关键词: 机器学习, 股票停牌, 组合模型, 随机森林

Abstract:

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