中国股票市场限价期成交量比预测模型

Jianwu Lin, Yishen Xu, Dayu Qin
{"title":"中国股票市场限价期成交量比预测模型","authors":"Jianwu Lin, Yishen Xu, Dayu Qin","doi":"10.1109/INDIN45582.2020.9442116","DOIUrl":null,"url":null,"abstract":"Algorithmic trading has become the major trading mechanism and one of the core technologies of electronic transactions globally. In USA, above 90% of electronic trading volumes has been done by algorithmic trading systems. However, algorithmic trading is still new in China capital market, only less than 10% of the volume has been done by algorithmic trading systems. With the rapid development of Chinese capital market and QFII capacity expansion, it will be the major trading mechanism in China. While being introduced into Chinese markets, it has to adapt to some special local trading rules, such as: Price limits (limit up and limit down). Because of the particular preferences by the Chinese investors, the market has a unique morphology forms in price limits. How to improve the model of price limits in China's algorithmic trading is the main focus of this research, especially under recent increasing volatility of global stock market in early 2020. This paper proposes a novel volume ratio prediction model, which can obtain a more accurate value of the price limit trading volume distribution. And an improved algorithmic trading logic based this model is proposed and proves its effectiveness.","PeriodicalId":185948,"journal":{"name":"2020 IEEE 18th International Conference on Industrial Informatics (INDIN)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Volume ratio prediction model during Price Limits Periods in China stock markets\",\"authors\":\"Jianwu Lin, Yishen Xu, Dayu Qin\",\"doi\":\"10.1109/INDIN45582.2020.9442116\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Algorithmic trading has become the major trading mechanism and one of the core technologies of electronic transactions globally. In USA, above 90% of electronic trading volumes has been done by algorithmic trading systems. However, algorithmic trading is still new in China capital market, only less than 10% of the volume has been done by algorithmic trading systems. With the rapid development of Chinese capital market and QFII capacity expansion, it will be the major trading mechanism in China. While being introduced into Chinese markets, it has to adapt to some special local trading rules, such as: Price limits (limit up and limit down). Because of the particular preferences by the Chinese investors, the market has a unique morphology forms in price limits. How to improve the model of price limits in China's algorithmic trading is the main focus of this research, especially under recent increasing volatility of global stock market in early 2020. This paper proposes a novel volume ratio prediction model, which can obtain a more accurate value of the price limit trading volume distribution. And an improved algorithmic trading logic based this model is proposed and proves its effectiveness.\",\"PeriodicalId\":185948,\"journal\":{\"name\":\"2020 IEEE 18th International Conference on Industrial Informatics (INDIN)\",\"volume\":\"21 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-07-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE 18th International Conference on Industrial Informatics (INDIN)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/INDIN45582.2020.9442116\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE 18th International Conference on Industrial Informatics (INDIN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INDIN45582.2020.9442116","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

算法交易已成为全球电子交易的主要交易机制和核心技术之一。在美国,超过90%的电子交易量是由算法交易系统完成的。然而,算法交易在中国资本市场上仍然是新生事物,只有不到10%的交易量是由算法交易系统完成的。随着中国资本市场的快速发展和QFII能力的扩大,QFII将成为中国主要的交易机制。在进入中国市场时,必须适应一些特殊的当地交易规则,例如:价格限制(涨跌限制)。由于中国投资者的特殊偏好,市场在限价方面形成了独特的形态形式。如何改进中国算法交易中的限价模型是本文研究的主要重点,特别是在近期全球股市在2020年初波动加剧的情况下。本文提出了一种新的成交量比预测模型,该模型可以获得更准确的限价交易量分布值。在此基础上提出了一种改进的算法交易逻辑,并证明了其有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Volume ratio prediction model during Price Limits Periods in China stock markets
Algorithmic trading has become the major trading mechanism and one of the core technologies of electronic transactions globally. In USA, above 90% of electronic trading volumes has been done by algorithmic trading systems. However, algorithmic trading is still new in China capital market, only less than 10% of the volume has been done by algorithmic trading systems. With the rapid development of Chinese capital market and QFII capacity expansion, it will be the major trading mechanism in China. While being introduced into Chinese markets, it has to adapt to some special local trading rules, such as: Price limits (limit up and limit down). Because of the particular preferences by the Chinese investors, the market has a unique morphology forms in price limits. How to improve the model of price limits in China's algorithmic trading is the main focus of this research, especially under recent increasing volatility of global stock market in early 2020. This paper proposes a novel volume ratio prediction model, which can obtain a more accurate value of the price limit trading volume distribution. And an improved algorithmic trading logic based this model is proposed and proves its effectiveness.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信