We study the optimal placement problem of a stock trader who wishes to clear his/her inventory by a predetermined time horizon t, by using a limit order or a market order. For a diffusive market, we characterize the optimal limit order placement policy and analyze its behavior under different market conditions. In part…
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48 results for “market order placements”
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.
To execute a trade, participants in electronic equity markets may choose to submit limit orders or market orders across various exchanges where a stock is traded. This decision is influenced by the characteristics of the order flow and queue sizes in each limit order book, as well as the structure of transaction fees a…
We identify and analyze statistical regularities and irregularities in the recent order flow of different NASDAQ stocks, focusing on the positions where orders are placed in the orderbook. This includes limit orders being placed outside of the spread, inside the spread and (effective) market orders. We find that limit …
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.
Modeling aggressive market order arrivals using Hawkes factor models.
problem Aggressive market order placements and their impact on stock prices.
method Bivariate marked Hawkes process with self-excitation and cross-excitation components.
result The Hawkes model with an exponential kernel produces better calibration than a monotonous exponential kernel.
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…
Explains financial market simulation mechanisms and agent behaviors.
problem Necessity of including fundamental value in multiagent financial market simulations.
method Discusses three methods for generating fundamental value and illustrates one Bayesian agent's estimation process.
result Presentation of two widely examined agents: Zero Intelligence and Heuristic Belief Learning.
Limit order market analysis and modelling: on an universal cause for over-diffusive pricescond-mat.stat-mech
We briefly review data analysis of the Island order book, part of NASDAQ, which suggests a framework to which all limit order markets should comply. Using a simple exclusion particle model, we argue that short-time price over-diffusion in limit order markets is due to the non-equilibrium of order placement, cancellatio…
We study the relaxation dynamics of the bid-ask spread and of the midprice after a sudden, large variation of the spread, corresponding to a temporary crisis of liquidity in a double auction financial market. We find that the spread decays very slowly to its normal value as a consequence of the strategic limit order pl…
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.
Adaptive market-making strategy improves profit by adjusting to order flow.
problem Optimizing market-making profits in a dynamic market environment.
method Closed-form solutions for optimal bid-ask spreads, modeling demand randomness, and adapting to market order behavior.
result Adaptive strategies outperform fixed and non-adaptive strategies.
This paper is split in three parts: first we use labelled trade data to exhibit how market participants accept or not transactions via limit orders as a function of liquidity imbalance; then we develop a theoretical stochastic control framework to provide details on how one can exploit his knowledge on liquidity imbala…
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.
Study uses DNM theory to detect early warning signals of market instability.
problem Detecting early warning signals of financial market instability.
method Applying Dynamical Network Marker (DNM) theory to trading data from the Tokyo Stock Exchange.
result Early warning signals of large price movements can be detected on a daily time scale.
We show that the statistics of spreads in real order books is characterized by an intrinsic asymmetry due to discreteness effects for even or odd values of the spread. An analysis of data from the NYSE order book points out that traders' strategies contribute to this asymmetry. We also investigate this phenomenon in th…
Trains a neural network to predict high-frequency trading outcomes.
problem Predicting the fill probability function for high-frequency trading.
method High-quality high-frequency data and neural network training with a weighted loss function.
result Strong state dependence properties of the fill probability function.
We develop an empirical behavioural order-driven (EBOD) model, which consists of an order placement process and an order cancellation process. Price limit rules are introduced in the definition of relative price. The order placement process is determined by several empirical regularities: the long memory in order direc…
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.
RL framework optimizes trading costs in noisy markets.
problem Optimal execution and placement in noisy markets.
method Dual-window Denoise PPO RL network, imitation learning, comprehensive market features, flexible action formulation.
result RL agents outperformed TWAP strategy in execution cost.
Paper proposes a COP model for Algo trading using LQR.
problem Complexities in child order placement in Algo trading.
method Stochastic LQR model for passive limit orders and aggressive takeout orders.
result Closed-form solutions for optimal child order placement.
In this chapter we review some recent results on the dynamics of price formation in financial markets and its relations with the efficient market hypothesis. Specifically, we present the limit order book mechanism for markets and we introduce the concepts of market impact and order flow, presenting their recently disco…
This work models market regimes using CTMSTOU and simulates trading policies.
problem Defining and understanding market regimes in finance.
method Discrete event time multi-agent market simulation with CTMSTOU model.
result Illustrates the importance of regime-awareness in trading policies.
We propose a parametric model for the simulation of limit order books. We assume that limit orders, market orders and cancellations are submitted according to point processes with state-dependent intensities. We propose new functional forms for these intensities, as well as new models for the placement of limit orders …
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.
Investigates optimal strategies for market makers using internal liquidity.
problem Optimizing strategies for market makers with internal liquidity.
method Investigates optimal multi-objective strategy for market makers with internal liquidity.
result Draws important qualitative insights for real-world trading.
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.
Project forecasts liquidity withdrawal using machine learning models.
problem Predicting liquidity withdrawal at individual stock levels.
method Tested a framework using machine learning models (AR, HAR, XGBoost) on Nasdaq MBO data.
result Introduced the Liquidity Withdrawal Index (LWI) for measuring liquidity removal.
Learning the Spoofability of Limit Order Books With Interpretable Probabilistic Neural Networksq-fin.TR
Real-time detection of spoofing in cryptocurrency exchanges using neural networks.
problem Detecting and mitigating spoofing activity in limit order books.
method Novel order flow variables based on multi-scale Hawkes processes and a probabilistic market manipulation gain model.
result 31% of large orders could spoof the market, highlighting the importance of posting distance in price formation.
What really causes large price changes?cond-mat.other
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…
New approach uses secants to improve sensor placement and feature selection for nonlinear systems.
problem Inadequacy of linear methods for minimal sensor placement and feature selection in nonlinear systems.
method Data-driven approach using secant vectors to develop greedy algorithms for robust, near-minimal reconstruction guarantees.
result Demonstrated on two problems where linear techniques fail, secant-based approach provides robust solutions.
Study analyzes market equilibrium returns with price impact and transaction costs.
problem Modeling equilibrium returns in markets with strategic order placement and transaction costs.
method Analyzes frictionless and transaction-cost markets, characterizes Nash equilibrium via FBSDEs.
result Equilibrium returns are affected by transaction costs, especially with noise traders.
Paper proposes efficient UAV placement for aerial base stations.
problem Efficient placement of UAVs as aerial base stations to serve varying traffic demand.
method Modeling UAV deployment as a non-cooperative game and using a learning-based algorithm to update UAV locations.
result Significant performance gains up to 52% and 74% in terms of throughput and dropped users compared to an optimized baseline algorithm.
Optimal bidding strategy for multi-platform ad auctions under budget constraints.
problem Optimizing ad placements for budget-constrained advertisers across multiple platforms.
method Developed an optimal bidding strategy for non-incentive-compatible auctions with budget constraints.
result Maximized total utility across auctions while satisfying budget constraints in expectation.
The Predictive Power of Zero Intelligence in Financial Marketscond-mat.stat-mech
Standard models in economics stress the role of intelligent agents who maximize utility. However, there may be situations where, for some purposes, constraints imposed by market institutions dominate intelligent agent behavior. We use data from the London Stock Exchange to test a simple model in which zero intelligence…
Study shows how wealth distribution leads to volatility clustering in speculative markets.
problem Volatility clustering in financial markets.
method Agent-based model of financial markets with heterogeneous wealth distribution and round-trip trading.
result Heterogeneous wealth distribution induces volatility clustering through market wealth redistribution.
We introduce a fully probabilistic framework of consumer product choice based on quality assessment. It allows us to capture many aspects of marketing such as partial information asymmetry, quality differentiation, and product placement in a supermarket.
A new model approximates complex functions in parameter space.
problem Complex and nonlinear functional regression problems.
method Mapping-to-Parameter function model with B-spline free knot placement.
result Robust knot placement algorithms improve model performance.
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 limit order book (LOB) model with dynamics that account for both the impact of the most recent order and the shape of the LOB. We present an empirical analysis showing that the type of the last order significantly alters the submission rate of immediate future orders, even after accounting for the state of…
Placeto learns efficient device placements for any neural network graph.
problem Finding efficient device placements for neural network training.
method Reinforcement learning approach with iterative placement improvements and graph embeddings.
result Placeto requires up to 6.1x fewer training steps and generalizes to unseen graphs.
Model shows liquidity stress crossover in market dynamics.
problem Identifying genuine market instabilities in agent-based models.
method Applied Bouchaud's phase-diagram method to a continuous-double-auction model.
result Emergent liquidity-stress crossover with specific tipping point parameters.
Study designs neural networks for fault localization, state estimation, and optimal PMU placement in power systems.
problem Fault localization, state estimation, and optimal PMU placement in power systems.
method Designs and compares various neural networks for fault localization, builds machine learning schemes for state estimation and parameter estimation, and designs an algorithm for optimal PMU placement.
result Comprehensive comparison of neural networks for fault localization shows that Graphical Convolutional NN and Neural Graph-based ODE perform best.
PLoP optimizes LoRA placement for efficient large model finetuning.
problem Improving efficiency of LoRA adaptation for large models.
method Intuitive theoretical analysis for automatic adapter placement.
result PLoP consistently outperforms existing placement strategies.
Market events such as order placement and order cancellation are examples of the complex and substantial flow of data that surrounds a modern financial engineer. New mathematical techniques, developed to describe the interactions of complex oscillatory systems (known as the theory of rough paths) provides new tools for…
ConvGNP improves sensor placement for climate monitoring.
problem Maximizing informativeness of environmental sensor placements in remote regions.
method Convolutional Gaussian neural processes (ConvGNP) for non-stationary spatial predictions.
result ConvGNP outperforms traditional GP models in predicting sensor performance and reducing uncertainty.
A new first-order sampler improves diffusion probabilistic model sampling quality.
problem The belief that first-order methods are inherently slower for diffusion probabilistic model sampling.
method A novel training-free, first-order sampler that approximates the forward-value evaluation via a one-step lookahead predictor.
result The proposed sampler provably approximates the ideal forward-value trajectory while retaining first-order convergence and can improve sample quality under the same NFE budget.
Limit order placement as an utility maximization problem and the origin of power law distribution of limit order pricesphysics.soc-ph
I consider the problem of the optimal limit order price of a financial asset in the framework of the maximization of the utility function of the investor. The analytical solution of the problem gives insight on the origin of the recently empirically observed power law distribution of limit order prices. In the framewor…