Essays on U.S. stock market microstructure and big data analytics
Description
TitleEssays on U.S. stock market microstructure and big data analytics
Date Created2021
Other Date2021-10 (degree)
SubjectEconomics, Finance, Stock exchanges -- United States, COVID-19 Pandemic, 2020- -- Influence, Big data, Applied econometrics, Applied machine learning, Big data analytics, Causal inference, Market microstructure, U.S. stock market, New York Stock Exchange
Extent1 online resource (xiii, 169 pages) : illustrations
DescriptionThis dissertation conducts empirical research on three important topics in the U.S. stock market microstructure using big data analytics.
The first chapter attempts to uncover the market conditions under which investors seek order executions in over-the-counter (OTC) markets, which by the end of June 2018 account for about 40% of aggregate daily trading volume in the U.S. stock market. To this end, I first develop a theoretical model of the decision to route off-exchange to either alternative trading systems (ATS) platforms or OTC non-ATS dealers for executions. With weekly time series of market shares for all ATS and OTC non-ATS trading centers at the individual stock level in the period of April 4, 2016 - June 30, 2018, I then test the model, using a panel-data instrumental variable approach, and confirm that both ATS and OTC non-ATS market shares increase with bid-ask spreads, decrease with on-exchange depth, and decrease with volatility. I further extend the model to allow for heterogeneity, grouping venues by the probability of finding off-exchange liquidity. When I estimate the model within trade intensity groups, I find that ATS or OTC non-ATS trading centers respond more elastically to exchange liquidity conditions.
The second chapter investigates the Tick Size Pilot Program, a randomized controlled trial, with the goal of policy evaluation beyond average treatment effect. Using a machine learning approach, I study policy effects stock-by-stock on three major market quality measures, percentage quoted spread, consolidated displayed depth, and high-low volatility. For each stock, I test whether it receives significant treatment effects. I find less than half of the pilot stocks in the treatment groups show positive significance for percentage quoted spread; more than 80% shows positive significance for consolidated displayed depth; only less than 5% shows significance for high-low volatility in either direction; the control group stocks rarely show significance for all the outcomes, revealing no spillover effect at the individual level. Tick constrainedness turns out to be useful in explaining differing significance only for percentage quoted spread but not for consolidated displayed depth. Percentage realized spread, though, appears to explain for the both outcomes: less-profitable stocks for liquidity providers in the pre-intervention periods are more likely to receive significant effects in the post-intervention periods.
The third chapter looks into the Covid-19 shutdown of the New York Stock Exchange (NYSE) trading floor that happened in March 23 - May 25, 2020. Using a machine learning approach, I quantify the effects of the shutdown on market quality during the market closing time, 3:50 - 4:00 pm. Analyzing NYSE- and Nasdaq-listed stocks in the Russell 3000 index for February - June 2020, I find that the closure of the NYSE floor overall has limited impacts on market quality for the NYSE-listed stocks: Percentage quoted spread and spot volatility for the NYSE-listed stocks increase relative to the Nasdaq-listed stocks only in the first three weeks of the floor closure, and it has no impact on consolidated displayed depth for the whole shutdown period. As far as those three market quality measures are concerned, my findings suggest that the role of the NYSE floor in the market closing time is replaceable by the electronic trading setup, configured for the Covid-19 shutdown.
NotePh.D.
NoteIncludes bibliographical references
Genretheses
LanguageEnglish
CollectionSchool of Graduate Studies Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
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