随机森林在量化选股中的应用研究

doi:10.12005/orms.2016.0098

运筹与管理 ›› 2016, Vol. 25 ›› Issue (3): 163-168.DOI: 10.12005/orms.2016.0098

随机森林在量化选股中的应用研究

王淑燕¹,曹正凤²,陈铭芷¹

1.辅仁大学商学研究所,台湾 24205;
2.北京博宇通达科技有限公司,北京 102617

收稿日期:2013-08-02 出版日期:2016-06-25
作者简介:王淑燕(1981-)女,山东菏泽人,博士生,经济统计分析;曹正凤(1979-),男,江西九江人,技术总监,博士,统计理论研究;通讯作者:陈铭芷(1981-)女,台湾台北人,教授兼所长,博士,作业管理、品质管理、经济管理。
基金资助:
国家自然科学基金资助项目(71071022)

Research on Application of Random Forests in the Quantitative Stock Selection Model

WANG Shu-yan¹, CAO Zheng-feng², CHEN Ming-zhi¹

1.Business Administration, Fu Jen Catholic University, Taiwan 24205, China;
2.Beijing Boyu Tongda Technology Co. Ltd., Beijing 102617, China

Received:2013-08-02 Online:2016-06-25

摘要/Abstract

摘要： 通过分析国内外量化选股模型采用的指标体系,从焦健的六因子模型出发,使用指标相关性分析方法,提出了八因子选股模型指标体系,选用了2013年3月200只股票的样本数据,使用随机森林算法实现了对2013年4月股票涨跌情况较高精确度的预测,通过对比分析焦健的六因子模型,并分析优选后的股票在行业平均收益、最值方面的实际表现,验证了该量化选股模型在中国股票市场上有较好的性能。

关键词: 随机森林, 量化投资, 选股指标, 价值成长投资策略

Abstract: By analyzing the indicator system used by the stock selection model at home and abroad, we start from the six factor model of Jiao Jian, use correlation analysis method of the indicators, and propose the indicator system of the eight factor stock selection model. We selecte the sample data for 200 stocks in 2013 March, achieve a prediction about the rise and fall of the stock in 2013 April by the random forests. We verify the quantitative stock model has better performance in the Chinese stock market, by comparing the six factor model of Jiao Jian and analyzing the actual performance values of the preferred stock in the average income and the minimum and maximum values in the trade.

Key words: random forests, quantitative investment, stock selection indicator, growth at a reasonable price(GARP)

中图分类号:

O212.6

王淑燕,曹正凤,陈铭芷. 随机森林在量化选股中的应用研究[J]. 运筹与管理, 2016, 25(3): 163-168.

WANG Shu-yan, CAO Zheng-feng, CHEN Ming-zhi. Research on Application of Random Forests in the Quantitative Stock Selection Model[J]. Operations Research and Management Science, 2016, 25(3): 163-168.

参考文献

[1] RBC Dominion Securities Inc.: Real Estate Investment Trusts-A Quantitative Approach to REIT Investing, 2008.
[2] 朱华成.RBC 选股模型在 A 股市场实证研究[R].上海:光大证券,2009.
[3] 赵永进.基于数据挖掘的股票分析与预测研究[D].河南:郑州大学,2005.
[4] 宋永辉,刘飞.基于神经网络模型的股票买卖时机探索商场现代化,2005,11(450):356.
[5] 焦健,赵学昂,葛新元.CART决策树的行业选股方法[R].深圳:国信证券经济研究所,2010.
[6] 陈光兴.张一明浅谈价值成长投资策略在中国股市的适用性[J].经营管理者,2010,(24):8-8.
[7] 严高剑.胡浩.马坚.李颖.GARP投资策略-成长与价值并重[R].中信证券研究所,2008.
[8] 张建军.基于数据挖掘的股票数据分析[D].山东:中国石油大学(华东),2010.
[9] 李云飞.基于人工智能方法的股票价值投资研究[D].黑龙江:哈尔滨工业大学,2008.
[10] Parkhurst D F, Brenner K P, Dufour A P, et al.. Indicator bacteria at five swimming beaches—analysis using random forests[J]. Water Research, 2005, 39(7).
[11] Smith A, Sterba-boatwright B, Mott J. Novel application of a statistical technique, random forests, in a bacterial source tracking study[J]. Water Research, 2010, 44(14).
[12] 刘毅.因子选股模型在中国市场的实证研究[D].上海:复旦大学,2012.
[13] 方匡南,吴见彬,朱建平,谢邦昌.随机森林方法研究综述[J]统计与信息论坛 2011,3(26):32-38.
[14] Breiman L. Random forests[J]. Machine Learning, 2001, 45(1): 5-32.
[15] Kerber R. ChiMerge: discretization of numeric attributes[C]. Proceedings Ninth National Conference on Artificial Intelli-gence. AAAl Press, 1992. 123-128.
[16] Liu H, Setiono R. Feature selection via discretization[J]. IEEE Transactions on Knowledge and Data Engineering, 1997, 9(4): 642-645.
[17] Tay E H, Shen L. A modified chi2 algorithm for discretization[J]. IEEE Transactions on Knowledge and Data Engineering, 2002, 14(3): 666-670.
[18] Chao-TonSu, Jyh-HwaHsu. An extended chi2 algorithm for discretization of real value attributes[J]. IEEE Transactions on Knowledge and Data Engineering, 2005, 17(3): 437-441.

随机森林在量化选股中的应用研究

Research on Application of Random Forests in the Quantitative Stock Selection Model

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 3

编辑推荐

Metrics

[1]	张建同, 孙嘉青. 基于组合预测法的共享单车租赁需求量预测[J]. 运筹与管理, 2021, 30(10): 146-152.
[2]	黄国兴, 曹先怀, 钱晓飞. 一种基于随机森林的备件预测模型研究[J]. 运筹与管理, 2021, 30(10): 165-168.
[3]	黄东宾, 孙宏伟, 左传长. 短期趋势判断与投资组合优选[J]. 运筹与管理, 2018, 27(10): 164-173.