We define a stochastic model of a two-sided limit order book in terms of its key quantities \textit{best bid [ask] price} and the \textit{standing buy [sell] volume density}. For a simple scaling of the discreteness parameters, that keeps the expected volume rate over the considered price interval invariant, we prove a…
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48 results for “buy limit order”
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.
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.
A prognosis oriented microscopic stock market modelcond-mat.stat-mech
We present a new microscopic stochastic model for an ensemble of interacting investors that buy and sell stocks in discrete time steps via limit orders based on individual forecasts about the price of the stock. These orders determine the supply and demand fixing after each round (time step) the new price of the stock …
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…
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…
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…
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…
Optimizes timing for buying and selling assets with a trailing stop.
problem Timing optimal buy and sell points for assets with a trailing stop.
method General linear diffusion framework, optimal double stopping problem, numerical method.
result Proves the optimality of using a sell limit order in conjunction with the trailing stop.
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…
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 propose and study a simple stochastic model for the dynamics of a limit order book, in which arrivals of market order, limit orders and order cancellations are described in terms of a Markovian queueing system. Through its analytical tractability, the model allows to obtain analytical expressions for various quantit…
We consider a stochastic model for the dynamics of the two-sided limit order book (LOB). Our model is flexible enough to allow for a dependence of the price dynamics on volumes. For the joint dynamics of best bid and ask prices and the standing buy and sell volume densities, we derive a functional limit theorem, which …
We generalise the description of the dynamics of the order book of financial markets in terms of a Brownian particle embedded in a fluid of incoming, exiting and annihilating particles by presenting a model of the velocity on each side (buy and sell) independently. The improved model builds on the time-averaged number …
Deep learning model improves financial return forecasting using LOBs.
problem Forecasting financial returns using Limit Order Books.
method Developed a deep learning architecture for simultaneous quantile regression of buy and sell positions.
result The model provides improved robustness and excellent performance in predicting financial returns.
Reinforcement learning optimizes market making decisions.
problem Optimizing market making decisions in cryptocurrency markets.
method Multi-agent reinforcement learning framework with two agents: macro-agent and micro-agent.
result The proposed framework leads to successful trades in the Bitcoin market.
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.
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.
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 investigate the statistical properties of the EBS order book for the EUR/USD and USD/JPY currency pairs and the impact of a ten-fold tick size reduction on its dynamics. A large fraction of limit orders are still placed right at or halfway between the old allowed prices. This generates price barriers where the best …
Optimal market making strategy with price forecasts reduces inventory costs and spreads.
problem Optimal market making strategy with price forecasts reduces inventory costs and spreads.
method Modeling market making strategy with linear price impact, random slope and intercept, and simultaneous order arrivals.
result Simultaneous order arrivals and price forecasts reduce inventory costs and spreads.
Simple model of a limit order-driven marketcond-mat.stat-mech
We introduce and study a simple model of a limit order-driven market. Traders in this model can either trade at the market price or place a limit order, i.e. an instruction to buy (sell) a certain amount of the stock if its price falls below (raises above) a predefined level. The choice between these two options is pur…
We consider a finite-horizon market-making problem faced by a dark pool that executes incoming buy and sell orders. The arrival flow of such orders is assumed to be random and, for each transaction, the dark pool earns a per-share commission no greater than the half bid-ask spread. Throughout the entire period, the mai…
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…
We propose a model for the dynamics of a limit order book in a liquid market where buy and sell orders are submitted at high frequency. We derive a functional central limit theorem for the joint dynamics of the bid and ask queues and show that, when the frequency of order arrivals is large, the intraday dynamics of the…
A limit order book provides information on available limit order prices and their volumes. Based on these quantities, we give an empirical result on the relationship between the bid-ask liquidity balance and trade sign and we show that liquidity balance on best bid/best ask is quite informative for predicting the futur…
We consider a dynamic market model where buyers and sellers submit limit orders. If at a given moment in time, the buyer is unable to complete his entire order due to the shortage of sell orders at the required limit price, the unmatched part of the order is recorded in the order book. Subsequently these buy unmatched …
The order submission and cancelation processes are two crucial aspects in the price formation of stocks traded in order-driven markets. We investigate the dynamics of order cancelation by studying the statistical properties of inter-cancelation durations defined as the waiting times between consecutive order cancelatio…
An empirical behavioral model of price formationphysics.soc-ph
Although behavioral economics has demonstrated that there are many situations where rational choice is a poor empirical model, it has so far failed to provide quantitative models of economic problems such as price formation. We make a step in this direction by developing empirical models that capture behavioral regular…
We propose a framework for studying optimal market making policies in a limit order book (LOB). The bid-ask spread of the LOB is modelled by a Markov chain with finite values, multiple of the tick size, and subordinated by the Poisson process of the tick-time clock. We consider a small agent who continuously submits li…
Order cancellation process plays a crucial role in the dynamics of price formation in order-driven stock markets and is important in the construction and validation of computational finance models. Based on the order flow data of 18 liquid stocks traded on the Shenzhen Stock Exchange in 2003, we investigate the empiric…
Optimal trading tactics for small asset purchases.
problem Finding the best way to buy a small amount of asset quickly.
method Order book model and optimization of expected utility function.
result The optimal trading tactic outperforms naive strategies.
Comparing effects of price limit and circuit breaker in stock exchanges by an agent-based modelq-fin.CP
Study compares price limit and circuit breaker effects in stock markets.
problem Preventing rapid and steep price drops in stock exchanges.
method Agent-based model for financial market simulation.
result Price limit and circuit breaker have similar effects under same conditions, but price limit less effective with shorter limit time range.
The paper explores how latency affects limit order placement and adverse selection risk.
problem Limit order placement and adverse selection risk.
method Stochastic control framework to exploit liquidity imbalance and measure latency impact.
result The added value of exploiting liquidity imbalance is reduced by latency.
Optimal buying and selling times for homes in fluctuating interest rates.
problem Maximizing profit from buying and selling homes in a market with variable interest rates.
method Nested optimal stopping problem solved using a nonnegative concave majorant approach.
result Investor's optimal buying and selling strategies derived for CIR interest rates.
Modeling liquidity risk in financial markets using agent-based simulation.
problem Understanding and quantifying the costs of buying or selling assets in financial markets.
method Agent-based modeling (ABM) with Monte Carlo simulations to calculate transaction costs and price impacts.
result Emergent price impacts without oversimplification, providing a realistic framework for liquidity risk.
Study uses reinforcement learning to optimize trading strategies.
problem Developing an optimal execution strategy for traders.
method Reinforcement learning model using ABIDES simulator.
result Reinforcement learning model outperforms standard strategies.
We consider a dynamic market model of liquidity where unmatched buy and sell limit orders are stored in order books. The resulting net demand surface constitutes the sole input to the model. We prove that generically there is no arbitrage in the model when the driving noise is a stochastic string. Under the equivalent …
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.
Monopoly Market with Externality: an Analysis with Statistical Physics and Agent Based Computational Economicscond-mat.dis-nn
We explore the effects of social influence in a simple market model in which a large number of agents face a binary choice: 'to buy/not to buy' a single unit of a product at a price posted by a single seller (the monopoly case). We consider the case of 'positive externalities': an agent is more willing to buy if the ot…
The paper examines logistic regression in sparse network settings, improving inference under varying degrees of dyadic dependence.
problem Improving inference in logistic regression with sparse network data.
method Sparse network asymptotics, martingale central limit theorem, variance decomposition.
result Sparse network asymptotics lead to better variance estimators for logistic regression.
In financial markets, liquidity is not constant over time but exhibits strong seasonal patterns. In this article we consider a limit order book model that allows for time-dependent, deterministic depth and resilience of the book and determine optimal portfolio liquidation strategies. In a first model variant, we propos…
DSLOB creates synthetic LOB data for benchmarking forecasting algorithms under distributional shifts.
problem Challenges in dealing with out-of-distribution limit order book data.
method Multi-agent market simulator to create labeled synthetic LOB dataset with and without market stress.
result Demonstrates the need for robust forecasting algorithms to handle distributional shifts.
A new model predicts price concavity and reversion after metaorder execution.
problem Modeling market response to exogenous trades on limit order books.
method Developed a Non-Markovian Zero Intelligence model with a time-weighted mid-price return function.
result The model predicts concave price paths and price reversion after metaorder execution.
WSB's investment advice significantly outperformed the S&P500 over 3 years, but not consistently.
problem Reliability of investment advice from WSB community.
method Data analysis of WSB posts and stock performance from 2019-2021.
result A WSB portfolio grew 200% over 3 years and 480% over 1 year, outperforming S&P500.
We describe a bottom-up framework, based on the identification of appropriate order parameters and determination of phase diagrams, for understanding progressively refined agent-based models and simulations of financial markets. We illustrate this framework by starting with a deterministic toy model, whereby indepe…
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…
New model captures asymmetric rough volatility with Zumbach effect.
problem Capturing asymmetric rough volatility and Zumbach effect.
method Proposes a bivariate QHawkes process to model asymmetric buying and selling actions.
result Derives a super-rough-Heston model preserving the Zumbach effect.