An SVM improvement prediction in multifactor model for stocks selection
In this paper, an entire multifactor model has constructed, based on financial indicators. We improve the prediction of the SVM classification in the multifactor model. The ranking method is used for data preprocessing, then SVM predicts the stock return classification. Finally, the distance from da...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
National Computer System Engineering Research Institute of China
2019-09-01
|
| Series: | Dianzi Jishu Yingyong |
| Subjects: | |
| Online Access: | http://www.chinaaet.com/article/3000108321 |
| _version_ | 1818290398233100288 |
|---|---|
| author | Zhang Weinan Lu Tongyu Sun Jianming |
| author_facet | Zhang Weinan Lu Tongyu Sun Jianming |
| author_sort | Zhang Weinan |
| collection | DOAJ |
| description | In this paper, an entire multifactor model has constructed, based on financial indicators. We improve the prediction of the SVM classification in the multifactor model. The ranking method is used for data preprocessing, then SVM predicts the stock return classification. Finally, the distance from data to the hyperplane is used to improve the classification predict. With this strategy, in constituent stocks of CSI500, the portfolio gains 88.96% accumulated return from 2016Q4 to 2018Q1. Technical analysis moving average(MA) and channel breakout(CB) as trading time strategies can decrease fluctuation and drawdown. High frequent data are used to re-construct the MA strategy and get lower fluctuation. This model provides a new research perspective: SVM character is used for prediction improvement, technical analysis for strategy return. |
| first_indexed | 2024-12-13T02:27:32Z |
| format | Article |
| id | doaj.art-0676b9cabf904d638c53bf91e32dcf02 |
| institution | Directory Open Access Journal |
| issn | 0258-7998 |
| language | zho |
| last_indexed | 2024-12-13T02:27:32Z |
| publishDate | 2019-09-01 |
| publisher | National Computer System Engineering Research Institute of China |
| record_format | Article |
| series | Dianzi Jishu Yingyong |
| spelling | doaj.art-0676b9cabf904d638c53bf91e32dcf022022-12-22T00:02:35ZzhoNational Computer System Engineering Research Institute of ChinaDianzi Jishu Yingyong0258-79982019-09-01459222710.16157/j.issn.0258-7998.1903043000108321An SVM improvement prediction in multifactor model for stocks selectionZhang Weinan0Lu Tongyu1Sun Jianming2College of Economics and Management，China Jiliang University，Hangzhou 310018，ChinaCollege of Economics and Management，China Jiliang University，Hangzhou 310018，ChinaCollege of Economics and Management，China Jiliang University，Hangzhou 310018，ChinaIn this paper, an entire multifactor model has constructed, based on financial indicators. We improve the prediction of the SVM classification in the multifactor model. The ranking method is used for data preprocessing, then SVM predicts the stock return classification. Finally, the distance from data to the hyperplane is used to improve the classification predict. With this strategy, in constituent stocks of CSI500, the portfolio gains 88.96% accumulated return from 2016Q4 to 2018Q1. Technical analysis moving average(MA) and channel breakout(CB) as trading time strategies can decrease fluctuation and drawdown. High frequent data are used to re-construct the MA strategy and get lower fluctuation. This model provides a new research perspective: SVM character is used for prediction improvement, technical analysis for strategy return.http://www.chinaaet.com/article/3000108321multifactor modelstock selectiontechnical analysis |
| spellingShingle | Zhang Weinan Lu Tongyu Sun Jianming An SVM improvement prediction in multifactor model for stocks selection Dianzi Jishu Yingyong multifactor model stock selection technical analysis |
| title | An SVM improvement prediction in multifactor model for stocks selection |
| title_full | An SVM improvement prediction in multifactor model for stocks selection |
| title_fullStr | An SVM improvement prediction in multifactor model for stocks selection |
| title_full_unstemmed | An SVM improvement prediction in multifactor model for stocks selection |
| title_short | An SVM improvement prediction in multifactor model for stocks selection |
| title_sort | svm improvement prediction in multifactor model for stocks selection |
| topic | multifactor model stock selection technical analysis |
| url | http://www.chinaaet.com/article/3000108321 |
| work_keys_str_mv | AT zhangweinan ansvmimprovementpredictioninmultifactormodelforstocksselection AT lutongyu ansvmimprovementpredictioninmultifactormodelforstocksselection AT sunjianming ansvmimprovementpredictioninmultifactormodelforstocksselection AT zhangweinan svmimprovementpredictioninmultifactormodelforstocksselection AT lutongyu svmimprovementpredictioninmultifactormodelforstocksselection AT sunjianming svmimprovementpredictioninmultifactormodelforstocksselection |