Application of VPRS model with enhanced threshold parameter selection mechanism to automatic stock market forecasting and portfolio selection

This study proposes a technique based upon Fuzzy C-Means (FCM) classification theory and related fuzzy theories for choosing an appropriate value of the Variable Precision Rough Set (VPRS) threshold parameter (β) when applied to the classification of continuous information systems. The VPRS model is then combined with a moving Average Autoregressive Exogenous (ARX) prediction model and Grey Systems theory to create an automatic stock market forecasting and portfolio selection mechanism. In the proposed mechanism, financial data are collected automatically every quarter and are input to an ARX prediction model to forecast the future trends of the collected data over the next quarter or half-year period. The forecast data are then reduced using a GM(1, N) model, classified using a FCM clustering algorithm, and then supplied to a VPRS classification module which selects appropriate investment stocks in accordance with a pre-determined set of decision-making rules. Finally, a grey relational analysis technique is employed to weight the selected stocks in such a way as to maximize the rate of return of the stock portfolio. The validity of the proposed approach is demonstrated using electronic stock data extracted from the financial database maintained by the Taiwan Economic Journal (TEJ). The portfolio results obtained using the proposed hybrid model are compared with those obtained using a Rough Set (RS) selection model. The effects of the number of attributes of the RS lower approximation set and VPRS β-lower approximation set on the classification are systematically examined and compared. Overall, the results show that the proposed stock forecasting and stock selection mechanism not only yields a greater number of selected stocks in the β-lower approximation set than in the RS approximation set, but also yields a greater rate of return.     

基于时序超图卷积神经网络的股票趋势预测方法

传统的股票预测方法大多基于时间序列模型,忽视了股票之间复杂的关系,并且该关系往往超出成对连接,例如同行业板块内股票或者基金持仓多支股票.针对该问题,提出一种基于时序超图卷积神经网络(HGCN)的股价走势预测方法,根据金融投资事实构造超图模型以拟合股票之间的多元关系,该模型包括两大组件:门控循环单元(GRU)网络和超图卷...     

基于遗传算法的股票市场选择模型

为提高投资者在股票市场的收益,解决在证券投资中股票选择这一重要问题,提出一种基于遗传算法的股票选择模型。算法以上市公司的财务指标为样本特征,为克服K-means算法的不稳定性,采用基于遗传算法的K-means算法对同一板块股票进行聚类分析,剔除财务指标较差的一类中的股票。对筛选条件编码,为解决传统遗传算法处理复杂问题时存在的过早收敛现象,提出改进的遗传算子,利用改进的遗传算法寻找使股票市场投资收益最大化的选股模型参数。实验结果表明,该算法在股票选择上具有较好的效果,可供市场投资者借鉴。     

OWA rough set model for forecasting the revenues growth rate of the electronic industry

Business operation performance is related to corporation profitability and directly affects the choices of investment in the stock market. This paper proposes a hybrid method, which combines the ordered weighted averaging (OWA) operator and rough set theory after an attribute selection procedure to deal with multi-attribute forecasting problems with respect to revenue growth rate of the electronic industry. In the attribute selection step, four most-important attributes within 12 attributes collected from related literature are determined via five attribute selection methods as the input of the following procedure of the proposed method. The OWA operator can adjust the weight of an attribute based on the situation of a decision-maker and aggregate different attribute values into a single aggregated value of each instance, and then the single aggregated values are utilized to generate classification rules by rough set for forecasting operation performance.To verify the proposed method, this research collects the financial data of 629 electronic firms for public companies listed in the TSE (Taiwan Stock Exchange) and OTC (Over-the-Counter) market in 2004 and 2005 to forecast the revenue growth rate. The results show that the proposed method outperforms the listing methods.     

基于Vague集相似度量的股票选择

选择具有投资价值的股票是股市投资者面临的现实问题,价值发现型的投资理念迫切需要科学的股票选择方法与之相对应.本文基于Vague集相似性度量提出一种价值型股票投资的选择方法.用Vague集核属性构建价值型股票评价体系后,使用本文提出的Vague集相似性度量方法挑选出与目标预期相符的股票.本文的方法对于投资者选择适合投资的股票具有现实意义.     

基于k-means聚类的股票KDJ类指标综合分析方法

股票技术分析是证券分析的常用手段之一，目前的股票技术分析主要存在2个问题：1)都是从某个角度进行单维度分析，投资决策有较大偏差；2)任何单一的技术指标都有其局限性，需要相互补充才能更好进行投资决策。针对这些问题，本文讨论如何利用数据挖掘技术进行股票多维度综合分析问题。首先，分析数据挖掘应用到股票分析中可以解决的问题及可能面临的挑战；其次，提出一种基于数据挖掘聚类方法的选股模型；最后，对1364只上证股票进行实证分析，形成对股票的随机指标K、D、J等的综合挖掘结果。     

Short term stock selection with case-based reasoning technique

Stock selection is an important decision making problem. Trading strategies and rules based on fundamental and technical analysis can be used for decision making process. In this paper, we propose an intelligent stock selection method, which is called case-based reasoning (CBR). This technique uses the fundamental and technical indicators to identify the winning stocks around the earning announcements. CBR method is compared with other artificial intelligence techniques such as multi layer perceptron (MLP), decision trees (QUEST, Classification and Regression Trees, C5), generalized rule induction (GRI) and logistic regression. We show that the performance of CBR is better than the performance of other techniques in terms of classification accuracy, average return, Sharpe ratio and ideal profit.     

The dominance-based rough set approach to cylindrical plunge grinding process diagnosis

The dominance-based rough set approach is proposed as a methodology for plunge grinding process diagnosis. The process is analyzed and next its diagnosis is considered as a multi-criteria decision making problem based on the modelling of relationships between different process states and their symptoms using a set of rules induced from measured process data. The development of the diagnostic system is characterized by three phases. Firstly, the process experimental data is prepared in the form of a decision table. Using selected methods of signal processing, each process running is described by 17 process state features (condition attributes) and 5 criteria evaluating process state and results (decision attributes). The semantic correlation between all the attributes is modelled. Next, the phase of condition attributes selection and knowledge extraction are strictly integrated with the phase of the model evaluation using an iterative approach. After each loop of the iterative feature selection procedure the induction of rules is conducted using the VC-DomLEM algorithm. The classification capability of the induced rules is carried out using the leave-one-out method and a set of measures. The classification accuracy of individual models is in the range of 80.77–98.72 %. The induced set of rules constitutes a classifier for an assessment of new process run cases.     

An integrated portfolio optimisation procedure based on data envelopment analysis,artificial bee colony algorithm and genetic programming

Portfolio optimisation is an important issue in the field of investment/financial decision-making and has received considerable attention from both researchers and practitioners. However, besides portfolio optimisation, a complete investment procedure should also include the selection of profitable investment targets and determine the optimal timing for buying/selling the investment targets. In this study, an integrated procedure using data envelopment analysis (DEA), artificial bee colony (ABC) and genetic programming (GP) is proposed to resolve a portfolio optimisation problem. The proposed procedure is evaluated through a case study on investing in stocks in the semiconductor sub-section of the Taiwan stock market for 4 years. The potential average 6-month return on investment of 9.31% from 1 November 2007 to 31 October 2011 indicates that the proposed procedure can be considered a feasible and effective tool for making outstanding investment plans, and thus making profits in the Taiwan stock market. Moreover, it is a strategy that can help investors to make profits even when the overall stock market suffers a loss.     

A GRASP based solution approach to solve cardinality constrained portfolio optimization problems

In the current work, a solution methodology which combines a meta-heuristic algorithm with an exact solution approach is presented to solve cardinality constrained portfolio optimization (CCPO) problem. The proposed method is comprised of two levels, namely, stock selection and proportion determination. In stock selection level, a greedy randomized adaptive search procedure (GRASP) is developed. Once the stocks are selected the problem reduces to a quadratic programming problem. As GRASP ensures cardinality constraints by selecting predetermined number of stocks and quadratic programming model ensures the remaining problem constraints, no further constraint handling procedures are required. On the other hand, as the problem is decomposed into two sub-problems, total computational burden on the algorithm is considerably reduced. Furthermore, the performance of the proposed algorithm is evaluated by using benchmark data sets available in the OR Library. Computational results reveal that the proposed algorithm is competitive with the state of the art algorithms in the related literature.     

虚拟企业伙伴选择的粗糙集方法

将粗糙集理论应用于虚拟企业合作伙伴选择中,提出了基于粗糙集理论的虚拟企业伙伴选择的模型和方法.该方法根据各潜在伙伴企业的样本数据集建立决策系统,以伙伴选择的评价指标作为属性,从中挖掘出反映评价指标本质关系的重要属性以及伙伴选择知识规则.这些规则很好地描述了有限样本中所反映出的属性之间的本质特征,运用这些规则可对伙伴选择数据库中的其他样本有效地进行伙伴选择.应用实例表明,该方法是正确有效的.     

用户驱动的组合服务选择算法

用户需求准确获取和复杂不确定环境下的最优服务选择是服务组合研究中的难题.首先提出一个接近用户表达习惯的用户权重表达模型获取用户服务质量(QoS)权重,然后提出一种基于层次分析法(AHP)和逼近理想解排序法(TOPSIS)的组合服务选择算法(CWSSA)以解决最优服务选择问题.该算法转换用户的QoS属性两两比较矩阵为用户权重,评估区间数表示的复杂不确定环境下的QoS信息.还介绍了一个QoS属性关系表示的用户权重模型和区间数描述的QoS模型及聚合算法.实验验证了该算法的优越性和有效性.     

Evaluating industry performance using extracted RGR rules based on feature selection and rough sets classifier

In strategy of investment, an important thing for investors is to correctly predict firm’s revenue growth rate (RGR), which is an effective evaluation indicator for them to see how big the potential power of future development is and measure how about the growth of future development for a target firm that may be selected to investment portfolios. However, conventional methods of forecasting RGR have some shortcomings such as statistical methods based on strict assumptions of linearity and/or normality limit applications in real world. Additionally, due to rapid changing of information technology (IT) today, some techniques (i.e. rough sets and data mining tools) have become important research trends to both practitioners and academicians. With these reasons above, a new procedure, using the feature selection method and rough sets classifier, is proposed to extract decision rules and improve accuracy rate for classifying RGR. In empirical study, an actual RGR dataset collected from publicly traded company of stock markets is employed to illustrate the proposed procedure. The experimental results of RGR dataset analyses indicate that the proposed procedure surpasses the listing methods in terms of both higher accuracy and fewer attributes, and the output of proposed procedure is to generate a set of easily understandable decision rules that are readily applied in knowledge-based investment systems by investors.     

A stock selection strategy using fuzzy neural networks

This paper describes, from a general system-design perspective, an artificial neural network (ANN) approach to a stock selection strategy. The paper suggests a concept of neural gates which are similar to the processing elements in ANN, but generalized into handling various types of information such as fuzzy logic, probabilistic and Boolean information together. Forecasting of stock market returns, assessing of country risk and rating of stocks based on fuzzy rules, probabilistic and Boolean data can be done using the proposed neural gates. Fuzzy logic is known to be useful for decision-making where there is a great deal of uncertainty as well as vague phenomena, but lacks the learning capability; on the other hand, neural networks are useful in constructing an adaptive system which can learn from historical data, but are not able to process ambiguous rules and probabilistic data sets. This paper describes how these problems can be solved using the proposed neural gates.     

Score-hesitation trade-off and portfolio selection under intuitionistic fuzzy environment

Qualitative portfolio selection approach is a suitable technique for obtaining an optimal portfolio when quantitative data are unavailable and traditional portfolio models are ineffective. However, few studies focus on this issue. This study addresses the lack of research by defining the score-hesitation trade-off rule and introducing the intuitionistic fuzzy set (IFS), based on which an intuitionistic fuzzy portfolio selection (IFPS) model is proposed. The IFS is introduced because of its comprehensive consideration of preference and nonpreference, and is used to represent qualitatively evaluated information from investors and experts. Furthermore, an intuitionistic fuzzy investment scenario is established and a trisection approach is designed to distinguish three types of risk investors, based on which three corresponding IFPS models are constructed. After this, a portfolio selection process under the intuitionistic fuzzy environment is provided, and a simple example is given to show the application of the process. In addition, the investment opportunities and efficient frontier of the IFPS model are investigated to demonstrate the effectiveness of the proposed portfolio selection model. Finally, an example of calculating optimal investment ratios and selecting an optimal portfolio for four newly listed stocks in China is provided to demonstrate the feasibility and practicability of the proposed approaches.     

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。     

大数据分析的应用案例——投资模型的稳健性

交易模型的稳健性,指的是该模型的利润率曲线的波动性较小,没有大起大落。针对一个基于支持向量回归（SVR）技术的算法交易模型的稳健性问题,提出了使用若干导出指标训练统一的交易模型的策略,以及投资组合多样化的方法。首先,介绍基于支持向量回归技术的算法交易模型;然后,基于常用指标,构造了若干导出指标,用于股票价格的短期预测。这些指标,刻画了近期价格运动的典型模式、超买/超卖市场状态,以及背离市场状态。对这些指标进行了规范化,用于训练交易模型,使得模型可以泛化到不同的股票;最后,设计了投资组合多样化方法。在投资组合里,各个股票之间的相关性,有时会导致较大的投资损失;因为具有较强相关关系的股票,其价格朝相同方向变化。如果交易模型预测的价格走势不正确,引起止损操作,那么这些具有较强相关关系的股票,将引发雪崩式的止损,于是导致损失加剧。把股票根据相似性聚类到不同类别,通过从不同聚类类别中选择若干股票来构成多样化的投资组合,其中,股票的相似性,通过交易模型在不同股票上近期的利润曲线的相似度进行计算。在900只股票10年的价格大数据上进行了实验,实验结果显示,交易模型能够获得超过定期存款的超额利润率,年化利润率为8.06%。交易模型的最大回撤由13.23%降为5.32%,夏普指数由81.23%提高到88.79%,交易模型的利润率曲线波动性降低,说明交易模型的稳健性获得了提高。     

The use of grey relational analysis in solving multiple attribute decision-making problems

There are many cases in daily life and in the workplace which pose a decision problem. Some of them involve picking the best from among multiple available alternatives. However, no single alternative works best for all performance attributes. This research proposes a multiple attribute decision making (MADM) method, grey relational analysis (GRA), for solving this kind of problem. Two cases, facility layout and dispatching rules selection problem, which have been analyzed by data envelopment analysis (DEA), were also analyzed using the GRA procedure, in order to illustrate the use of GRA. In the case of the facility layout problem, 18 alternative layouts and 6 performance attributes were considered. In the case of the problem of selecting dispatching rules, 9 alternatives dispatching rules and 7 performance attributes were considered. For the two cases examined, the results of comparisons show that GRA is efficient for solving MADM problem.     

变精度粗糙集模型在决策树构造中的应用

本文在应用变精度粗糙集模型构造决策树的研究基础上,提出了具有置信度规则的决策树的构造方法。该方法是对决策树生成方法的一个改进,所构造的决策树具有更强的实用性以及更高的可理解性。本文还针对两个甚至两个以上属性的分类质量量度相等的特殊情形,给出了如何选择较优的属性作为结点的方法。与传统的ID3算法相比,该方法所构造的决策树不仅结构简单,而且更加实用,利于理解。     

数据挖掘在证券业务分析系统中的应用

对数据挖掘在证券业务分析系统中的应用相关理论进行了探讨,重点阐述了通过数据挖掘技术挖掘股票之间的关联规则,使投资者了解各种股票的走势及股票之间的关系,从而作出正确的投资决策。