Computer Engineering and Applications ›› 2020, Vol. 56 ›› Issue (18): 272-278.DOI: 10.3778/j.issn.1002-8331.1906-0310

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Research on Stock Suspension Prediction Based on Combination Model

SUN Fuxiong, LIU Guangming, ZENG Zixuan, PENG Mengqi   

  1. School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan 430074, China
  • Online:2020-09-15 Published:2020-09-10



  1. 中南财经政法大学 信息与安全工程学院,武汉 430074


Aiming at the problem of irregular suspension and long suspension of stock, a combination model of stock suspension prediction is proposed by using machine learning technology. The financial and stock indicators are selected as data, and divided into multiple feature data sets by calculating the importance of indicators. Multiple classification models are built to form a model pool, from which system randomly extracts some of models for classification, and obtain the final prediction result by voting method. Empirical analysis takes listed companies in China as the research object. The results of the experiments show that the proposed system has relatively high accuracy and can reduce misdiagnosis rate and omissive judgement rate compared single model.

Key words: machine learning, stock suspension, combination model, random forest



关键词: 机器学习, 股票停牌, 组合模型, 随机森林