{"title":"Stock Liquidity and Algorithmic Market Making During the COVID-19 Crisis","authors":"Bidisha Chakrabarty, R. Pascual","doi":"10.2139/ssrn.3857463","DOIUrl":null,"url":null,"abstract":"Much of the liquidity supply in modern markets comes from algorithmic traders (ATs). Prompted by concerns of fragility induced by such voluntary market making, we examine ATs’ liquidity-provision role during the COVID-19 crisis. We find that amidst the turmoil as market liquidity declined, ATs did not (disproportionately) withdraw liquidity supply. Stocks with the highest algorithmic trading (AT) experienced lower liquidity reduction compared to stocks with the lowest AT activity. High AT stocks did not experience greater reduction in either competition for liquidity provision or price improvements than low AT stocks. Multiple tests indicate that high AT did not associate with any greater deterioration in price efficiency vis-à-vis low AT stocks. Stocks in the industries hardest hit by COVID-19 did not see any less AT competition for liquidity supply or price efficiency than stocks in the least affected ones. Overall, our results allay some concerns that the current levels of AT make markets more susceptible to liquidity withdrawal in times of crises. © 2022 Elsevier B.V.","PeriodicalId":11757,"journal":{"name":"ERN: Other Microeconomics: General Equilibrium & Disequilibrium Models of Financial Markets (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ERN: Other Microeconomics: General Equilibrium & Disequilibrium Models of Financial Markets (Topic)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.3857463","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 10
COVID-19危机期间的股票流动性和算法做市
现代市场的大部分流动性供应来自算法交易员(ATs)。出于对这种自愿做市行为引发的脆弱性的担忧,我们研究了ATs在2019冠状病毒病危机期间提供流动性的作用。我们发现,在市场流动性下降的动荡中,ATs并没有(不成比例地)撤回流动性供应。与算法交易(AT)活动最低的股票相比,算法交易(AT)活动最高的股票经历了更低的流动性减少。与低库存相比,高库存在流动性供应或价格改善方面的竞争并没有经历更大的减少。多重检验表明,与-à-vis低AT股票相比,高AT与任何更大的价格效率恶化无关。受新冠疫情影响最严重的行业的股票在流动性供应或价格效率方面的竞争并不比受影响最轻的行业的股票少。总的来说,我们的结果减轻了一些担忧,即当前的AT水平使市场在危机时期更容易受到流动性撤出的影响。©2022 Elsevier B.V.
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