
on Market Microstructure 
By:  Matteo Aquilina; Sean Foley; Peter O'Neill; Matteo Thomas Ruf 
Abstract:  We investigate stale reference pricing and liquidity provision in dark pools using proprietary, participantlevel regulatory data. We show a substantial amount of stale trading occurs, imposing large costs on passive dark pool participants. Consistent with these costs, HFTs almost never provide liquidity in the dark, instead frequently consuming liquidity, in particular in order to take advantage of stale reference prices. Finally, we show that market design interventions randomizing dark execution times are successful at countering dark pool latency arbitrage, protecting passive providers of dark liquidity. Our results have substantial implications for practitioners and policymakers aiming to improve liquidity provision in dark pools. 
Keywords:  highfrequency trading, dark pools, latency arbitrage, stale quotes, reference prices 
JEL:  D47 G10 G14 
Date:  2023–08 
URL:  http://d.repec.org/n?u=RePEc:bis:biswps:1115&r=mst 
By:  Ulrich Horst; D\"orte Kreher; Konstantins Starovoitovs 
Abstract:  We establish a first and secondorder approximation for an infinite dimensional limit order book model (LOB) in a single (''critical'') scaling regime where market and limit orders arrive at a common time scale. With our choice of scaling we obtain nondegenerate firstorder and secondorder approximations for the price and volume dynamics. While the firstorder approximation is given by a standard coupled ODEPDE system, the secondorder approximation is nonstandard and described in terms of an infinitedimensional stochastic evolution equation driven by a cylindrical Brownian motion. The driving noise processes exhibit a nontrivial correlation in terms of the model parameters. We prove that the evolution equation has a unique solution and that the sequence of standardized LOB models converges weakly to the solution of the evolution equation. The proof uses a nonstandard martingale problem. We calibrate a simplified version of our model to market data and show that the model accurately captures correlations between price and volume fluctuations. 
Date:  2023–08 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:2308.00805&r=mst 
By:  Crocker Herbert Liu (Cornell University); Charles Trzcinka (Indiana University); Ziwei Zhao (University of Lausanne; Swiss Finance Institute) 
Abstract:  Chinese firms can initiate trading halts. While many plausible reasons exist for halts to occur after a price decline: 42% of halts come after a 7day price rise. We argue the only reason for halts after a price rise is to increase management information. We find that our measures of private information are negatively associated with the likelihood of a halt. However, halts increase the cost of capital by 121 basis points. We show that price nonsynchronicity, institutional ownership, and accounting variables predict a trading halt and explain the positive CARs after a halt. 
Keywords:  trading, halts, fundamentals, noise traders, liquidity 
JEL:  E44 G12 G14 N20 O16 O53 
Date:  2023–08 
URL:  http://d.repec.org/n?u=RePEc:chf:rpseri:rp2362&r=mst 
By:  Yuki Sato; Kiyoshi Kanazawa 
Abstract:  In this research, we focus on the ordersplitting behavior. The order splitting is a trading strategy to execute their large potential metaorder into small pieces to reduce transaction cost. This strategic behavior is believed to be important because it is a promising candidate for the microscopic origin of the longrange correlation (LRC) in the persistent order flow. Indeed, in 2005, Lillo, Mike, and Farmer (LMF) introduced a microscopic model of the ordersplitting traders to predict the asymptotic behavior of the LRC from the microscopic dynamics, even quantitatively. The plausibility of this scenario has been qualitatively investigated by Toth et al. 2015. However, no solid support has been presented yet on the quantitative prediction by the LMF model in the lack of large microscopic datasets. In this report, we have provided the first quantitative statistical analysis of the ordersplitting behavior at the level of each trading account. We analyse a large dataset of the Tokyo stock exchange (TSE) market over nine years, including the account data of traders (called virtual servers). The virtual server is a unit of trading accounts in the TSE market, and we can effectively define the trader IDs by an appropriate preprocessing. We apply a strategy clustering to individual traders to identify the ordersplitting traders and the random traders. For most of the stocks, we find that the metaorder length distribution obeys power laws with exponent $\alpha$, such that $P(L)\propto L^{\alpha1}$ with the metaorder length $L$. By analysing the sign correlation $C(\tau)\propto \tau^{\gamma}$, we directly confirmed the LMF prediction $\gamma \approx \alpha1$. Furthermore, we discuss how to estimate the total number of the splitting traders only from public data via the ACF prefactor formula in the LMF model. Our work provides the first quantitative evidence of the LMF model. 
Date:  2023–08 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:2308.01112&r=mst 