Research Papers

  1. "The Off-Exchange Routing Decision"
    with Bruce Mizrach; submitted; [SSRN]

    Abstract: A rising fraction of U.S. equity trading volume is being executed away from the national stock exchanges on the over-the-counter (OTC) market. This paper develops a theoretical model of the decision to route off-exchange to either alternative trading systems (ATS) platforms and OTC non-ATS dealers for executions. Using data from the Financial Industry Regulatory Authority, we compute 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. We 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. We extend our model to allow for heterogeneity, grouping venues by the probability of finding off-exchange liquidity. When we estimate our model within trade intensity groups, we find that ATS or OTC non-ATS trading centers respond more elastically to exchange liquidity conditions.


  2. "Machine Learning from the Covid-19 Pandemic about the Value of the NYSE Floor in Market Closing Time"
    with Bruce Mizrach; submitted; [SSRN]

    Abstract: The spread of the Covid-19 forced the New York Stock Exchange (NYSE) to temporarily shut down its trading floor between March 23 and May 25, 2020. Using a machine learning approach, we investigate the effects of this Covid-19 closure 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 the period of February to June 2020, we find that the closure of the NYSE floor 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. Our findings suggest that the role of the NYSE floor in the market closing time can be replicated electronically.


  3. "Machine Learning from Randomized Experiments: The Case of the Tick Size Pilot Program"
    job market paper [PDF]

    Abstract: In this paper, I investigate the Tick Size Pilot Program 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 pilot 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: the lower percentage realized spread, the more likely is the null hypothesis rejected, indicating less-profitable stocks for liquidity providers in the pre-intervention periods tend to receive significant effects in the post-intervention periods.

© 2021 Hyungil Kye. All rights reserved.