日内外汇买卖价差模式-分析和预测

Andrius Paukste, A. Raudys
{"title":"日内外汇买卖价差模式-分析和预测","authors":"Andrius Paukste, A. Raudys","doi":"10.1109/CIFEr.2013.6611706","DOIUrl":null,"url":null,"abstract":"In the foreign exchange, market liquidity is represented by the best bid and the best ask price spread. We searched for liquidity patterns during 24h trading sessions After experimental comparison, we found that neural networks and regression trees are most suitable for liquidity forecasting and outperform simple averaging and regression. We also rated the factors that most influence forecasting accuracy. Time of day is the factor that influences liquidity the most, followed by day of the week. Month and day of the month have no effect on liquidity. As a final conclusion, we state that in most currency pairs the liquidity can be forecasted more accurately than the simple averaging which is often used in practice for planning large order execution.","PeriodicalId":226767,"journal":{"name":"2013 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Intraday forex bid/ask spread patterns - Analysis and forecasting\",\"authors\":\"Andrius Paukste, A. Raudys\",\"doi\":\"10.1109/CIFEr.2013.6611706\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the foreign exchange, market liquidity is represented by the best bid and the best ask price spread. We searched for liquidity patterns during 24h trading sessions After experimental comparison, we found that neural networks and regression trees are most suitable for liquidity forecasting and outperform simple averaging and regression. We also rated the factors that most influence forecasting accuracy. Time of day is the factor that influences liquidity the most, followed by day of the week. Month and day of the month have no effect on liquidity. As a final conclusion, we state that in most currency pairs the liquidity can be forecasted more accurately than the simple averaging which is often used in practice for planning large order execution.\",\"PeriodicalId\":226767,\"journal\":{\"name\":\"2013 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-04-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CIFEr.2013.6611706\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CIFEr.2013.6611706","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5

摘要

在外汇交易中,市场流动性由最佳买入价和最佳卖出价价差表示。通过实验比较,我们发现神经网络和回归树最适合于流动性预测,并且优于简单的平均和回归。我们还对影响预测准确性的因素进行了评级。一天中的时间是影响流动性最大的因素,其次是一周中的哪一天。月份和日期对流动性没有影响。作为最后的结论,我们指出,在大多数货币对中,流动性可以比简单的平均预测更准确,这在实践中经常用于计划大订单的执行。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Intraday forex bid/ask spread patterns - Analysis and forecasting
In the foreign exchange, market liquidity is represented by the best bid and the best ask price spread. We searched for liquidity patterns during 24h trading sessions After experimental comparison, we found that neural networks and regression trees are most suitable for liquidity forecasting and outperform simple averaging and regression. We also rated the factors that most influence forecasting accuracy. Time of day is the factor that influences liquidity the most, followed by day of the week. Month and day of the month have no effect on liquidity. As a final conclusion, we state that in most currency pairs the liquidity can be forecasted more accurately than the simple averaging which is often used in practice for planning large order execution.
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
期刊最新文献
Balance sheet outlier detection using a graph similarity algorithm Portfolio optimization using improved artificial bee colony approach Empirical analysis of model selection criteria for genetic programming in modeling of time series system DynOpt: Incorporating dynamics into mean-variance portfolio optimization Crowdsourced stock clustering through equity analyst hypergraph partitioning
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1