A Decision Tree-based Classification Approach to Rule Extraction for Security Analysis
A Decision Tree-based Classification Approach to Rule Extraction for Security Analysis
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DOI：
10.1142/s0219622006001824
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发表时间：
2006-03
期刊：
影响因子：
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通讯作者：
Na Ren;M. Zargham;S. Rahimi
中科院分区：
文献类型：
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作者：
Na Ren;M. Zargham;S. Rahimi
股票选择规则被广泛应用于构造高绩效的股票投资组合。然而，过去一些经济专家开发的规则的预测性能对于当前的股票市场已经急剧下降。本文采用C4.5决策树分类方法，建立了基于股票基本面数据的股票预测模型，并在此基础上推导出一套股票选择规则。实验结果表明，生成的规则具有优异的预测性能。此外，它也证明了C4.5决策树分类模型可以有效地工作在高噪声的股票数据域。
Stock selection rules are extensively utilized as the guideline to construct high performance stock portfolios. However, the predictive performance of the rules developed by some economic experts in the past has decreased dramatically for the current stock market. In this paper, C4.5 decision tree classification method was adopted to construct a model for stock prediction based on the fundamental stock data, from which a set of stock selection rules was derived. The experimental results showed that the generated rules have exceptional predictive performance. Moreover, it also demonstrated that the C4.5 decision tree classification model can work efficiently on the high noise stock data domain.