A Forecasting Model of Dynamic Grey Rough Set and its Application on Stock Selection

2006 IEEE Conference on Cybernetics and Intelligent Systems Pub Date : 2006-06-07 DOI:10.1109/ICCIS.2006.252320

Ting-Cheng Chang, Chuen-Jiuan Jane, Yuan-Paio Lee

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引用次数: 1

Abstract

The main purpose of paper is to establish a system, which combines rough set and grey theory. This model is used to let the time-serial, season-serial or regular data have the dynamic trend concepts by grey prediction, then, select the data sets with trend value through rough set screening system. It mainly is applied for a portfolio prediction in the stock market. Our study first predicts each listed company's attributes of condition and decision-making by grey prediction, secondly groups their attributes by K-means grouping tools, then filters and categorizes the groups with the classified capacity of rough set for uncertain and non-sufficient information and selects the stock portfolio. And then we evaluate the company shares from the portfolio according to their past EPS and ROE and elect the better ones again. Finally, the selected companies are arranged in order with grey relation and determine the weight of each share in the portfolio according to it. The experimental result in Taiwan: during five years (2000-2004), the average annual rate of return was 38.1%. The portfolio determined by the model overran the market dramatically

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动态灰色粗糙集预测模型及其在选股中的应用

本文的主要目的是建立一个将粗糙集理论与灰色理论相结合的系统。该模型通过灰色预测让时间序列、季节序列或规律数据具有动态趋势概念，然后通过粗集筛选系统选择具有趋势值的数据集。它主要应用于股票市场的投资组合预测。本文首先利用灰色预测方法对各上市公司的条件属性和决策属性进行预测，然后利用K-means分组工具对各上市公司的属性进行分组，然后利用粗糙集对不确定信息和不充分信息的分类能力对分组进行过滤和分类，选择股票组合。然后我们根据公司过去的每股收益和净资产收益率对投资组合中的公司股票进行评估，并再次选出较好的股票。最后，将选取的公司按灰色关联排序，并据此确定投资组合中各股份的权重。台湾地区的实验结果:在2000-2004年的五年间，平均年收益率为38.1%。这个模型决定的投资组合大大超过了市场

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2006 IEEE Conference on Cybernetics and Intelligent Systems

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