Limit Order Research Articles

Strong CP problem in the quantum rotor

Recent studies have claimed that the strong CP problem does not occur in QCD, proposing a new order of limits in volume and topological sectors when studying observables on the lattice. In order to shed light on this issue, we study the effect of the topological θ-term on a simple quantum mechanical rotor that allows a lattice description. The topological susceptibility and the θ-dependence of the energy spectrum are both computed using local lattice correlation functions. The sign problem is overcome by considering Taylor expansions in θ, exploiting automatic differentiation methods for Monte Carlo processes. Our findings confirm the conventional wisdom on the strong CP problem. Published by the American Physical Society 2024

Hydrogen desorption kinetics of hafnium hydride powders

The kinetics of hydrogen gas release from hafnium hydride are investigated by combining experiments and density functional theory. The material is a candidate neutron moderator and attenuator for compact nuclear reactors, where hydrogen release will lead to a degradation in moderating function. Experimentally, we have studied the decomposition of epsilon phase (HfH2-x) powders from 25-1000°C using thermogravimetry and X-ray diffraction. Isochronal heating reveals 3 characteristic desorption regions corresponding to the release of hydrogen from each phase (ε-HfH2-x, δ-HfH1.6-x and α-Hf), at ∼ 350, 415, and 700°C. These results are well supported by the modelling output from density functional theory. A Kissinger analysis allowed for activation energies for desorption to be calculated (∼150 kJ/mol, 170 kJ/mol and 90 kJ/mol respectively). The peak shape and desorption rate data suggests that a second order diffusion limited reaction controls the ε→ε+δ desorption, a first order interface limited reaction controls the ε+δ→δ reaction, and a surface limited zeroth order reaction limits the complete desorption of the δ+α phase. The analysis suggests that, at least for δ→α regime, engineering solutions for improved thermal stability should focus on reductions in surface reactivity.

Reinforcement learning and rational expectations equilibrium in limit order markets

This paper shows that simple payoff-based reinforcement learning can help to achieve rational expectations equilibrium in limit order markets. In equilibrium, speculators mainly supply liquidity, while liquidity consumption increases in the private values of no-speculators with intrinsic motives for trade. Driven by information acquisition of the non-speculators, liquidity consumption is hump-shaped in fundamental volatility for the speculators but U-shaped for the non-speculators. In contrast, liquidity supply decreases in fundamental volatility for the speculators but is hump-shaped for the non-speculators. Unlike the informed traders who trade on asset fundamentals, the uninformed traders trade more on order book and trading information.

Does maker-taker limit order subsidy improve market outcomes? Quasi-natural experimental evidence

We provide a new theory of exchange access fees that explains why fees relatively reduce the probability of execution and increase the limit order queue length on “maker-taker” platforms. Nonetheless, the limit order subsidy greatly improves market depth, together with market efficiency and trading volume. Moreover, fee structures never “wash out” regardless of the minimum tick. The regulatory requirement that trading and order flow depend only on raw (nominal) spreads and prices underpins the multi-billion-dollar subsidy to limit orders. So long as a platform remains competitive, elimination of the fee structure does not alter the raw spread, but it does lower the cum fee spread. We test these implications with a unilateral maker-taker fee/rebate reduction using NASDAQ's “quasi-natural” $1.9 trillion experiment to find support for our theory.

Long short-term temporal fusion transformer for short-term forecasting of limit order book in China markets

Long short-term temporal fusion transformer for short-term forecasting of limit order book in China markets

High Price Impact Trade Identification and Its Implication for Volatility and Price Efficiency

We include limit order book (LOB) matchedness as a new trade attribute to identify high price impact trades (HPITs). HPITs are trades associated with large price changes relative to their volume proportion. We show that the inclusion of matchedness provides a finer analysis of the relationship between price contribution and trade categories. We further verify that a stronger presence of HPITs leads to a decline in volatility and improves price efficiency, which suggests a link between HPITs and informed trades.

Price predictability in limit order book with deep learning model

ABSTRACT This study explores the prediction of high-frequency price changes using deep learning models. Although state-of-the-art methods perform well, their complexity impedes the understanding of successful predictions. We found that an inadequately defined target price process may render predictions meaningless by incorporating past information. The commonly used three-class problem in asset price prediction can generally be divided into volatility and directional prediction. When relying solely on the price process, directional prediction performance is not substantial. However, volume imbalance improves directional prediction performance.

Limit Order Book dynamics and order size modelling using Compound Hawkes Process

Hawkes Process has been used to model Limit Order Book (LOB) dynamics in several ways in the literature however the focus has been limited to capturing the inter-event times while the order size is usually assumed to be constant. We propose a novel methodology of using Compound Hawkes Process for the LOB where each event has an order size sampled from a calibrated distribution. The process is formulated in a novel way such that the spread of the process always remains positive. Further, we condition the model parameters on time of day to support empirical observations. We make use of an enhanced non-parametric method to calibrate the Hawkes kernels and allow for inhibitory cross-excitation kernels. We showcase the results and quality of fits for an equity stock’s LOB in the NASDAQ exchange and compare them against several baselines. Finally, we conduct a market impact study of the simulator and show the empirical observation of a concave market impact function is indeed replicated.

BMI changes among U.S. Navy and Marine Corps active-duty service members during the COVID-19 pandemic, 2019–2021

BackgroundThe COVID-19 pandemic disrupted the daily life and routines of Americans across the United States (U.S.), including those of our active-duty service members (ADSMs). Limited movement orders enacted during this time to promote social distancing prohibited access to fitness and dining facilities for ADSMs. This study aims to expand on previous work identifying changes in body mass index (BMI) among U.S. Army service members by identifying changes in body mass index (BMI) among active-duty service members from both the Navy and Marine Corps during the same time period.MethodsWe conducted a retrospective cohort study of active-duty service members from the Navy and Marine Corps using data from the Military Health System Data Repository. BMI was calculated and categorized according to CDC guidelines both before (February 2019 - January 2020) and during the pandemic (September 2020 - June 2021). Women who were pregnant or delivered during and one year prior to the study periods were excluded. Statistical analyses included paired t-tests evaluating mean BMI, percent change, and the Stuart-Maxwell test for marginal homogeneity.ResultsWe identified 98,330 active-duty Sailors and 55,298 active-duty Marines for inclusion in this study. During the pandemic period the percentage of Sailors with Underweight decreased by 11%, Healthy weight decreased by 11.1%, Overweight increased by 2.1%, and the percentage of Sailors with Obesity increased by 16.5%. During this same time period, Marines with Underweight decreased by 1%, Healthy weight decreased by 16%, Overweight increased by 3.0%, and Marines with Obesity increased by 51%. The largest increases in service members with overweight and obesity observed among both cohorts were among female service members, service members under age 20, and service members with a Junior Enlisted rank.ConclusionsSignificant increases in obesity were observed amongst active-duty United States Navy and Marine Corps service members during DoD pandemic mitigation efforts. Increased rates of obesity likely effected fitness and force readiness. Future interventions should be targeted at younger, Junior-Enlisted Marines and Sailors to promote healthy lifestyles and provide education on nutrition, appropriate exercise, sleep hygiene, and stress management.

Theories of governance and development: How does India’s experience fit?

ABSTRACT This article examines the apparent paradox of India’s combination of durable democracy and electoral accountability with a capable bureaucracy, coinciding with a deficient development trajectory. It outlines the nature of the conceptual problem, where India is an outlier in global patterns. Then it compares different conceptual approaches to drawing connections from governance institutions to development outcomes. Next, it reviews some of the contributions to understanding India’s political economy in recent decades. Finally, it draws lessons from India’s experience for the appropriateness of different theories of governance and development. One conclusion is that the paradox recedes when attention is paid to the subnational level, where India’s states encompass considerable variation in initial societal and economic conditions and in development outcomes, and a sufficiently rich theoretical framework, that of limited access orders, is used to situate and encompass the different subnational experiences.

An unbounded intensity model for point processes

We develop a model for point processes on the real line, where the intensity can be locally unbounded without inducing an explosion. In contrast to an orderly point process, for which the probability of observing more than one event over a short time interval is negligible, the bursting intensity causes an extreme clustering of events around the singularity. We propose a nonparametric approach to detect such bursts in the intensity. It relies on a heavy traffic condition, which admits inference for point processes over a finite time interval. With Monte Carlo evidence, we show that our testing procedure exhibits size control under the null, whereas it has high rejection rates under the alternative. We implement our approach on high-frequency data for the EUR/USD spot exchange rate, where the test statistic captures abnormal surges in trading activity. We detect a nontrivial amount of intensity bursts in these data and describe their basic properties. Trading activity during an intensity burst is positively related to volatility, illiquidity, and the probability of observing a drift burst. The latter effect is reinforced if the order flow is imbalanced or the price elasticity of the limit order book is large.

Spatiotemporal patterns of leaf nutrients of wild apples in a wild fruit forest plot in the Ili Valley, China

Malus sieversii, commonly known as wild apples, represents a Tertiary relict plant species and serves as the progenitor of globally cultivated apple varieties. Unfortunately, wild apple populations are facing significant degradation in localized areas due to a myriad of factors. To gain a comprehensive understanding of the nutrient status and spatiotemporal variations of M. sieversii, green leaves were collected in May and July, and the fallen leaves were collected in October. The concentrations of leaf nitrogen (N), phosphorus (P), and potassium (K) were measured, and the stoichiometric ratios as well as nutrient resorption efficiencies were calculated. The study also explored the relative contributions of soil, topographic, and biotic factors to the variation in nutrient traits. The results indicate that as the growing period progressed, the concentrations of N and P in the leaves significantly decreased (P < 0.05), and the concentration of K in October was significantly lower than in May and July. Throughout plant growth, leaf N–P and N–K exhibited hyperallometric relationships, while P–K showed an isometric relationship. Resorption efficiency followed the order of N < P < K (P < 0.05), with all three ratios being less than 1; this indicates that the order of nutrient limitation is K > P > N. The resorption efficiencies were mainly regulated by nutrient concentrations in fallen leaves. A robust spatial dependence was observed in leaf nutrient concentrations during all periods (70.1–97.9% for structural variation), highlighting that structural variation, rather than random factors, dominated the spatial variation. Nutrient resorption efficiencies (NRE, PRE, and KRE) displayed moderate structural variation (30.2–66.8%). The spatial patterns of nutrient traits varied across growth periods, indicating they are influenced by multifactorial elements (in which, soil property showed the highest influence). In conclusion, wild apples manifested differentiated spatiotemporal variability and influencing factors across various leaf nutrient traits. These results provide crucial insights into the spatiotemporal patterns and influencing factors of leaf nutrient traits of M. sieversii at the permanent plot scale for the first time. This work is of great significance for the ecosystem restoration and sustainable management of degrading wild fruit forests.

Risk-Averse Markov Decision Processes Through a Distributional Lens

By adopting a distributional viewpoint on law-invariant convex risk measures, we construct dynamic risk measures (DRMs) at the distributional level. We then apply these DRMs to investigate Markov decision processes, incorporating latent costs, random actions, and weakly continuous transition kernels. Furthermore, the proposed DRMs allow risk aversion to change dynamically. Under mild assumptions, we derive a dynamic programming principle and show the existence of an optimal policy in both finite and infinite time horizons. Moreover, we provide a sufficient condition for the optimality of deterministic actions. For illustration, we conclude the paper with examples from optimal liquidation with limit order books and autonomous driving. Funding: This work was supported by Natural Sciences and Engineering Research Council of Canada [Grants RGPAS-2018-522715 and RGPIN-2018-05705].

Failures in Ukrainian Arms Procurement 2014–2023

This article discusses critical failures in Ukrainian defense production and procurement in the period 2014 to 2023. The main puzzle is why Ukraine did so little to satisfy the needs of the country’s armed forces in essential segments of military capabilities when there seems to have been no disagreement on their necessity. Furthermore, both technological and financial resources appear to have been present. The main argument is that Ukraine’s status as a limited access order (LAO) is an important part of the explanation. The empirical evidence in support of this conclusion comes from process-tracing production and procurement in the three areas of ammunition, unmanned aerial vehicles, and armored vehicles. The initial condition of an LAO is linked to the outcome of production and procurement failure through a detailed investigation of the actors and processes that took place within each of the mentioned segments. It is primarily a theory-guided single-outcome study, but the findings may serve as inspiration for more generic studies of the effects of LAOs on national security.

Equity auction dynamics: latent liquidity models with activity acceleration

Equity auctions display several distinctive characteristics in contrast to continuous trading. As the auction time approaches, the rate of events accelerates causing a substantial liquidity buildup around the indicative price. This, in turn, results in a reduced price impact and decreased volatility of the indicative price. In this study, we adapt the latent/revealed order book framework to the specifics of equity auctions. We provide precise measurements of the model parameters, including order submissions, cancelations, and diffusion rates. Our setup allows us to describe the full dynamics of the average order book during closing auctions in Euronext Paris. These findings support the relevance of the latent liquidity framework in describing limit order book dynamics. Lastly, we analyze the factors contributing to a sub-diffusive indicative price and demonstrate the absence of indicative price predictability.

Probabilistic Models and Statistics for Electronic Financial Markets in the Digital Age

The scope of this manuscript is to review some recent developments in statistics for discretely observed semimartingales which are motivated by applications for financial markets. Our journey through this area stops to take closer looks at a few selected topics discussing recent literature. We moreover highlight and explain the important role played by some classical concepts of probability and statistics. We focus on three main aspects: Testing for jumps; rough fractional stochastic volatility; and limit order microstructure noise. We review jump tests based on extreme value theory and complement the literature proposing new statistical methods. They are based on asymptotic theory of order statistics and the Rényi representation. The second stage of our journey visits a recent strand of research showing that volatility is rough. We further investigate this and establish a minimax lower bound exploring frontiers to what extent the regularity of latent volatility can be recovered in a more general framework. Finally, we discuss a stochastic boundary model with one-sided microstructure noise for high-frequency limit order prices and its probabilistic and statistical foundation.

Graphene Multiplexed Sensor for Point-of-Need Viral Wastewater-Based Epidemiology.

Wastewater-based epidemiology (WBE) can help mitigate the spread of respiratory infections through the early detection of viruses, pathogens, and other biomarkers in human waste. The need for sample collection, shipping, and testing facilities drives up the cost of WBE and hinders its use for rapid detection and isolation in environments with small populations and in low-resource settings. Given the ubiquitousness and regular outbreaks of respiratory syncytial virus, SARS-CoV-2, and various influenza strains, there is a rising need for a low-cost and easy-to-use biosensing platform to detect these viruses locally before outbreaks can occur and monitor their progression. To this end, we have developed an easy-to-use, cost-effective, multiplexed platform able to detect viral loads in wastewater with several orders of magnitude lower limit of detection than that of mass spectrometry. This is enabled by wafer-scale production and aptamers preattached with linker molecules, producing 44 chips at once. Each chip can simultaneously detect four target analytes using 20 transistors segregated into four sets of five for each analyte to allow for immediate statistical analysis. We show our platform's ability to rapidly detect three virus proteins (SARS-CoV-2, RSV, and Influenza A) and a population normalization molecule (caffeine) in wastewater. Going forward, turning these devices into hand-held systems would enable wastewater epidemiology in low-resource settings and be instrumental for rapid, local outbreak prevention.

Effective field theory and inelastic dark matter results from XENON1T

In this work, we expand on the XENON1T nuclear recoil searches to study the individual signals of dark matter interactions from operators up to dimension eight in a chiral effective field theory (ChEFT) and a model of inelastic dark matter (iDM). We analyze data from two science runs of the XENON1T detector totaling 1 t×yr exposure. For these analyses, we extended the region of interest from [4.9,40.9] keVNR to [4.9,54.4] keVNR to enhance our sensitivity for signals that peak at nonzero energies. We show that the data are consistent with the background-only hypothesis, with a small background overfluctuation observed peaking between 20 and 50 keVNR, resulting in a maximum local discovery significance of 1.7σ for the Vector⊗Vectorstrange ChEFT channel for a dark matter particle of 70 GeV/c2 and 1.8σ for an iDM particle of 50 GeV/c2 with a mass splitting of 100 keV/c2. For each model, we report 90% confidence level upper limits. We also report upper limits on three benchmark models of dark matter interaction using ChEFT where we investigate the effect of isospin-breaking interactions. We observe rate-driven cancellations in regions of the isospin-breaking couplings, leading to up to 6 orders of magnitude weaker upper limits with respect to the isospin-conserving case. Published by the American Physical Society 2024

Thermodynamic, structural, surface and transport properties of Au-Cu melt

Thermodynamic, structural, surface and transport properties of Au-Cu liquid alloy were calculated on the basis of different theoretical modeling equations. The thermodynamic properties, such as excess Gibbs free energy of mixing, enthapy of mixing and activity were estimated and compared with the available experimental and literature data on the basis of simple theory of mixing (STM). The best fit values of model parameters were estimated using the experimental values of excess Gibbs free energy of mixing and enthalpy of mixing of the system at 1550 K. Using the same model parameters and frame, the concentration fluctuation in long wave-length limit and Warren-Cowley short range order parameter were calculated and analysed to understand the local arrangement of atoms in the liquid mixture. The surface tension and extent of surface segregation of atoms in the initial melt were computed using Butler’s model. In transport properties, the ratio of mutual to intrinsic diffusion coefficients was calculated using STM and viscosity was calculated using Kaptay equation. The system showed complete ordering tendency at its melting temperature.

The correlated trading and investment performance of individual investors

Individual investors tend to trade in the same direction as other individual investors in the same broker branch. The more pronounced an individual investor's herding behavior, the worse his/her investment performance. We find that the limit orders of herding investors have a lower execution ratio, a longer time-to-execution, and a higher probability of being picked up by institutional investors, indicating that their orders are subject to the pick-off risk as they face fierce execution competition and tend to become stale after submissions. Finally, we find that individual investors learn from experience and herd less in the future.