[1]黄文龙,薛未杰,冷佳俊,等.基于堆叠策略的高送转题材股预测模型[J].计算机技术与发展,2022,32(S1):89-93.[doi:10. 3969 / j. issn. 1673-629X. 2022. S1. 020]
　HUANG Wen-long,XUE Wei-jie,LENG Jia-jun,et al.Forecasting Model of High-Delivery-Rate Stock Based on Stacking Strategy[J].,2022,32(S1):89-93.[doi:10. 3969 / j. issn. 1673-629X. 2022. S1. 020]
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基于堆叠策略的高送转题材股预测模型()

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
32
期数:
2022年S1期
页码:
89-93
栏目:
应用前沿与综合
出版日期:
2022-12-11

文章信息/Info

Title:
Forecasting Model of High-Delivery-Rate Stock Based on Stacking Strategy
文章编号:
1673-629X(2022)S1-0089-05
作者:
黄文龙; 薛未杰; 冷佳俊; 吕 品
上海电机学院,上海 201306
Author(s):
HUANG Wen-long; XUE Wei-jie; LENG Jia-jun; LYU Pin
Shanghai Dianji University,Shanghai 201306,China
关键词:
堆叠策略; LSTM; 差分进化; 高送转; 因子合成
Keywords:
stacking strategy; LSTM; differential evolution; high-delivery-rate; factor synthesis
分类号:
TP39
DOI:
10. 3969 / j. issn. 1673-629X. 2022. S1. 020
摘要:
该文提出了一种基于堆叠策略的高送转题材股预测模型。 该模型使用的 4 种个体分类器分别是支持向量机、神经网络、随机森林和极限梯度提升树,元学习器为线性模型。 为了提升高送转题材股预测模型的精度,采用差分进化算法对每个个体分类器的参数进行了优化。 实验结果表明,该模型在年送转数据集上的精度达到了 94. 3% 。? ?为进一步帮助投资者分析单一个股,还利用日数据构建了一个高送转辅助预测模型 LSTM,用于预测送转比变化趋势,以进一步帮助投资者及时捕捉市场信息。
Abstract:
We propose a forecasting model of high-send transfer theme stocks based on stacking strategy. The four individual classifiersused in this model are support vector machines,neural networks,random forests,and extreme gradient boosting trees. The meta-learner isa linear model. In order to improve the accuracy of the prediction model of high-send stocks,the differential evolution algorithm is usedto optimize the parameters of each individual classifier. The experimental results show that the accuracy of the proposed model on theannual transmission data set reaches 94. 3% . In order to further help investors analyze a single stock,we also use daily data to construct ahigh delivery-to-transfer auxiliary prediction model LSTM,which is used to predict the change trend of the delivery-to-transfer ratio tofurther help investors capture market information in a timely manner.

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更新日期/Last Update: 2022-06-10