nep-mst New Economics Papers
on Market Microstructure
Issue of 2021‒04‒19
six papers chosen by
Thanos Verousis

  1. Time is Money: The Equilibrium Trading Horizon and Optimal Arrival Price By Kevin Patrick Darby
  2. Political Insider Trading: A narrow versus comprehensive approach By Jan Hanousek; Christos Pantzalis; Jung Chul Park
  3. Order Protection through Delayed Messaging By Aldrich, Eric M; Friedman, Daniel
  4. Optimal Market Making by Reinforcement Learning By Matias Selser; Javier Kreiner; Manuel Maurette
  5. Do ETFs increase the commonality in liquidity of underlying stocks? By Agarwal, Vikas; Hanouna, Paul; Moussawi, Rabih; Stahel, Christof W.
  6. Do ETFs increase liquidity? By Saæglam, Mehmet; Tuzun, Tugkan; Wermers, Russ

  1. By: Kevin Patrick Darby
    Abstract: Executing even moderately large derivatives orders can be expensive and risky; it's hard to balance the uncertainty of working an order over time versus paying a liquidity premium for immediate execution. Here, we introduce the Time Is Money model, which calculates the Equilibrium Trading Horizon over which to execute an order within the adversarial forces of variance risk and liquidity premium. We construct a hypothetical at-the-money option within Arithmetic Brownian Motion and invert the Bachelier model to compute an inflection point between implied variance and liquidity cost as governed by a central limit order book, each in real time as they evolve. As a result, we demonstrate a novel, continuous-time Arrival Price framework. Further, we argue that traders should be indifferent to choosing between variance risk and liquidity cost, unless they have a predetermined bias or an exogenous position with a convex payoff. We, therefore, introduce half-life factor asymptotics to the model based on a convexity factor and compare results to existing models. We also describe a specialization of the model for trading a basket of correlated instruments, as exemplified by a futures calendar spread. Finally, we establish groundwork for microstructure optimizations as well as explore short term drift and conditional expected slippage within the Equilibrium Horizon framework.
    Date: 2021–04
  2. By: Jan Hanousek (Department of Finance, University of South Florida, Tampa, FL 33620, Faculty of Business and Economics, Department of Finance, Mendel University in Brno, Czech Republic); Christos Pantzalis (Kate Tiedemann School of Business and Finance, Muma College of Business, BSN3403, University of South Florida, Tampa, FL 33620); Jung Chul Park (Kate Tiedemann School of Business and Finance, Muma College of Business, BSN3403, University of South Florida, Tampa, FL 33620)
    Abstract: We examine senators’ electronically filed stock transactions between 2012 and 2019 to assess the extent of politician’s insider trading. Our results suggest that senators use inside political information when investing and earn significant market-adjusted returns. To extend traditional return-based methods, we propose a new comprehensive approach based on abnormal idiosyncratic volatility (AIV), which captures the degree of information asymmetry around their trading dates. We document that senator trades are associated with substantially high levels of AIV, suggesting that they represent only a tip of the iceberg, since the mass of unfiled transactions using the same inside information remains undetected.
    Keywords: Abnormal idiosyncratic volatility, legislator’s trading, politician’s insider trading, STOCK Act
    JEL: C58 G12 G14 G28
    Date: 2021–04
  3. By: Aldrich, Eric M; Friedman, Daniel
    Keywords: Market design, high-frequency trading, continuous double auction, IEX, lab experiments
    Date: 2019–06–29
  4. By: Matias Selser; Javier Kreiner; Manuel Maurette
    Abstract: We apply Reinforcement Learning algorithms to solve the classic quantitative finance Market Making problem, in which an agent provides liquidity to the market by placing buy and sell orders while maximizing a utility function. The optimal agent has to find a delicate balance between the price risk of her inventory and the profits obtained by capturing the bid-ask spread. We design an environment with a reward function that determines an order relation between policies equivalent to the original utility function. When comparing our agents with the optimal solution and a benchmark symmetric agent, we find that the Deep Q-Learning algorithm manages to recover the optimal agent.
    Date: 2021–04
  5. By: Agarwal, Vikas; Hanouna, Paul; Moussawi, Rabih; Stahel, Christof W.
    Abstract: We examine the impact of ETF ownership on the commonality in liquidity of underlying stocks, while controlling for other institutional ownership. Analyses using aggregate stock-level ETF ownership and common ETF ownership at the stock-pair level indicate that ETF ownership significantly increases commonality. We show that greater arbitrage activities are associated with a larger effect of ETF ownership on commonality. We use quasi-natural experiments that exploit the reconstitution of Russell indexes, and ETF trading halts, to establish the causal effect of ETF ownership and the arbitrage mechanism, respectively. Our results suggest that ETFs reduce investors' ability to diversify liquidity risk.
    Keywords: Exchange-Traded Funds (ETFs),Liquidity,Commonality,Arbitrage,Trading Halts,Index Reconstitution
    JEL: G10 G12 G14 G23
    Date: 2021
  6. By: Saæglam, Mehmet; Tuzun, Tugkan; Wermers, Russ
    Abstract: This paper investigates the impact of exchange-traded funds (ETFs) on the liquidity of their underlying stockholdings. Using a difference-in-differences methodology for large changes in the index weights of stocks in the S&P 500 and NASDAQ 100 indexes, we find that increases in ETF ownership are associated with increases in commonly used measures of liquidity. Stocks with high ETF ownership have higher price resilience and lower adverse selection costs. However, ETFs are linked to higher liquidation costs during the 2011 U.S. debt-ceiling crisis, suggesting that stocks with high ETF ownership may experience impaired liquidity during major market stress events.
    Date: 2021

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