nep-mst New Economics Papers
on Market Microstructure
Issue of 2022‒05‒02
three papers chosen by
Thanos Verousis

  1. Frequent batch auctions and informed trading By Eibelshäuser, Steffen; Smetak, Fabian
  2. Bayesian Bilinear Neural Network for Predicting the Mid-price Dynamics in Limit-Order Book Markets By Martin Magris; Mostafa Shabani; Alexandros Iosifidis
  3. A mean-field game of market-making against strategic traders By Bastien Baldacci; Philippe Bergault; Dylan Possama\"i

  1. By: Eibelshäuser, Steffen; Smetak, Fabian
    Abstract: We study liquidity provision by competitive high-frequency trading firms (HFTs) in a dynamic trading model with private information. Liquidity providers face adverse selection risk from trading with privately informed investors and from trading with other HFTs that engage in latency arbitrage upon public information. The impact of the two different sources of risk depends on the details of the market design. We determine equilibrium transaction costs in continuous limit order book (CLOB) markets and under frequent batch auctions (FBA). In the absence of informed trading, FBA dominates CLOB just as in Budish et al. (2015). Surprisingly, this result does no longer hold with privately informed investors. We show that FBA allows liquidity providers to charge markups and earn profits - even under risk neutrality and perfect competition. A slight variation of the FBA design removes the inefficiency by allowing traders to submit orders conditional on auction excess demand.
    Keywords: market design,market microstructure,liquidity provision,high-frequency trading,continuous limit order book,frequent batch auctions,sniping,latency arbitrage
    JEL: G10 D47
    Date: 2022
  2. By: Martin Magris; Mostafa Shabani; Alexandros Iosifidis
    Abstract: The prediction of financial markets is a challenging yet important task. In modern electronically-driven markets traditional time-series econometric methods often appear incapable of capturing the true complexity of the multi-level interactions driving the price dynamics. While recent research has established the effectiveness of traditional machine learning (ML) models in financial applications, their intrinsic inability in dealing with uncertainties, which is a great concern in econometrics research and real business applications, constitutes a major drawback. Bayesian methods naturally appear as a suitable remedy conveying the predictive ability of ML methods with the probabilistically-oriented practice of econometric research. By adopting a state-of-the-art second-order optimization algorithm, we train a Bayesian bilinear neural network with temporal attention, suitable for the challenging time-series task of predicting mid-price movements in ultra-high-frequency limit-order book markets. By addressing the use of predictive distributions to analyze errors and uncertainties associated with the estimated parameters and model forecasts, we thoroughly compare our Bayesian model with traditional ML alternatives. Our results underline the feasibility of the Bayesian deep learning approach and its predictive and decisional advantages in complex econometric tasks, prompting future research in this direction.
    Date: 2022–03
  3. By: Bastien Baldacci; Philippe Bergault; Dylan Possama\"i
    Abstract: We design a market-making model \`a la Avellaneda-Stoikov in which the market-takers act strategically, in the sense that they design their trading strategy based on an exogenous trading signal. The market-maker chooses her quotes based on the average market-takers' behaviour, modelled through a mean-field interaction. We derive, up to the resolution of a coupled HJB--Fokker--Planck system, the optimal controls of the market-maker and the representative market-taker. This approach is flexible enough to incorporate different behaviours for the market-takers and takes into account the impact of their strategies on the price process.
    Date: 2022–03

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