Research Papers

  1. "The Off-Exchange Routing Decision" (Aug. 2019)
    with Bruce Mizrach [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. "NYSE Floor Shutdown and Market Quality into the Close" (Apr. 2022)
    with Bruce Mizrach [PDF]

    Abstract: The spread of Covid-19 forced the New York Stock Exchange (NYSE) to shut down its trading floor from March 23 through May 25, 2020, resulting in complete suspension of floor brokers’ use of D-Orders, a special order type that enables investors to participate in the closing auction beyond 15:50. We investigate its impact on consolidated limit order books over the last 10 minutes into the close 15:50-16:00, the period in which floor brokers become a possible alternative execution channel for investors. Analyzing NYSE- and Nasdaq-listed stocks in the Russell 3000 index in matched-sample and machine learning (ML) approaches, we found that the floor closure had a limited impact on market quality overall: (a) our matched-sample analysis revealed that percentage quoted spread and consolidated displayed depth on average did not differ between the two groups; (b) our ML investigation uncovered that only a small fraction of the NYSE-listed sample stocks experienced wider percentage quoted spread and lower consolidated displayed depth, limited to the first fewweeks of the shutdown.

  3. "Machine Learning from Randomized Experiments: The Case of the Tick Size Pilot Program" (Oct. 2020)

    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.

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