Anonymized traders' wallet addresses reveal predictive trading information.
problem Adverse selection and return predictability in public trading environments.
method Reconstructed full-depth limit order book from 17.1 billion messages and 14.3 million orders.
result Wallets' activity correlates with price movements and improves return prediction.
Visualizes futures markets using particle physics tools.
problem Understanding high-velocity data in futures markets.
method Uses ROOT, an open-source data-analysis tool, to reconstruct and visualize message-based data.
result Allows stakeholders to gain a better understanding of markets and monitor effectively.
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.
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.
Extends a market impact model to include mean-reversion, revealing new order book dynamics.
problem Understanding market impact in a latent order book model.
method Mean-reversion added to a minimal model, analyzed with mean-field assumption.
result New order book dynamics and price impact development shown.
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.
Paper uses ML for electricity price forecasting using order book data.
problem Forecasting German electricity spot market prices.
method Developed feature extraction for order book data, used cross-validation, compared neural networks and random forests to statistical models.
result Machine learning models outperform traditional approaches.
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.
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.
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.
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.
New model reveals latent liquidity in financial markets.
problem Understanding the connection between latent and observable order books.
method Suggests a simple mechanism for revealing latent liquidity and quantifies it from real data.
result Existence of a market instability threshold leading to liquidity crises.
This paper improves market making strategies by incorporating non-Markovian features in order book models.
problem Failure of order book models to accurately represent real market behavior.
method Identification of statistical properties, design of market making strategies, and comparison of performances.
result Incorporating non-Markovian features in order book models significantly improves market making strategies.
Public trading wallets reveal price information not captured by anonymous data.
problem Informed traders' anonymity in public exchanges.
method Reconstructed full-depth limit order book from 17.1 billion messages.
result Wallets' aggressive orders predict returns, with a 13.2% gain over anonymous benchmarks.
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.
Modeling high-frequency order book data with Hawkes-Markovian process.
problem Capturing the dynamics of high-frequency order book events.
method Hawkes process with Markovian baseline intensities, LASSO regularization, and Akaike Information Criteria.
result Effective modeling of order book dynamics with reduced parameter redundancy.
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.
Model uses statistical physics principles to predict financial market volatility and returns.
problem Predicting price volatility and expected returns in financial markets.
method Inspired by statistical physics, the study introduces a physical model using Level 3 order book data to measure kinetic energy and momentum.
result The model outperforms traditional and machine learning approaches in forecasting volatility and expected returns.
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.
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.
We present a general Markovian framework for order book modeling. Through our approach, we aim at providing a tool enabling to get a better understanding of the price formation process and of the link between microscopic and macroscopic features of financial assets. To do so, we propose a new method of order book repre…
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…
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.
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…
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.
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.
Sunshine trading theory predicts lower execution costs and liquidity provision through explicit preannouncements, but evidence is scarce in traditional markets.
problem Adverse selection on liquidity provision
method Reconstructing metaorders and comparing them with visible TWAP executions
result Visible TWAPs face lower execution costs and leave a smaller permanent price impact compared to hidden metaorders.
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.
New method estimates order book dynamics efficiently.
problem Understanding complex order book dynamics in financial markets.
method Nonparametric estimation of branching ratio matrix for multivariate Hawkes process.
result Reveals relationships between order book events.
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.
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…
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 distribution of returns in financial time series exhibits heavy tails. In empirical studies, it has been found that gaps between the orders in the order book lead to large price shifts and thereby to these heavy tails. We set up an agent based model to study this issue and, in particular, how the gaps in the order …
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.
When modelling stock market dynamics, the price formation is often based on an equilbrium mechanism. In real stock exchanges, however, the price formation is goverend by the order book. It is thus interesting to check if the resulting stylized facts of a model with equilibrium pricing change, remain the same or, more g…
Models interactions among market participants in a financial asset's order book.
problem Understanding dynamic interactions among market participants in a financial asset's order book.
method Derives variational partial differential equations for MM and HFT strategies, and explains almost optimal control.
result Illustrates interactions between market participants through simulations of an order book.
Study shows gaps in Bitcoin order book are linked to returns but only in the short term.
problem Understanding the relationship between gaps and returns in Bitcoin order books.
method Examined the dynamics of gaps and returns in a Bitcoin order book without considering long-term causation.
result The causal relationship between gaps and returns is limited to instantaneous causation.
Multivariate Hawkes processes analyze order dynamics in financial markets.
problem Complex interactions between order timing and size in financial markets.
method Multivariate Hawkes processes with nonparametric estimation.
result Simple volume-time independence models are inadequate for financial data.
Simulates financial market orders using anomalous diffusion models.
problem Anomalous diffusion in financial market order dynamics.
method Discrete Time Random Walk with Sibuya waiting times, non-uniform sampling, and cubic spline interpolation.
result Demonstrates price impact for different forcing functions and model parameters.
This paper develops a new neural network architecture for modeling spatial distributions (i.e., distributions on R^d) which is computationally efficient and specifically designed to take advantage of the spatial structure of limit order books. The new architecture yields a low-dimensional model of price movements deep …
This paper consists of two parts. The first part is devoted to empirical analysis of consolidated order book (COB) for the index RTS futures. In the second part we consider Poissonian multi--agent model of the COB. By varying parameters of different groups of agents submitting orders to the book we are able to model va…
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
Deep learning predicts Bitcoin spot price movements from order books.
problem Predicting cryptocurrency spot price movements from order book data.
method Temporal CNNs trained on 2-second prediction time horizon.
result 71% walk-forward accuracy on coinbase data.
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.
Paper derives second order approximations for limit order books.
problem Modeling fluctuations in price and volume processes.
method Second order approximation of infinite dimensional limit order book dynamics.
result Confidence intervals for optimal portfolio liquidation models.
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.