前件长度2个指标上优于ID3算法,能简洁、高效地挖掘出决策信息系统中的全部决策规则,为挖掘未知知识提供了新的思路。针对挖掘出的全部决策规则,提出新的确定性度量和一致性度量指标,用以准确地反映决策规则的性能。     

一个基于Rough set理论的增量式学习算法

为了获取最小决策规则,当增加新例子时,传统的方法通常需要对决策表中所有数据重新计算,效率欠佳。为了尽量减少重复计算量,该文从Roughset理论出发,提出了一种新的增量式学习算法和最小重新计算的标准,并且用理论和实验对新算法和传统算法在算法复杂度上做了对比。     

基于GNP的多Agent人工股市模型

结合奥地利学派的经济思想，本文介绍了一种新的基于GNP算法的多Agent人工股市模型。该模型采用GNP算法来模拟交易个体的行为模式，进化他们的决策规则；同时在设计上强化Agent的异质性，并利用GA算法来优化模型参数。仿真结果表明，GNP-ASM模型表现出很好的统计性能，能够体现真实股市的一些基本特征。     

粗糙集理论分析及其在农业数据中的应用

从数据库中挖掘有用信息，将难理解的纯数据变为容易利用的规则，从而为以后的决策提供依据。以粗糙集理论和规则提取算法为基础，将基于信息量的粗糙集属性约简算法和规则提取算法集成起来提出一种集成算法，应用粗糙集约简掉冗余属性，然后利用规则提取算法得出有效规则。将此集成算法应用于农业领域，得出规则，并且效果良好，理论分析和应用都表明了本算法的有效性和实用性。此集成算法可以应用于各种大型数据库中，从中得出有效规则，让历史数据为以后的决策服务。     

最小化决策规则集的计算方法

在决策算法中，并不是所有的决策规则都是必要的，一些过剩的决策规则应该消去，而不影响作决策，因此，研究最小化决策规则集的计算方法是很有意义的．传统的决策算法并没有给出最小化决策规则集的形式化计算方法，为了解决最小化决策规则集的形式化计算问题，引入了最小化决策规则可辨识矩阵概念，提供了基于可辨识矩阵的基本决策规则的最小化决策规则集的计算方法．     

Stock Trading Using RSPOP: A Novel Rough Set-Based Neuro-Fuzzy Approach

This paper investigates the method of forecasting stock price difference on artificially generated price series data using neuro-fuzzy systems and neural networks. As trading profits is more important to an investor than statistical performance, this paper proposes a novel rough set-based neuro-fuzzy stock trading decision model called stock trading using rough set-based pseudo outer-product (RSPOP) which synergizes the price difference forecast method with a forecast bottleneck free trading decision model. The proposed stock trading with forecast model uses the pseudo outer-product based fuzzy neural network using the compositional rule of inference [POPFNN-CRI(S)] with fuzzy rules identified using the RSPOP algorithm as the underlying predictor model and simple moving average trading rules in the stock trading decision model. Experimental results using the proposed stock trading with RSPOP forecast model on real world stock market data are presented. Trading profits in terms of portfolio end values obtained are benchmarked against stock trading with dynamic evolving neural-fuzzy inference system (DENFIS) forecast model, the stock trading without forecast model and the stock trading with ideal forecast model. Experimental results showed that the proposed model identified rules with greater interpretability and yielded significantly higher profits than the stock trading with DENFIS forecast model and the stock trading without forecast model.     

MMDR算法的优化以及在证券仿真中的应用

设计了一种降低了时间复杂度和空间复杂度的横向拟正则规则双层的MMDR算法,介绍了利用这个优化的算法在股市数据中得到的正则规则进行股票价格预测的方法,以及如何将此算法的运行过程中产生的大量规则应用于证券交易仿真复杂适应系统的“个体”建模中,以解决证券交易仿真系统需要大量互不相同的个体建模的问题。     

基于深度强化学习的金融交易算法研究

交易策略在金融资产交易中具有十分重要的作用,如何在复杂动态金融市场中自动化选择交易策略是现代金融重要研究方向.强化学习算法通过与实际环境交互作用,寻找最优动态交易策略,最大化获取收益.提出了一个融合了CNN与LSTM的端到端深度强化学习自动化交易算法,CNN模块感知股票动态市场条件以及抽取动态特征,LSTM模块循环学习...     

An intelligent hybrid trading system for discovering trading rules for the futures market using rough sets and genetic algorithms

Discovering intelligent technical trading rules from nonlinear and complex stock market data, and then developing decision support trading systems, is an important challenge. The objective of this study is to develop an intelligent hybrid trading system for discovering technical trading rules using rough set analysis and a genetic algorithm (GA). In order to obtain better trading decisions, a novel rule discovery mechanism using a GA approach is proposed for solving optimization problems (i.e., data discretization and reducts) of rough set analysis when discovering technical trading rules for the futures market. Experiments are designed to test the proposed model against comparable approaches (i.e., random, correlation, and GA approaches). In addition, these comprehensive experiments cover most of the current trading system topics, including the use of a sliding window method (with or without validation dataset), the number of trading rules, and the size of training period. To evaluate an intelligent hybrid trading system, experiments were carried out on the historical data of the Korea Composite Stock Price Index 200 (KOSPI 200) futures market. In particular, trading performance is analyzed according to the number of sets of decision rules and the size of the training period for discovering trading rules for the testing period. The results show that the proposed model significantly outperforms the benchmark model in terms of the average return and as a risk-adjusted measure.     

Data mining investigation of co-movements on the Taiwan and China stock markets for future investment portfolio

On June 29, 2010, Taiwan signed an Economic Cooperation Framework Agreement (ECFA) with China as a major step to open markets between Taiwan and China. Thus, the ECFA will contribute by creating a closer relationship between China and Taiwan through economic and market interactions. Co-movements of the world’s national financial market indexes are a popular research topic in the finance literature. Some studies examine the co-movements and the benefits of international financial market portfolio diversification/integration and economic performance. Thus, this study investigates the co-movement in the Taiwan and China (Hong Kong) stock markets under the ECFA using a data mining approach, including association rules and clustering analysis. Thirty categories of stock indexes are implemented as decision variables to observe the behavior of stock index associations during the periods of ECFA implementation. Patterns, rules, and clusters of data mining results are discussed for future stock market investment portfolio.     

Combined soft computing model for value stock selection based on fundamental analysis

The stock selection problem is one of the major issues in the investment industry, which is mainly solved by analyzing financial ratios. However, considering the complexity and imprecise patterns of the stock market, obvious and easy-to-understand investment rules, based on fundamental analysis, are difficult to obtain. Therefore, in this paper, we propose a combined soft computing model for tackling the value stock selection problem, which includes dominance-based rough set approach, formal concept analysis, and decision-making trial and evaluation laboratory technique. The objectives of the proposed approach are to (1) obtain easy-to-understand decision rules, (2) identify the core attributes that may distinguish value stocks, (3) explore the cause–effect relationships among the attributes or criteria in the strong decision rules to gain more insights. To examine and illustrate the proposed model, this study used a group of IT stocks in Taiwan as an empirical case. The findings contribute to the in-depth understanding of the value stock selection problem in practice.     

基于遗传编程的中国股票市场有效性检验

现代资本市场理论与金融投资实践之间存在着有效市场假说与技术分析之间的矛盾,使用流行的技术交易规则检验股票市场有效性可能导致两种结论偏差。遗传编程使用树形结构表示问题的候选解,可以很好地描述技术交易规则。利用遗传编程算法生成一种技术交易策略,并用其检验上证综合指数和5个沪深股市个股。回测结果表明,提出的方法相对于“买入-持有”策略能够获得超额收益,并且优于常用的流行技术指标,也说明我国股票市场并未达到弱式有效。     

Trend following deep Q-Learning strategy for stock trading

Computers and algorithms are widely used to help in stock market decision making. A few questions with regards to the profitability of algorithms for stock trading are can computers be trained to beat the markets? Can an algorithm take decisions for optimal profits? And so forth. In this research work, our objective is to answer some of these questions. We propose an algorithm using deep Q-Reinforcement Learning techniques to make trading decisions. Trading in stock markets involves potential risk because the price is affected by various uncertain events ranging from political influences to economic constraints. Models that trade using predictions may not always be profitable mainly due to the influence of various unknown factors in predicting the future stock price. Trend Following is a trading idea in which, trading decisions, like buying and selling, are taken purely according to the observed market trend. A stock trend can be up, down, or sideways. Trend Following does not predict the stock price but follows the reversals in the trend direction. A trend reversal can be used to trigger a buy or a sell of a certain stock. In this research paper, we describe a deep Q-Reinforcement Learning agent able to learn the Trend Following trading by getting rewarded for its trading decisions. Our results are based on experiments performed on the actual stock market data of the American and the Indian stock markets. The results indicate that the proposed model outperforms forecasting-based methods in terms of profitability. We also limit risk by confirming trading actions with the trend before actual trading.     

Financial time-series analysis with rough sets

We investigate the use of the rough set model for financial time-series data analysis and forecasting. The rough set model is an emerging technique for dealing with vagueness and uncertainty in data. It has many advantages over other techniques, such as fuzzy sets and neural networks, including attribute reduction and variable partitioning of data. These characteristics can be very useful for improving the quality of results from data analysis. We demonstrate a rough set data analysis model for the discovery of decision rules from time series data for example, the New Zealand stock exchanges. Rules are generated through reducts and can be used for future prediction. A unique ranking system for the decision rules based both on strength of the rule and stability of the rule is used in this study. The ranking system gives the user confidence regarding their market decisions. Our experiment results indicate that the forecasting of future stock index values using rough sets obtains decision ruleswith high accuracy and coverage.     

关联规则在股票板块联动分析中的应用

Apriori算法是关联规则挖掘中的经典算法,针对Apriori算法的不足进行了一些改进。新算法使用垂直数据格式,并改进了产生候选项的连接方法。为了研究股票板块的联动关系,将改进算法应用于股票板块指数分析中。实验结果表明,改进算法能快速发现板块之间的联动关系,对股市分析和投资决策有一定的指导作用。