A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Optimal order execution strategies for brokers under reference benchmarks.
Optimal trade execution strategies show adaptive methods reduce costs.
The volume weighted average price (VWAP) execution strategy is well known and widely used in practice. In this study, we explicitly introduce a trading volume process into the Almgren-Chriss model, which is a standard model for optimal execution. We then show that the VWAP strategy is the optimal execution strategy for…
The paper analyzes trade execution strategies for large traders in a stochastic market environment.
Optimal trading strategies in fluctuating financial markets are analyzed using complex mathematical models.
Paper uses DDPG to learn optimal execution strategies in dynamic markets.
Develops strategies to minimize trading costs in volatile markets.
When executing their orders, investors are proposed different strategies by brokers and investment banks. Most orders are executed using VWAP algorithms. Other basic execution strategies include POV (also called PVol) -- for percentage of volume --, IS -- implementation shortfall -- or Target Close. In this article ded…
Study shows randomized strategies can't be Nash equilibria in markets with transient price impact.
The paper proposes a new order slicing strategy to reduce market impact in large-volume trading.
Trading algorithms that execute large orders are susceptible to exploitation by order anticipation strategies. This paper studies the influence of order anticipation strategies in a multi-investor model of optimal execution under transient price impact. Existence and uniqueness of a Nash equilibrium is established unde…
We propose a design for schedule-based execution trading strategies based on uncertainty bands. This formulation: 1) simplifies strategy specification and implementation; 2) provides for flexible allocation among passive, opportunistic, aggressive, and dark pool crossing execution tactics; 3) allows for rapid enhanceme…
Develops a new model to optimize trading in markets.
Study uses reinforcement learning to optimize trading strategies.
LLMs translate natural language trading intents into correct option strategies using a domain-specific language.
Study analyzes optimal execution under uncertain volatility and liquidity.
In this short note, we study an optimization problem of expected implementation shortfall (IS) cost under general shaped market impact functions. In particular, we find that an optimal strategy is a VWAP (volume weighted average price) execution strategy when the market model is a Black-Scholes type with stochastic clo…
Optimizes stock execution costs using stochastic control theory.
Optimal trade execution in a fluctuating market with stochastic liquidity.
This paper optimizes trading strategies to minimize risk and maximize profit while accounting for market uncertainty.
RL agents optimize order execution in a realistic market simulation.
In this article, we consider the optimal execution problem associated to accelerated share repurchase contracts. When firms want to repurchase their own shares, they often enter such a contract with a bank. The bank buys the shares for the firm and is paid the average market price over the execution period, the length …
In a continuous-time model with multiple assets described by càdlàg processes, this paper characterizes superhedging prices, absence of arbitrage, and utility maximizing strategies, under general frictions that make execution prices arbitrarily unfavorable for high trading intensity. Such frictions induce a duality bet…
Optimizes trading large volumes of volatile assets with fast mean-reverting volatility.
Study shows portfolio trading impacts intraday liquidity and optimizes execution strategies.
We model the impact costs of a strategy that trades a basket of correlated instruments, by extending to the multivariate case the linear propagator model previously used for single instruments. Our specification allows us to calibrate a cost model that is free of arbitrage and price manipulation. We illustrate our resu…
ICON-OCnet solves optimal execution problems with neural networks and few examples.
Optimizes large stock order execution with LSTM neural networks.
Short-term incentives lead to riskier trading strategies.
Optimized execution model using interbank and internal liquidity.
In the present paper, we study the optimal execution problem under stochastic price recovery based on limit order book dynamics. We model price recovery after execution of a large order by accelerating the arrival of the refilling order, which is defined as a Cox process whose intensity increases by the degree of the m…
Optimal execution strategy for merger & acquisition contracts with price impact.
Study optimal execution in financial markets with constraints.
Paper proposes a novel policy distillation method for better order execution in noisy markets.
This paper introduces a high frequency trade execution model to evaluate the economic impact of supervised machine learners. Extending the concept of a confusion matrix, we present a 'trade information matrix' to attribute the expected profit and loss of the high frequency strategy under execution constraints, such as …
RL optimizes trading algorithms to reduce market impact and costs.
The study examines how brokers' identity affects their trading strategies on the Toronto Stock Exchange.
This paper deals with numerical solutions to an impulse control problem arising from optimal portfolio liquidation with bid-ask spread and market price impact penalizing speedy execution trades. The corresponding dynamic programming (DP) equation is a quasi-variational inequality (QVI) with solvency constraint satisfie…
We study optimal trade execution strategies in financial markets with discrete order flow. The agent has a finite liquidation horizon and must minimize price impact given a random number of incoming trade counterparties. Assuming that the order flow is given by a Poisson process, we give a full analysis of the prop…
TensorOpt finds optimal parallelization strategies for DNN training.
We study an optimal execution problem in the presence of market impact where the security price follows a geometric Ornstein-Uhlenbeck process, which implies the mean-reverting property, and show that the optimal strategy is a mixture of initial/terminal block liquidation and gradual intermediate liquidation. The mean-…
Study uses SGD to find near-optimal execution cost policies in dynamic markets.
We consider optimal execution strategies for block market orders placed in a limit order book (LOB). We build on the resilience model proposed by Obizhaeva and Wang (2005) but allow for a general shape of the LOB defined via a given density function. Thus, we can allow for empirically observed LOB shapes and obtain a n…
Study uses agent-based simulation to analyze impact of OBI strategy on financial markets.
Study compares high-frequency trading vs. buy and hold in stock markets with and without execution delay.
In this study, we introduce an explicit trading-volume process into the Almgren-Chriss model, which is a standard model for optimal execution. We propose a penalization method for deriving a verification theorem for an adaptive optimization problem. We also discuss the optimality of the volume-weighted average-price st…
We extend the framework of trading strategies of Gatheral [2010] from single stocks to a pair of stocks. Our trading strategy with the executions of two round-trip trades can be described by the trading rates of the paired stocks and the ratio of their trading periods. By minimizing the potential cost arising from cros…
In this article, we develop a general framework to study optimal execution and to price block trades. We prove existence of optimal liquidation strategies and we provide regularity results for optimal strategies under very general hypotheses. We exhibit a Hamiltonian characterization for the optimal strategy that can b…