Machine Learning and High-Frequency Algorithms during Batch Auctions

IRPN: Science Pub Date : 2018-04-28 DOI:10.2139/ssrn.3170378
Gabriel Yergeau
{"title":"Machine Learning and High-Frequency Algorithms during Batch Auctions","authors":"Gabriel Yergeau","doi":"10.2139/ssrn.3170378","DOIUrl":null,"url":null,"abstract":"We present the first direct evidence of algorithmic imprints during batch auctions. Order anticipation is an integral part of high-frequency traders' strategies. Hence, some participants may have economic incentive to encrypt noise in the data. We use machine learning to identify five types of algorithmic imprints that hinder the processing of auction information and have the encrypted noise characteristics. Our approach rests on the shifted wavelet tree (Yunyue and Shasha (2003)), a burst detection indicator, and the dynamic time warping similarity measure (Skutkova, Vitek, et al. (2013)). We show that market participants can adapt their trading to the presence of encrypted noise by filtering data in real time, thus clarifying the price discovery process. This could reveal the presence of informed traders. The methodology deployed is adaptable to different environments, including continuous trading.","PeriodicalId":198407,"journal":{"name":"IRPN: Science","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2018-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IRPN: Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.3170378","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

We present the first direct evidence of algorithmic imprints during batch auctions. Order anticipation is an integral part of high-frequency traders' strategies. Hence, some participants may have economic incentive to encrypt noise in the data. We use machine learning to identify five types of algorithmic imprints that hinder the processing of auction information and have the encrypted noise characteristics. Our approach rests on the shifted wavelet tree (Yunyue and Shasha (2003)), a burst detection indicator, and the dynamic time warping similarity measure (Skutkova, Vitek, et al. (2013)). We show that market participants can adapt their trading to the presence of encrypted noise by filtering data in real time, thus clarifying the price discovery process. This could reveal the presence of informed traders. The methodology deployed is adaptable to different environments, including continuous trading.
批量拍卖中的机器学习和高频算法
我们提出了第一个直接证据的算法印记在批量拍卖。订单预期是高频交易者策略的一个组成部分。因此,一些参与者可能有经济动机来加密数据中的噪声。我们使用机器学习来识别五种类型的算法印记,这些印记阻碍了拍卖信息的处理,并具有加密的噪声特征。我们的方法基于位移小波树(Yunyue和Shasha(2003))、突发检测指标和动态时间扭曲相似性度量(Skutkova, Vitek等(2013))。我们表明,市场参与者可以通过实时过滤数据来适应加密噪声的存在,从而澄清价格发现过程。这可能会暴露出知情交易员的存在。所部署的方法适用于不同的环境,包括连续交易。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
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