Optimal stock trading strategy with market orders and limit orders in a risky market.
problem Finding the best time and amount to place market and limit orders to minimize costs.
method Analyzes single and multi-period models with limit and market orders, considering liquidity risk.
result Optimal placement of market and limit orders can be determined under different market conditions.
In order-driven markets, limit-order book (LOB) resiliency is an important microscopic indicator of market quality when the order book is hit by a liquidity shock and plays an essential role in the design of optimal submission strategies of large orders. However, the evolutionary behavior of LOB resilience around liqui…
Study analyzes order transitions in high, medium, and low market cap stocks using Markov chains.
problem Understanding order transitions in stocks of different market caps.
method First-order discrete-time Markov chain model applied to NASDAQ100 stocks.
result Limit orders exhibit higher inertia during opening hours but decrease in subsequent hours, while market orders increase.
Optimal market making strategy for electronic markets with persistent order flows.
problem Market making on electronic markets with persistent order flows.
method Formulated as a stochastic control problem, characterized by viscosity solutions, and implemented numerically.
result Characterization of an optimal market making strategy.
Study shows maker-taker fees improve market efficiency but increase costs.
problem Impact of maker-taker fees on total cost of taking orders.
method Agent-based simulation model for financial markets.
result Maker-taker fees increase total costs but improve market efficiency.
We empirically study the market impact of trading orders. We are specifically interested in large trading orders that are executed incrementally, which we call hidden orders. These are reconstructed based on information about market member codes using data from the Spanish Stock Market and the London Stock Exchange. We…
Model simulates sparse order books in illiquid markets.
problem Inaccurate LOB models in illiquid markets.
method Inhomogeneous Poisson process for order arrivals and cancellations.
result Enhanced understanding of LOB dynamics in illiquid markets.
Study shows marketable order routing to wholesalers benefits all traders, leading to lower market depth and price volatility.
problem Determining the preference of retail traders for marketable order routing.
method Two models: one for market makers competing for retail order flow (Bertrand model) and another for price-taking competitive liquidity providers (open exchange model).
result Routing marketable orders to wholesalers is preferred by all traders, leading to mean reverting inventories and lower market depth.
We briefly review data analysis of the Island order book, part of NASDAQ, which suggests a framework to which all limit order markets should comply. Using a simple exclusion particle model, we argue that short-time price over-diffusion in limit order markets is due to the non-equilibrium of order placement, cancellatio…
Study uses contrastive learning to analyze market order behavior.
problem Understanding diverse market order behaviors.
method Self-supervised learning with triplet loss for order representation.
result Identified distinct behavior types using K-means clustering.
This paper identifies a negative profit effect in limit order fills.
problem Profit drag in limit order fills due to adverse price movements.
method Discrete market model, empirical simulation of US Treasury Bond futures.
result Existence of negative drift in limit order fills.
This paper measures and compares the tail risks of limit and market orders using Extreme Value Theory. The analysis examines realised tail outcomes using the Dealing 2000-2 electronic broking system based on completed transactions rather than the more common analysis of indicative quotes. In general, limit and market o…
This paper investigates the market impact of passive orders.
problem Understanding the market impact of passive orders executed through limit orders.
method Developed a microstructure model linking liquidity dynamics and price moves, replacing the constant information content assumption with a function dependent on available volume.
result Derived useful approximations for market impact curves, leading to closed-form formulas.
Generates realistic stock market order streams using GANs.
problem Creating high-fidelity stock market data.
method Conditional Wasserstein GAN with auction mechanism and order-book augmentation.
result Generated data is close to real market data.
ClusterLOB clusters market events to identify different trading behaviors.
problem Understanding market microstructure and participant behavior in financial markets.
method ClusterLOB uses K-means++ algorithm to cluster market events based on six time-dependent features.
result ClusterLOB identifies three distinct trading behaviors: directional, opportunistic, and market-making participants.
A new high-frequency market making strategy using Deep Hawkes process.
problem Optimizing high-frequency trading in volatile markets.
method Developed a Deep Hawkes process to model order arrivals and their effects on the limit order book.
result The new strategy outperforms traditional methods in market making.
Study examines market impact of small orders in futures contracts.
problem Understanding market impact of small orders in financial markets.
method Empirical study using tick data, normalizing results, proposing a simple linear model.
result Market impact of small orders is either linear or concave, depending on the instrument.
Market making is one of the most important aspects of algorithmic trading, and it has been studied quite extensively from a theoretical point of view. The practical implementation of so-called "optimal strategies" however suffers from the failure of most order book models to faithfully reproduce the behaviour of real m…
We demonstrate an application of risk-sensitive reinforcement learning to optimizing execution in limit order book markets. We represent taking order execution decisions based on limit order book knowledge by a Markov Decision Process; and train a trading agent in a market simulator, which emulates multi-agent interact…
Study proves fluid limits of fragmented limit-order markets.
problem Modeling fragmented limit-order markets with small and frequent orders.
method Proved convergence of discrete system to fluid limit characterized by coupled nonlinear ODEs.
result Fluid system converges to stationary equilibrium state over time.
Analyzes how order flow affects price formation in financial markets.
problem Understanding how prices are formed by order flow in financial markets.
method Critical discussion of modeling approaches and empirical observations, focusing on market impact and transaction costs.
result Algorithmic trading impacts the quality and cost of trading.
Improved queue-reactive model considers order sizes for better market simulation.
problem Accurately modeling market dynamics and order flow properties.
method Integrates order sizes, type, and arrival rate into queue-reactive model.
result Extended model produces markets with volatility matching historical data.
Paper models limit order book with informed traders and market makers.
problem Modeling the limit order book with heterogeneous market participants.
method Agent-based model with four types of participants: informed traders, noise traders, informed market makers, and noise market makers. Based on Glosten-Milgrom and Huang-Rosenbaum-Saliba approaches.
result Derived the static limit order book characteristics and compared them with existing models.
Bayesian theory explains market impact of large trades.
problem Reduction of price impact from large trades.
method Bayesian approach incorporating all trade information.
result Recovery of market impact laws including square-root and linear regimes.
Study applies market microstructure to Cuban informal currency market, finding market makers improve liquidity.
problem Understanding dynamics of informal currency markets.
method Modeling bid/ask intentions using Limit Order Book, applying Avellaneda-Stoikov model with Market Maker.
result Market Maker improves market quality and bid/ask dynamics.
We develop a new market-making model, from the ground up, which is tailored towards high-frequency trading under a limit order book (LOB), based on the well-known classification of order types in market microstructure. Our flexible framework allows arbitrary order volume, price jump, and bid-ask spread distributions as…
Paper proposes DigMA to generate controllable financial market orders.
problem Generating realistic financial market orders with controllability.
method DigMA model using conditional diffusion and meta agent.
result DigMA achieves superior controllability and generation fidelity.
RL agents optimize order execution in a realistic market simulation.
problem Optimal order execution challenges in a complex market.
method Multi-agent RL in a historical order book simulation.
result RL agents converge to TWAP strategies in some scenarios.
Bayesian framework explains price formation with learning and market impact.
problem Understanding how prices form in markets with informed participants.
method Introduces a Bayesian model for updating priors on efficient prices.
result Exponential intensities for aggressive order arrivals are a natural outcome.
Study on heavy tails in closing auction returns, explaining imbalance through limit order submission.
problem Understanding heavy tails in closing auction return distributions.
method Used the stochastic call auction model of Derksen et al. (2020a) to derive and verify a relation between tail exponents.
result Large closing price fluctuations are not caused by large market orders, but by imbalance in limit orders.
The paper models market dynamics using a limit order book system to explain slippage and inefficiency.
problem Inefficiency in matching markets due to structural liquidity constraints and slippage.
method Introduces a market microstructure framework with a latent preference state matrix and a dynamic discrete choice execution model.
result Persistent slippage and regional invariance of preference orderings are explained by liquidity thresholds.
Study uses agent-based simulation to analyze impact of OBI strategy on financial markets.
problem Improving execution in markets with supply-demand imbalance.
method Built an execution algorithm that accounts for OBI, tested it in artificial markets.
result OBI strategy can improve execution, especially in volatile markets.
Study reveals stylized facts in German bond futures markets.
problem Understanding market dynamics in German bond futures.
method Analyzed tick-by-tick data of four German bond futures contracts.
result Uncovered commonalities and unique characteristics across different futures.
Modeling price impacts and trading signals for optimal execution and speculation.
problem Optimal execution and speculation in markets with trade signals.
method Price impact model driven by order flow, stochastic price impact, Meyer-σ-fields signal process, Marcus-type SDEs. result Derivation and numerical solution of HJB equation for optimal execution, enhanced speculative strategies.
In financial markets, the order flow, defined as the process assuming value one for buy market orders and minus one for sell market orders, displays a very slowly decaying autocorrelation function. Since orders impact prices, reconciling the persistence of the order flow with market efficiency is a subtle issue. A poss…
We consider a simple model for the evolution of a limit order book in which limit orders of unit size arrive according to independent Poisson processes. The frequencies of buy limit orders below a given price level, respectively sell limit orders above a given level are described by fixed demand and supply functions. B…
The IMH suggests market price fluctuations are driven by order flow, not fundamental values.
problem Reconciling IMH with microstructure literature on market dynamics.
method Reviewed empirical facts and applied Latent Liquidity Theory to predict price impact multiplier.
result The multiplier M is of order unity, consistent with IMH, and depends on stock volatility and daily traded market cap fraction. A consistency criterion for price impact functions in limit order markets is proposed that prohibits chain arbitrage exploitation. Both the bid-ask spread and the feedback of sequential market orders of the same kind onto both sides of the order book are essential to ensure consistency at the smallest time scale. All t…
Our goal in this paper is to study the market impact in a market in which the order flow is autocorrelated. We build a model which explains qualitatively and quantitatively the empirical facts observed so far concerning market impact. We define different notions of market impact, and show how they lead to the different…
Measures price impact in order-driven markets without relying on averages.
problem Measuring price impact in order-driven markets without relying on averages.
method Modeling the limit order book using state-dependent Hawkes processes and defining price impact profile as a function of the compensator of a stochastic process.
result The clustering of sell child orders has a bigger impact on price than their sizes.
Commonly used limit order book attributes are empirically considered based on NASDAQ ITCH data. It is shown that some of them have the properties drastically different from the ones assumed in many market dynamics study. Because of this difference we propose to make a transition from "Statistical" type of order book st…
In this paper, we assume that the permanent market impact of metaorders is linear and that the price is a martingale. Those two hypotheses enable us to derive the evolution of the price from the dynamics of the flow of market orders. For example, if the market order flow is assumed to follow a nearly unstable Hawkes pr…
Enhances queue-reactive model for realistic limit order book simulation.
problem Realistic simulation of limit order books for market research and strategy development.
method Extends Queue-Reactive model with neural network for complex dependencies and varying market conditions.
result Captures key market properties like square-root law of market impact and order size patterns.
The paper proposes a new order slicing strategy to reduce market impact in large-volume trading.
problem Significant market impact and slippage in large-volume trading.
method Volatility-volume-based order slicing strategy using Exponential Weighted Moving Average and Markov Chain Monte Carlo simulations.
result Improves trade execution efficiency and reduces market impact.
Market impact is reduced when orders are filled with concentrated counterparts.
problem Market impact increases with a large number of trading counterparts.
method Analyzed London Stock Exchange data to show concentrated trading impacts market price.
result Concentrated trading reduces market impact when matched with similarly concentrated counterparts.
Paper uses RL for market making, improving stability in non-stationary markets.
problem Optimizing market making strategies in non-stationary limit order book dynamics.
method Reinforcement Learning (Proximal-Policy Optimization) applied to a simulator.
result RL agent outperforms closed-form optimal solution in non-stationary markets.
New method uses statistical physics to detect financial market manipulation.
problem Detecting financial market manipulation activities like spoofing and layering.
method Modeling order book dynamics as particle motion and using momentum measure.
result Method outperforms conventional Z-score-based anomaly detection.
A new algebraic framework models LOBs with physics and stochastic processes.
problem Capturing the dynamics of limit order books (LOBs).
method Algebraic framework using Dirac notation and generating functions.
result Exact simulations of market scenarios using the Gillespie algorithm.