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.
The study compares how deletions and trades affect stock prices and spread changes.
problem Understanding the impact of deletions and trades on stock prices and spread changes.
method Examined the frequencies of relative amounts of price changing events due to trades, deletions, and order placements.
result Deletions of orders open the bid-ask spread more often than trades and have a similar effect on prices as trades.
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…
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.
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.
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.
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 shows how order flow at multiple price levels affects stock prices.
problem Understanding how order flow at different price levels influences stock prices.
method Fit a linear relationship between multi-level order-flow imbalance (MLOFI) and mid-price changes using high-quality data.
result The inclusion of more price levels in MLOFI improves the fit with mid-price changes.
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.
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…
Optimizes large stock order execution with LSTM neural networks.
problem Minimizing transaction costs in large stock order execution.
method Trained LSTM neural network to minimize transaction costs.
result LSTM strategy outperforms TWAP and VWAP strategies.
Generative tools mimic stock market traders using synthetic data.
problem Imitating trading behavior of stock market participants.
method Modified state-space model applied to limit order book data, trained on synthetic data generated from a heterogeneous agent-based model.
result Model's predicted distribution matches ground truths from the agent-based model.
Using ultra-high-frequency data extracted from the order flows of 23 stocks traded on the Shenzhen Stock Exchange, we study the empirical regularities of order placement in the opening call auction, cool period and continuous auction. The distributions of relative logarithmic prices against reference prices in the thre…
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.
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…
DeepLOB predicts stock price movements from limit order book data.
problem Predicting price movements from limit order book data.
method Deep Convolutional Neural Networks with LSTM modules.
result Outperforms existing algorithms on LOB dataset and delivers stable out-of-sample predictions.
We have analyzed the statistical probabilities of limit-order book (LOB) shape through building the book using the ultra-high-frequency data from 23 liquid stocks traded on the Shenzhen Stock Exchange in 2003. We find that the averaged LOB shape has a maximum away from the same best price for both buy and sell LOBs. Th…
Study shows market quality improves with larger orders, not smaller tick sizes or higher trading frequencies.
problem Impact of order book tick sizes, metaorders, and trading frequencies on market quality.
method Multi-agent reinforcement learning model to simulate stock market dynamics.
result Market quality benefits from larger orders but not from smaller tick sizes or higher trading frequencies.
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.
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 models stock price formation using Gibbs Grand-Canonical Ensemble.
problem Modeling stock price formation and temperature differences.
method Using Gibbs Grand-Canonical Ensemble, defining temperatures for bid and ask orders.
result Temperature difference between bid and ask orders correlates with VAO indicator.
Develops a model for stock price dynamics using reduced-form approximations.
problem Capturing the dynamics of best bid and ask prices in level-1 limit order books.
method Investigates data and develops a nonparametric discrete model for price dynamics.
result A reduced-form model with analytical tractability that fits empirical data.
Study shows bifurcating price dynamics in ASME with traders.
problem Understanding price dynamics in artificial stock markets.
method Agent-based model of endogenous traders interacting through a LOB.
result Bistability in price equilibria: zero-price and persistent positive-price states.
A new Hawkes process model captures order book dynamics in high-frequency trading.
problem Capturing the complex dynamics of high-frequency trading with large datasets.
method Estimation of an order book dependent Hawkes process using a product of a Hawkes process and covariates.
result Capturing the nonlinearity of order book information improves the model's performance.
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 uses stochastic volatility to optimize trading strategies in a limit order book market.
problem Optimizing trading strategies in a limit order book market with stochastic volatility.
method Employed the Heston stochastic volatility model to derive optimal trading strategies for dealers in a security market.
result Developed optimal trading strategies for dealers in both stock and option markets with stochastic volatility.
The paper solves portfolio liquidation under transient price impact for 100 NASDAQ stocks.
problem Determining optimal trading strategies under various market impact models.
method Derives explicit solutions for market impact parameters in a portfolio liquidation model.
result The derived strategy achieves significant cost savings compared to benchmark models.
Market liquidity plays a vital role in the field of market micro-structure, because it is the vigor of the financial market. This paper uses a variable called convexity to measure the potential liquidity provided by order-book. Based on the high-frequency data of each stock included in the SSE (Shanghai Stock Exchange)…
In this paper, we establish a fluid limit for a two--sided Markov order book model. Our main result states that in a certain asymptotic regime, a pair of measure-valued processes representing the "sell-side shape" and "buy-side shape" of an order book converges to a pair of deterministic measure-valued processes in a c…
Sequential processing biases asset allocation in artificial stock markets.
problem Systematic bias in asset allocation due to sequential processing of order books.
method Examined the impact of sequential versus parallel clearing mechanisms on multi-asset price dynamics.
result Sequential processing introduces a significant bias affecting the allocation of traders' capital.
Study improves Cox model for predicting stock trading signs using Japanese market data.
problem Improving Cox model for predicting stock trading signs using Japanese market data.
method Added new covariates and used high-frequency trading data for 222 Nikkei 225 stocks.
result Cox-type model performs well in Japanese market and identifies key factors for accurate estimation.
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.
Analyzes order flow in limit order books, finds varied behavior.
problem Understanding order flow dynamics in limit order books.
method Empirical analysis of Nasdaq data, identifying distinct phases of order flow.
result Identifies varied order flow behavior not consistent with stimulated refill.
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 …
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…
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…
Order submission and cancellation are two constituent actions of stock trading behaviors in order-driven markets. Order submission dynamics has been extensively studied for different markets, while order cancellation dynamics is less understood. There are two positions associated with a cancellation, that is, the price…
Deep learning models struggle with new data in stock price trend prediction.
problem Stock price trend prediction using Deep Learning models.
method Examination of fifteen state-of-the-art DL models on LOB data, using LOBCAST framework.
result All models show significant performance drop with new data, questioning their market applicability.
Price gap, defined as the logarithmic price difference between the first two occupied price levels on the same side of a limit order book (LOB), is a key determinant of market depth, which is one of the dimensions of liquidity. However, the properties of price gaps have not been thoroughly studied due to the less avail…
Proposes a model combining order book data and herd behavior to replicate long-range memory in financial returns.
problem Replicating long-range memory in financial returns and trading activity.
method Combines empirical order book data and financial herd behavior model.
result Model successfully replicates long-range memory in absolute returns and trading activity.
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.
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.
Paper proposes new features and deep learning models for mid-price prediction.
problem Challenging task of predicting mid-price movement based on LOB data.
method Developed new handcrafted features and deep learning models.
result Deep learning models can predict mid-price movement and time to change.
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.
We study the price impact of order book events - limit orders, market orders and cancelations - using the NYSE TAQ data for 50 U.S. stocks. We show that, over short time intervals, price changes are mainly driven by the order flow imbalance, defined as the imbalance between supply and demand at the best bid and ask pri…
We empirically study the trading activity in the electronic on-book segment and in the dealership off-book segment of the London Stock Exchange, investigating separately the trading of active market members and of other market participants which are non-members. We find that (i) the volume distribution of off-book tran…
Enhanced deep learning model predicts stock price movement using LOB data.
problem Challenges in predicting stock price movement from high-dimensional, volatile LOB data.
method Siamese architecture with multi-head attention and LSTM modules.
result Significant improvement in stock price prediction performance over strong baselines.
Constant price impact functions, much used in financial literature, are shown to give rise to paradoxical outcomes since they do not allow for proper predictability removal: for instance the exploitation of a single large trade whose size and time of execution are known in advance to some insider leaves the arbitrage o…