nep-mst New Economics Papers
on Market Microstructure
Issue of 2020‒07‒13
five papers chosen by
Thanos Verousis

  1. Coming early to the party By Mario Bellia; Loriana Pelizzon; Marti G. Subrahmanyam; Jun Uno; Darya Yuferova
  2. The Overnight Drift By Boyarchenko, Nina; Larsen, Lars C.; Whelan, Paul
  3. Multi-Agent Reinforcement Learning in a Realistic Limit Order Book Market Simulation By Micha\"el Karpe; Jin Fang; Zhongyao Ma; Chen Wang
  4. C\`adl\`ag semimartingale strategies for optimal trade execution in stochastic order book models By Julia Ackermann; Thomas Kruse; Mikhail Urusov
  5. Foreign Exchange Order Flow as a Risk Factor By Craig Burnside; Mario Cerrato; Zhekai Zhang

  1. By: Mario Bellia (Department of Economics, University Of Venice Cà Foscari; SAFE, Goethe University); Loriana Pelizzon (Department of Economics, University Of Venice Cà Foscari; SAFE, Goethe University); Marti G. Subrahmanyam (Leonard N. Stern School of Business, New York University); Jun Uno (Waseda University; Ca' Foscari University of Venice); Darya Yuferova (Norwegian School of Economics (NHH))
    Abstract: We examine the strategic behavior of High Frequency Traders (HFTs) during the pre-opening phase and the opening auction of the NYSE-Euronext Paris exchange. HFTs actively participate, and profitably extract information from the order flow. They also post “flash crash” orders, to gain time priority. They make profits on their last-second orders; however, so do others, suggesting that there is no speed advantage. HFTs lead price discovery, and neither harm nor improve liquidity. They “come early to the party”, and enjoy it (make profits); however, they also help others enjoy the party (improve market quality) and do not have privileges (their speed advantage is not crucial).
    Keywords: High-Frequency Traders (HFTs), Proprietary Trading, Opening Auction, Liquidity Provision, Price Discovery
    JEL: G12 G14
    Date: 2020
  2. By: Boyarchenko, Nina; Larsen, Lars C.; Whelan, Paul
    Abstract: Since the advent of electronic trading in the mid 1990's, U.S. equities have traded (almost) 24 hours a day through equity index futures. This allows new information to be incorporated continuously into asset prices, yet, we show that almost 100% of the U.S equity premium is earned during a 1-hour window between 2:00 a.m. and 3:00 a.m. (ET) which we dub the "overnight drift". We study explanations for this finding within a framework a la Grossman and Miller (1988) and derive testable predictions linking dealer inventory shocks to high-frequency return predictability. Consistent with the predictions of the model, we document a strong negative relationship between end of day order imbalance, arising from market sell offs, and the overnight drift occurring at the opening of European financial markets. Further, we show that in recent years dealers have increasingly offloaded inventory shocks at the opening of Asian markets and exploit a natural experiment based on daylight savings time to show that Asian offloading shifts by one hour between summer and winter.
    Keywords: Equity Risk; Intraday Immediacy; Inventory management; Overnight Returns
    JEL: G13 G14 G15
    Date: 2020–03
  3. By: Micha\"el Karpe; Jin Fang; Zhongyao Ma; Chen Wang
    Abstract: Optimal order execution is widely studied by industry practitioners and academic researchers because it determines the profitability of investment decisions and high-level trading strategies, particularly those involving large volumes of orders. However, complex and unknown market dynamics pose enormous challenges for the development and validation of optimal execution strategies. We propose a model-free approach by training Reinforcement Learning (RL) agents in a realistic market simulation environment with multiple agents. First, we have configured a multi-agent historical order book simulation environment for execution tasks based on an Agent-Based Interactive Discrete Event Simulation (ABIDES) [arXiv:1904.12066]. Second, we formulated the problem of optimal execution in an RL setting in which an intelligent agent can make order execution and placement decisions based on market microstructure trading signals in HFT. Third, we developed and trained an RL execution agent using the Double Deep Q-Learning (DDQL) algorithm in the ABIDES environment. In some scenarios, our RL agent converges towards a Time-Weighted Average Price (TWAP) strategy. Finally, we evaluated the simulation with our RL agent by comparing the simulation on the actual market Limit Order Book (LOB) characteristics.
    Date: 2020–06
  4. By: Julia Ackermann; Thomas Kruse; Mikhail Urusov
    Abstract: We analyze an optimal trade execution problem in a financial market with stochastic liquidity. To this end we set up a limit order book model in continuous time. Both order book depth and resilience are allowed to evolve randomly in time. We allow for trading in both directions and for c\`adl\`ag semimartingales as execution strategies. We derive a quadratic BSDE that under appropriate assumptions characterizes minimal execution costs and identify conditions under which an optimal execution strategy exists. We also investigate qualitative aspects of optimal strategies such as, e.g., appearance of strategies with infinite variation or existence of block trades and discuss connections with the discrete-time formulation of the problem. Our findings are illustrated in several examples.
    Date: 2020–06
  5. By: Craig Burnside; Mario Cerrato; Zhekai Zhang
    Abstract: This paper proposes a set of novel pricing factors for currency returns that are motivated by microstructure models. In so doing, we bring two strands of the exchange rate literature, namely market-microstructure and risk-based models, closer together. Our novel factors use order flow data to provide direct measures of buying and selling pressure related to carry trading and momentum strategies. We find that they appear to be good proxies for currency crash risk. Additionally, we show that the association between our order-flow factors and currency returns differs according to the customer segment of the foreign exchange market. In particular, it appears that financial customers are risk takers in the market, while non-financial customers serve as liquidity providers.
    JEL: F31 G15
    Date: 2020–05

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