Large traders disrupt the market's long-term memory of order signs.
problem Long-term memory of market order signs is weakened by large traders.
method Analyzed over 6.7 billion trades to investigate the impact of large investment funds on market order dynamics.
result The long-term memory of market order signs is weaker when large investment funds trade in a directional manner and when their participation is high.
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.
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.
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…
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.
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.
Study of stock market mini flash crashes, revealing large market orders as primary cause.
problem Understanding the causes and recovery of mini flash crashes in stock markets.
method Empirical analysis of stock market data, focusing on Ultrafast Extreme Events.
result Large market orders are the primary cause of most mini flash crashes, not high frequency trading.
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.
Large trades in a financial market are usually split into smaller parts and traded incrementally over extended periods of time. We address these large trades as hidden orders. In order to identify and characterize hidden orders we fit hidden Markov models to the time series of the sign of the tick by tick inventory var…
We study the dynamics of order flows around large intraday price changes using ultra-high-frequency data from the Shenzhen Stock Exchange. We find a significant reversal of price for both intraday price decreases and increases with a permanent price impact. The volatility, the volume of different types of orders, the b…
In a recent paper, Alfonsi, Fruth and Schied (AFS) propose a simple order book based model for the impact of large orders on stock prices. They use this model to derive optimal strategies for the execution of large orders. We apply these strategies to an agent-based stochastic order book model that was recently propose…
We study the cause of large fluctuations in prices in the London Stock Exchange. This is done at the microscopic level of individual events, where an event is the placement or cancellation of an order to buy or sell. We show that price fluctuations caused by individual market orders are essentially independent of the v…
Study validates Lillo-Mike-Farmer model predicting financial market long-range correlations.
problem Quantifying long-range correlations in financial markets.
method Analyzed nine years of market data to classify traders as order-splitting or random, measured metaorder-length distributions, and compared to LMF model predictions.
result Agreement between LMF model predictions and actual data, validating the model.
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.
This paper explains how predictable order flow can lead to Brownian motion in financial prices.
problem Why financial prices exhibit Brownian motion despite predictable order flow.
method Generalized Lillo-Mike-Farmer model to nonlinear price-impact dynamics, mapping to Lévy-walk model.
result Price dynamics remain diffusive under the square-root law, even with persistent order flow.
Simulates realistic execution and costs in limit order books.
problem Realistic simulation of limit order books for large-tick assets.
method Tractable representation of spread and volume imbalance; calibrated event timing; feedback mechanism for market impact.
result Simulator yields realistic behavior and sensitivity to execution parameters.
LLMs simulate financial markets, revealing consistent trading strategies and market dynamics.
problem Testing financial theories with AI trading agents.
method Simulated stock market with LLMs using a persistent order book and varied strategies.
result LLMs can simulate different trading strategies and market dynamics.
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 shows power law market impact for limit orders.
problem Understanding market impact of limit orders.
method Proprietary database of metaorders, empirical analysis.
result Market impact follows a power law, stabilizes at approximately two-thirds of maximum.
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.
Optimal strategy found for liquidating large-tick stocks.
problem Maximizing wealth in liquidating stock positions.
method Semi-Markov decision process, Laplace method, queueing theory, dynamic programming.
result Optimal liquidation policy found.
The study examines when large trades are considered news or liquidity shocks in a market model.
problem Understanding when large trades are news or liquidity shocks in a market model.
method A sequential competitive limit order book model with asymmetric information and Student-t tails for liquidity demand.
result Heavy-tailed liquidity demand flattens and concavifies price impact, delaying price discovery.
In the present paper, we study the optimal execution problem under stochastic price recovery based on limit order book dynamics. We model price recovery after execution of a large order by accelerating the arrival of the refilling order, which is defined as a Cox process whose intensity increases by the degree of the m…
Paper proposes BOCPD for real-time order flow and market impact prediction.
problem Persistent order flow patterns in financial markets.
method Bayesian online change-point detection (BOCPD) with score-driven approach.
result Model outperforms existing models in predicting order flow and market impact.
MarketGPT models financial time series with realistic order flow data.
problem Creating accurate financial market simulations.
method Generative pre-trained transformer (GPT) for long sequence generation.
result Model reproduces key features of real financial markets and stylized facts.
Equity auctions show linear price impact up to a large volume, then non-linear.
problem Understanding price impact in equity auctions.
method High-quality data analysis of Paris stock exchange auctions.
result Price impact is linear up to a large volume, then becomes non-linear.
Develops a stochastic approach to financial market delays.
problem Modeling delays in financial markets with multiple assets.
method Introduces a general stochastic framework for information and order execution delays.
result Delayed markets maintain fundamental asset pricing theorems and no asymptotic free lunch condition.
Study uses MFG approach to model equilibrium pricing with market clearing condition.
problem Continuous asset pricing with market clearing condition.
method Mean field game approach to solve forward-backward SDEs of McKean-Vlasov type.
result Net order flow converges to zero in large N-limit with specified conditions.
A framework uses deep reinforcement learning to optimize energy storage in intraday markets.
problem Optimizing energy storage in intraday markets for renewable energy integration.
method Markov Decision Process, asynchronous distributed fitted Q iteration algorithm, artificial trajectories.
result The agent converges to a policy that achieves higher total revenues than the benchmark strategy.
In this paper we develop a model of an order-driven market where traders set bids and asks and post market or limit orders according to exogenously fixed rules. Agents are assumed to have three components to the expectation of future asset returns, namely-fundamentalist, chartist and noise trader. Furthermore agents di…
Market makers reduce price spread in a simple limit order book model.
problem Unrealistically large spread in a simple limit order book model.
method Introduced market makers that place both buy and sell limit orders at current bid and ask prices.
result Market makers reduce spread to a rate that closes it completely.
The study explains the concavity of price impact in markets.
problem The asymptotic concavity of price impact in meta-orders.
method A model with linear local price impact and co-directional trades.
result Volumes at best bid and ask prices favor the executor.
Study finds a crossover from linear to square-root market impact based on order volume.
problem Understanding the dynamics of market impact as a function of order volume.
method Used a large dataset of 8 million trades to establish the crossover between linear and square-root market impact regimes. Applied a dynamical theory of liquidity to explain the results.
result Quantitative agreement with data achieved by considering two liquidity time scales: fast and slow.
Statistical properties of an order book and the effect they have on price dynamics were studied using the high-frequency NASDAQ Level II data. It was observed that the size distribution of marketable orders (transaction sizes) has power law tails with an exponent 1+mu_{market}=2.4 \pm 0.1. The distribution of limit ord…
Recent empirical studies have demonstrated long-memory in the signs of orders to buy or sell in financial markets [2, 19]. We show how this can be caused by delays in market clearing. Under the common practice of order splitting, large orders are broken up into pieces and executed incrementally. If the size of such lar…
Prediction markets can be manipulated by traders who can move contract settlements, harming price discovery.
problem Manipulation of settlement times in prediction markets leads to unfair wealth transfer and harms price discovery.
method Developed a model showing how settlement manipulation transfers wealth and harms price discovery, and observed real-world effects on Polymarket's Bitcoin contract.
result Manipulators capture significant profits from retail traders, especially when settlement times are short.
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.
We present a study of price impact in the over-the-counter credit index market, where no limit order book is used. Contracts are traded via dealers, that compete for the orders of clients. Despite this distinct microstructure, we successfully apply the propagator technique to estimate the price impact of individual tra…
The paper analyzes optimal market making in latency-prone markets.
problem Optimal market making for large-tick assets in the presence of latency.
method Random walk model for asset price, Markov Decision Processes (MDP) formulation, explicit criteria for profitability.
result Market makers can earn positive expected profit under certain conditions.
Paper models Bitcoin market dynamics using 1+1D field theory.
problem Understanding stylized facts in Bitcoin markets.
method Collects order-book datasets, applies KPZ-like stochastic equations.
result Predicts order book dynamics with high precision.
We confirm the square-root law of market impact on Apple Inc. using a large dataset.
problem Testing the square-root law of market impact on a single U.S. large-cap equity.
method Using a full market-by-order feed, we reconstruct metaorders and calibrate impact using the square-root formula.
result The square-root law is confirmed with a prefactor of 0.34, consistent with worldwide 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.
The study reveals how institutional trading activity impacts markets, finding that total order flow is key.
problem Understanding how institutional trading activity affects market impact.
method Analysis of a large database of metaorders by institutional investors in the U.S. equity market, using a simple heuristic model.
result The market impact of multiple metaorders depends on the total number of metaorders and their mutual sign correlation, reproducing empirical market impact curves.
A great deal of academic and theoretical work has been dedicated to optimal liquidation of large orders these last twenty years. The optimal split of an order through time (`optimal trade scheduling') and space (`smart order routing') is of high interest \rred{to} practitioners because of the increasing complexity of t…
New MGCPP model for order flow in financial markets.
problem Modeling order flow dynamics in financial markets.
method Developed MGCPP, proved LLN and FCLTs, applied to real data.
result Validated MGCPP model with real trading data.
Model asset pricing with habit formation in a large market.
problem Understanding asset pricing in large heterogeneous markets with habit formation.
method Mean field game theory and quadratic-growth mean field BSDEs.
result Derives a semi-analytic solution for asset pricing model.
Traditional market makers are losing their importance as automated systems have largely assumed the role of liquidity provision in markets. We update the model of Glosten and Milgrom (1985) to analyze this new world: we add multiple securities and introduce an automated market maker who uses the relationships between s…
MarS simulates financial markets using generative models.
problem Simulating realistic financial market effects.
method Order-level generative foundation model (LMM) for realistic, interactive, and controllable order generation.
result Strong scalability and robust realism in MarS.