Two price regimes identified in limit order books: close and far from quotes.
problem Understanding the distribution and behavior of limit orders in limit order books.
method Analysis of limit order book data in dimensions of price, time, lifetime, and volume.
result Identification of two distinct regimes in the limit order book: close and far from quotes.
New neural network models limit order book dynamics efficiently.
problem Efficiently modeling price movements in the limit order book.
method Developed a spatial neural network architecture.
result Spatial neural network outperforms other models in risk management.
New neural network predicts stock price jumps using limit order book data.
problem Predicting short-term price movements in stock markets.
method Attention-based Convolutional Long Short-Term Memory network architecture.
result Attention mechanism improves jump prediction performance.
Model predicts limit order book dynamics based on market participant interactions.
problem Understanding and predicting the dynamics of the limit order book in financial markets.
method Agent-based model with informed, noise, and market maker traders; deduces limit order book from interactions.
result Link between price dynamics, trade proportions, volume, spread, and equilibrium state.
Proposes a model for simulating limit order books with state-dependent intensities.
problem Simulating the dynamics of limit order books with varying intensities of order submission.
method Developed a parametric model with state-dependent intensities for limit orders, market orders, and cancellations. Introduced new models for order placement and cancellation selection.
result The proposed model accurately simulates the dynamics of limit order books and outperforms standard Poisson models.
Study of Hawkes processes in limit order books for price volatility analysis.
problem Understanding price volatility in limit order books.
method Construct and analyze general compound Hawkes processes.
result Established Law of Large Numbers and Functional Central Limit Theorems for specific variations.
A new model for limit order book dynamics with time-dependent arrival rates.
problem Modeling the dynamics of limit order books with time-dependent arrival rates.
method Proposes a stochastic model with endogenous price dynamics and shows the conditional diffusion limit is Brownian meander.
result The model's conditional diffusion limit is the Brownian meander.
Deep learning predicts stock price changes in Limit Order Books.
problem Predicting high-frequency Limit Order Book mid-price changes.
method Cutting-edge deep learning methodologies applied to NASDAQ stocks.
result Deep learning methods' effectiveness varies by stock microstructure.
Study new Hawkes processes to model price changes in limit order books.
problem Model price volatility in limit order books.
method Prove LLN and FCLTs for general compound and regime-switching general compound Hawkes processes.
result Volatilities of price changes are expressed in terms of parameters describing arrival rates and price changes.
Transformers predict price movements from limit order books.
problem Predicting price movements from limit order books.
method Causal convolutional network with masked self-attention.
result Significantly outperforms existing architectures on FI-2010 dataset.
Paper introduces new Hawkes processes to model price changes in limit order books.
problem Modeling price volatility and order flow in limit order books.
method Introduces compound and regime-switching compound Hawkes processes, proving Law of Large Numbers and FCLTs.
result Volatilities of price changes are linked to parameters of arrival rates and price changes.
Machine learning predicts short-term price movements from LOB features.
problem Understanding and predicting short-term price movements from LOB dynamics.
method Machine learning approach to analyze LOB features.
result Significantly superior prediction results compared to baseline.
The paper examines the reliability of limit order book representations in the face of data perturbation.
problem The reliability of limit order book representations under data perturbation.
method Experimental analysis of existing representations and guidelines for future research.
result Existing representations of limit order book data are vulnerable to data perturbation.
The paper analyzes fill probabilities in limit order books with varying price levels.
problem Determining the likelihood of limit orders being executed in a limit order book.
method Developed a state-dependent stochastic framework to model limit order book dynamics.
result Derived semi-analytical expressions for fill probabilities and mid-price changes.
We study the analytical properties of a one-side order book model in which the flows of limit and market orders are Poisson processes and the distribution of lifetimes of cancelled orders is exponential. Although simplistic, the model provides an analytical tractability that should not be overlooked. Using basic result…
The paper models and analyzes the dynamics of a limit order book using Hawkes processes.
problem Modeling the complex dynamics of a limit order book driven by market price and volume.
method Derives a scaling limit for an infinite dimensional model driven by Hawkes processes.
result The dynamics converge to a coupled SDE-ODE system, with specific limiting processes and intensities.
Proposes a stochastic model for limit order book dynamics.
problem Captures the dynamics of limit order books in financial markets.
method Develops a stochastic partial differential equation (SPDE) model with multiplicative noise.
result Shows efficient estimation and computation methods for the model.
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.
Optimizes trade execution with reinforcement learning for limit orders.
problem Maximizing revenue in a limit order book with market and limit orders.
method Formulated as a dynamic allocation task, uses multivariate logistic-normal distributions for efficient training.
result Outperforms traditional strategies in simulated environments.
Paper improves deep learning models for limit order book data.
problem Deep learning models' performance depends on robust input data representation.
method Identified and modified flaws in existing representations.
result Proposed modifications lead to state-of-the-art performance.
Order positions are key variables in algorithmic trading. This paper studies the limiting behavior of order positions and related queues in a limit order book. In addition to the fluid and diffusion limits for the processes, fluctuations of order positions and related queues around their fluid limits are analyzed. As a…
Study integrates deep learning with financial data for improved trading strategies.
problem Enhancing predictive performance in algorithmic trading and portfolio optimization.
method Developed embedding techniques to treat limit order book snapshots as image-based input channels.
result Achieved state-of-the-art performance in high-frequency trading algorithms.
Paper provides a benchmark dataset for mid-price forecasting in limit order book data.
problem Forecasting mid-price in high-frequency financial markets.
method Extracted and normalized time series data from NASDAQ Nordic stocks.
result Dataset of ~4,000,000 time series samples for 5 stocks.
The paper models stock order books with varying price changes.
problem Modeling stock order books with variable price changes.
method Developed a general semi-Markov model with two and multiple states.
result Validated the model with real data from multiple companies.
Study state-dependent Hawkes processes for limit order book modeling.
problem Modeling feedback loop between order flow and limit order book shape.
method Existence and uniqueness of state-dependent Hawkes processes, simulation, maximum likelihood estimation.
result Excitation effects in order flow are strongly state-dependent.
Tests if market noise is explained by limit order book variables.
problem Determining if market microstructure noise is fully explained by specific limit order book variables.
method Compares two quasi-maximum likelihood estimators of volatility, one including residual noise and one not, in a nonparametric framework.
result Examines central limit theory of quasi-maximum likelihood estimation in the presence of residual noise.
We develop a second-order model for limit order books in a single scaling regime.
problem Modeling price and volume dynamics in a limit order book with market and limit orders at a common time scale.
method Established a first- and second-order approximation for an infinite dimensional limit order book model.
result Proved the existence and uniqueness of a solution for the second-order approximation.
In this paper we present a novel approach to the determination of fat tails in financial data by studying the information contained in the limit order book. In an order-driven market buyers and sellers may submit limit orders, which are executed when the price touches a pre-specified lower, respectively higher, limit-p…
Paper uses ML to predict stock price movements from order book data.
problem Forecasting stock price movements in financial markets.
method Combines handcrafted and ML features for three classifiers, evaluated on two setups.
result Machine Learning shows promise for this task, suggesting future research.
Unified analytic account of correlation emergence and Epps effect in coupled limit order books
problem Correlation emergence and Epps effect in coupled limit order books
method Discrete random-walk description of order flow with creation, cancellation, and diffusion, coupled reaction-diffusion equations with moving reaction boundary
result Realized correlations as a function of aggregation time
Study examines meso-scale order flows and LOB resilience.
problem Understanding price formation and LOB resilience on meso-scale.
method Empirical analysis of order flows and LOB metrics from tick data.
result Limit order flows and shape are key predictors of price formation and LOB resilience.
This paper uses CGANs to simulate and improve trading agent performance in limit order books.
problem Improving trading agent performance in limit order book environments.
method Investigates conditional generative models (CGANs) for order book simulation and adversarial attacks to enhance realism and robustness.
result CGANs can be improved to better simulate real market conditions and are more robust to adversarial attacks.
Exchange uses incentives to optimize limit order book dynamics.
problem Optimizing market liquidity in fragmented electronic markets.
method Modeling limit order book as SPDE and using control theory to design incentives.
result Exchange can design incentives to modify order book shape and increase liquidity.
RL agent learns to place limit orders for trading signals in financial markets.
problem Training an RL agent to execute trading signals in limit order book markets.
method Deep Duelling Double Q-learning with APEX architecture, using synthetic alpha signals.
result RL agent outperforms heuristic trading strategies in inventory management and order placing.
HLOB predicts mid-price changes in L.O.Bs using deep learning.
problem Forecasting mid-price changes in Limit Order Books.
method HLOB uses a deep learning model with an Information Filtering Network and Homological Convolutional Neural Networks.
result HLOB outperforms state-of-the-art models in real-world datasets.
A framework models order book dynamics using point processes and mass transport.
problem Capturing the complex dynamics of limit order books.
method Combines spatial point process for order flow and mass transport operator for market clearing.
result Provides insights into the interplay between order flow and price dynamics.
Financial markets can be described on several time scales. We use data from the limit order book of the London Stock Exchange (LSE) to compare how the fluctuation dominated microstructure crosses over to a more systematic global behavior.
Model simulates correlation emergence in two coupled limit order books.
problem Modeling correlation emergence in coupled limit order books.
method Simulated two coupled diffusive limit order books using random walks in the fluid limit, with trader interactions.
result Demonstrated the recovery of an Epps effect from the model.
Models predict order book recovery from aggressive trading follows a simple t^1/3 scaling.
problem Understanding order book dynamics in financial markets.
method Developed macroscopic models and solved numerically and asymptotically.
result Order book recovery follows a t^1/3 scaling law.
We examine the dynamics of the bid and ask queues of a limit order book and their relationship with the intensity of trade arrivals. In particular, we study the probability of price movements and trade arrivals as a function of the quote imbalance at the top of the limit order book. We propose a stochastic model in an …
Paper connects micro to macro models of limit order books using SPDEs.
problem Modeling high-frequency trading dynamics in limit order books.
method Microscopic to mesoscopic to macroscopic limit analysis, SPDEs.
result Macroscopic limit described by reflected SPDEs.
Python module for RL trading in limit order books.
problem Training RL agents for algorithmic trading in limit order books.
method Model-based gym environments for reinforcement learning.
result Efficient RL training for trading problems.
RNNs predict price-flips in limit order books, reducing adverse selection.
problem Predicting price-flips in limit order books to reduce adverse selection.
method Recurrent Neural Networks (RNNs) applied to high-frequency trading data.
result RNNs capture non-linear relationships and outperform other classifiers.
Graph Neural Network improves volatility forecasting for 500 S&P stocks.
problem Forecasting short-term realized volatility in a multivariate setting.
method Graph Transformer Network for Volatility Forecasting.
result Our model outperforms benchmarks on 500 S&P stocks.
We examine the correlation of the limit price with the order book, when a limit order comes. We analyzed the Rebuild Order Book of Stock Exchange Electronic Trading Service, which is the centralized order book market of London Stock Exchange. As a result, the limit price is broadly distributed around the best price acc…
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.
The study examines how limit-order book resilience changes after effective market orders in Chinese stocks.
problem Understanding the resilience of limit-order books after liquidity shocks.
method Empirical analysis of order flow data from Chinese stocks, focusing on bid-ask spread, LOB depth, and order intensity.
result Traders are more likely to submit effective market orders when the bid-ask spread is low, same-side depth is high, and opposite-side depth is low.
Paper proposes a deep learning method to estimate fill probabilities of limit orders in LOBs.
problem Estimating the fill probabilities of limit orders in different levels of a limit order book.
method Survival analysis model using a convolutional-Transformer encoder and a monotonic neural network decoder.
result The proposed method significantly outperforms other approaches in survival analysis.