纽约证券交易所混合市场推出前后的算法交易和市场效率

IF 2.1 2区经济学 Q2 BUSINESS, FINANCE

Journal of Financial Markets Pub Date : 2024-04-09 DOI:10.1016/j.finmar.2024.100909

Darya Yuferova

{"title":"纽约证券交易所混合市场推出前后的算法交易和市场效率","authors":"Darya Yuferova","doi":"10.1016/j.finmar.2024.100909","DOIUrl":null,"url":null,"abstract":"<div><p>I study the effect of algorithmic trading on market efficiency, taking into account past market and limit order flows alike. I find that an exogenous increase in algorithmic trading around the introduction of the NYSE Hybrid Market leads to a significant decrease in the predictive power of surprises in market order imbalance and limit order book imbalances, especially at the outer levels of the limit order book. However, the predictive power of past returns remains largely unchanged. This suggests that algorithmic trading improves market efficiency by facilitating the incorporation of information embedded in both market and limit order flows.</p></div>","PeriodicalId":47899,"journal":{"name":"Journal of Financial Markets","volume":"69 ","pages":"Article 100909"},"PeriodicalIF":2.1000,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1386418124000272/pdfft?md5=9a23e0c2025935fdfe0b5d03b5223c13&pid=1-s2.0-S1386418124000272-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Algorithmic trading and market efficiency around the introduction of the NYSE Hybrid Market\",\"authors\":\"Darya Yuferova\",\"doi\":\"10.1016/j.finmar.2024.100909\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>I study the effect of algorithmic trading on market efficiency, taking into account past market and limit order flows alike. I find that an exogenous increase in algorithmic trading around the introduction of the NYSE Hybrid Market leads to a significant decrease in the predictive power of surprises in market order imbalance and limit order book imbalances, especially at the outer levels of the limit order book. However, the predictive power of past returns remains largely unchanged. This suggests that algorithmic trading improves market efficiency by facilitating the incorporation of information embedded in both market and limit order flows.</p></div>\",\"PeriodicalId\":47899,\"journal\":{\"name\":\"Journal of Financial Markets\",\"volume\":\"69 \",\"pages\":\"Article 100909\"},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2024-04-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S1386418124000272/pdfft?md5=9a23e0c2025935fdfe0b5d03b5223c13&pid=1-s2.0-S1386418124000272-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Financial Markets\",\"FirstCategoryId\":\"96\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1386418124000272\",\"RegionNum\":2,\"RegionCategory\":\"经济学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"BUSINESS, FINANCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Financial Markets","FirstCategoryId":"96","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1386418124000272","RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BUSINESS, FINANCE","Score":null,"Total":0}

引用次数: 0

摘要

我研究了算法交易对市场效率的影响，同时考虑了过去的市场订单流和限价订单流。我发现，在纽约证券交易所混合市场推出前后，算法交易的外生性增长导致市场订单失衡和限价订单簿失衡的意外预测能力显著下降，尤其是在限价订单簿的外层。但是，对过去回报的预测能力基本保持不变。这表明，算法交易通过促进市场订单流和限价订单流中蕴含的信息的融入，提高了市场效率。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Algorithmic trading and market efficiency around the introduction of the NYSE Hybrid Market

I study the effect of algorithmic trading on market efficiency, taking into account past market and limit order flows alike. I find that an exogenous increase in algorithmic trading around the introduction of the NYSE Hybrid Market leads to a significant decrease in the predictive power of surprises in market order imbalance and limit order book imbalances, especially at the outer levels of the limit order book. However, the predictive power of past returns remains largely unchanged. This suggests that algorithmic trading improves market efficiency by facilitating the incorporation of information embedded in both market and limit order flows.

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

Journal of Financial Markets BUSINESS, FINANCE-

CiteScore

3.40

自引率

3.60%

发文量

期刊介绍： The Journal of Financial Markets publishes high quality original research on applied and theoretical issues related to securities trading and pricing. Area of coverage includes the analysis and design of trading mechanisms, optimal order placement strategies, the role of information in securities markets, financial intermediation as it relates to securities investments - for example, the structure of brokerage and mutual fund industries, and analyses of short and long run horizon price behaviour. The journal strives to maintain a balance between theoretical and empirical work, and aims to provide prompt and constructive reviews to paper submitters.