nep-mst New Economics Papers
on Market Microstructure
Issue of 2022‒12‒05
five papers chosen by
Thanos Verousis

  1. The Profitability of Lead-Lag Arbitrage at High-Frequency By Poutré, Cédric; Dionne, Georges; Yergeau, Gabriel
  2. Are Front-running HFTs Harmful? By Ziyi Xu; Xue Cheng
  3. How Liquid Has the Treasury Market Been in 2022? By Michael J. Fleming; Claire Nelson
  4. Clients’ connections: measuring the role of private information in decentralized markets By Kondor, Peter; Pinter, Gabor
  5. Predictive Crypto-Asset Automated Market Making Architecture for Decentralized Finance using Deep Reinforcement Learning By Tristan Lim

  1. By: Poutré, Cédric (University of Montreal); Dionne, Georges (HEC Montreal, Canada Research Chair in Risk Management); Yergeau, Gabriel (HEC Montreal, Canada Research Chair in Risk Management)
    Abstract: Any lead-lag effect in an asset pair implies the future returns on the lagging asset have the potential to be predicted from past and present prices of the leader, thus creating statistical arbitrage opportunities. We utilize robust lead-lag indicators to uncover the origin of price discovery and we propose an econometric model exploiting that effect with level 1 data of limit order books (LOB). We also develop a high-frequency trading strategy based on the model predictions to capture arbitrage opportunities. The framework is then evaluated on six months of DAX 30 cross-listed stocks’ LOB data obtained from three European exchanges in 2013: Xetra, Chi-X, and BATS. We show that a high-frequency trader can profit from lead-lag relationships because of predictability, even when trading costs, latency d execution-related risks are considered.
    Keywords: Lead-lag relationship; High-frequency trading; Statistical arbitrage; Limit order book; Cross-listed stocks; Econometric models.
    JEL: C25 C53 C58 G10 G14 G15 G17
    Date: 2022–09–14
  2. By: Ziyi Xu; Xue Cheng
    Abstract: This paper is concerned with how a front-running high-frequency trader (HFT) influences the large trader: whether and under what conditions the latter is harmed or benefited. We study, in the extended Kyle's model, the interactions between a large informed trader and an HFT who can predict the former's incoming order in some extent. Equilibria under various specific situations are discussed. We conclude that HFT always front-runs and the large trader could be favored in the following circumstances: (1) there is sufficient noise trading with HFT's liquidity-consuming trading; (2) the noise trading with HFT's liquidity-consuming trading is inadequate but HFT's signal is vague enough. Besides, we explore the influences of market noise and signal noise on investors' behavior and profits. We find surprisingly that (1) increasing the noise in HFT's signal might decrease the large trader's profit; (2) when there are few market noises, although HFT nearly does nothing, the large trader is still hurt; (3) in any case, HFT will not front-run more than half of the large trader's order.
    Date: 2022–11
  3. By: Michael J. Fleming; Claire Nelson
    Abstract: Policymakers and market participants are closely watching liquidity conditions in the U.S. Treasury securities market. Such conditions matter because liquidity is crucial to the many important uses of Treasury securities in financial markets. But just how liquid has the market been and how unusual is the liquidity given the higher-than-usual volatility? In this post, we assess the recent evolution of Treasury market liquidity and its relationship with price volatility and find that while the market has been less liquid in 2022, it has not been unusually illiquid after accounting for the high level of volatility.
    Keywords: Treasury market; liquidity; bid-ask spreads; price impact; depth
    JEL: G1
    Date: 2022–11–15
  4. By: Kondor, Peter; Pinter, Gabor
    Abstract: We propose a new measure of private information in decentralized markets—connections—which exploits the time variation in the number of dealers with whom a client trades in a time period. Using trade-level data for the U.K. government bond market, we show that clients perform better when having more connections as their trades predict future price movements. Time variation in market-wide connections also helps explain yield dynamics. Given our novel measure, we present two applications suggesting that (i) dealers pass on information, acquired from their informed clients, to their affiliates, and (ii) informed clients better predict the orderflow intermediated by their dealers.
    JEL: F3 G3
    Date: 2022–02–01
  5. By: Tristan Lim
    Abstract: The study proposes a quote-driven predictive automated market maker (AMM) platform with on-chain custody and settlement functions, alongside off-chain predictive reinforcement learning capabilities to improve liquidity provision of real-world AMMs. The proposed AMM architecture is an augmentation to the Uniswap V3, a cryptocurrency AMM protocol, by utilizing a novel market equilibrium pricing for reduced divergence and slippage loss. Further, the proposed architecture involves a predictive AMM capability, utilizing a deep hybrid Long Short-Term Memory (LSTM) and Q-learning reinforcement learning framework that looks to improve market efficiency through better forecasts of liquidity concentration ranges, so liquidity starts moving to expected concentration ranges, prior to asset price movement, so that liquidity utilization is improved. The augmented protocol framework is expected have practical real-world implications, by (i) reducing divergence loss for liquidity providers, (ii) reducing slippage for crypto-asset traders, while (iii) improving capital efficiency for liquidity provision for the AMM protocol. To our best knowledge, there are no known protocol or literature that are proposing similar deep learning-augmented AMM that achieves similar capital efficiency and loss minimization objectives for practical real-world applications.
    Date: 2022–09

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