Axiomatic Market Makingq-fin.MF
Axiomatizes the bid-ask market maker's quoting rule
problem Axiomatizing the quoting rule of a market maker
method Eight natural axioms and six environmental assumptions
result A unique three-parameter family emerges
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48 results for “inventory cost”
In this paper we complete and extend our previous work on stochastic control applied to high frequency market-making with inventory constraints and directional bets. Our new model admits several state variables (e.g. market spread, stochastic volatility and intensities of market orders) provided the full system is Mark…
New method reduces inventory inaccuracies by 10x, saving retailers 4% annually.
problem Inaccurate inventory records cost retailers 4% annually, and manual detection is impractical.
method Proposes a new anomaly detection method for low-rank Poisson matrices using cross-sectional data.
result Our approach reduces anomaly detection costs by up to 10x compared to existing methods.
Study Nash competition among dealers quoting prices to clients with unknown trading motives.
problem Adverse selection and inventory costs in dealer-client interactions.
method Analyzes one-shot Nash competition with unknown client type and inventory constraints.
result Unique symmetric Nash equilibrium exists and can be characterized by a nonlinear ODE.
We consider a repeated newsvendor problem where the inventory manager has no prior information about the demand, and can access only censored/sales data. In analogy to multi-armed bandit problems, the manager needs to simultaneously "explore" and "exploit" with her inventory decisions, in order to minimize the cumulati…
A new approach integrates inventory prediction and routing optimization for better supply chain management.
problem Optimizing efficient route selection in supply chain management with uncertain inventory demand.
method Decision-focused learning approach using neural networks to directly integrate inventory prediction and routing optimization.
result Direct integration of inventory prediction and routing optimization leads to better supply chain decisions.
We propose a continuous-time stock-flow consistent model for inventory dynamics in an economy with firms, banks, and households. On the supply side, firms decide on production based on adaptive expectations for sales demand and a desired level of inventories. On the demand side, investment is determined as a function o…
Brokers and an informed trader compete for liquidity, affecting trading costs and inventory risk.
problem How brokers and an informed trader manage liquidity and trading costs.
method Sequential Stackelberg game, solving for trading strategies, numerical solutions.
result Equilibrium strategies and liquidity prices determined, not Pareto efficient.
Study finds inventory inaccuracies are linked to store activity and product perishability.
problem Inventory record inaccuracy in grocery retailing environments.
method Analysis of 24,000 SKUs across 11 stores, field quasi-experiment on audits.
result Inventory audits can boost sales by 11%, especially for perishable items.
Bayesian optimization improves forest inventory sampling using remote sensing data.
problem Optimizing forest inventory sampling in large areas with limited data.
method Bayesian optimization applied to RS data for improved sampling design.
result The proposed method outperforms baseline methods in terms of MSE values.
We consider a stochastic inventory control problem under censored demands, lost sales, and positive lead times. This is a fundamental problem in inventory management, with significant literature establishing near-optimality of a simple class of policies called ``base-stock policies'' for the underlying Markov Decision …
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.
Study on inventory control with changing demand, proposing adaptive algorithms.
problem Inventory control with non-stationary demand distributions.
method Adaptive online algorithms optimizing base-stock policies.
result Sharp separation in adaptability across different inventory models.
Deep neural networks optimize inventory decisions in complex supply chains.
problem Optimizing inventory decisions in stochastic multi-echelon supply chains.
method Pairwise modeling and DNN agents for order-up-to levels.
result The method performs better than alternate methods in general supply chain networks.
In this paper we study a continuous time stochastic inventory model for a commodity traded in the spot market and whose supply purchase is affected by price and demand uncertainty. A firm aims at meeting a random demand of the commodity at a random time by maximizing total expected profits. We model the firm's optimal …
Study on HFTs' interactions with a large trader using mean field game theory.
problem Interactions between high-frequency traders and a large trader executing assets at discrete times.
method Modeling HFTs' behavior using a jump process and solving the equilibrium through mean field game approach.
result Inventory-averse HFTs lower LT's costs when market impact is large.
The paper analyzes how open-end fund sales affect prices and returns.
problem How open-end fund sales impact prices and returns.
method Continuous-time market-clearing model to derive expected-return restrictions.
result Forced-sale pressure predicts actual fund selling and positive returns.
An option market maker incurs funding costs when carrying and hedging inventory. To hedge a net long delta inventory, for example, she pays a fee to borrow stock from the securities lending market. Because of haircuts, she posts additional cash margin to the lender which needs to be financed at her unsecured debt rate.…
Paper introduces privacy-preserving inventory policy learning for feature-based newsvendor with unknown demand.
problem Privacy-preserving inventory policy learning for feature-based newsvendor with unknown demand distribution and nonsmooth loss function.
method Developed a clipped noisy gradient descent algorithm based on convolution smoothing for optimal inventory estimation within f-differential privacy framework.
result Achieved privacy-preserving optimal inventory policy with provable privacy guarantees and desirable statistical precision.
Optimal vehicle repositioning policy found for shared mobility services.
problem Matching fixed supply with spatial customer demand under uncertain and correlated demand.
method Base-stock repositioning policy, asymptotic optimality, regret analysis, adaptive repositioning algorithm.
result Surrogate Optimization and Adaptive Repositioning algorithm achieves optimal regret of .
Study on inventory management under uncertainty using smooth ambiguity preference.
problem Managing inventory under Knightian uncertainty with smooth ambiguity preference.
method Demonstrates continuous-time smooth ambiguity as the infinitesimal limit of Kalman-Bucy filtering with recursive robust utility. Solves forward-backward stochastic differential equations with quadratic growth to determine cost function. Derives value function and optimal control policy using variational inequalities and viscosity solutions. Transforms problem into two-dimensional singular control.
result Ambiguity drives decision-makers to act earlier, reducing the continuation region.
Unified framework connects two market-making models, revealing their underlying equivalence.
problem Independent calibration of two market-making frameworks (Avellaneda-Stoikov and Cartea-Jaimungal).
method Axiomatic approach to market preference functional, showing equivalence under specific conditions.
result Avellaneda-Stoikov and Cartea-Jaimungal frameworks are equivalent under certain conditions.
This paper studies the equilibrium price of an asset that is traded in continuous time between N agents who have heterogeneous beliefs about the state process underlying the asset's payoff. We propose a tractable model where agents maximize expected returns under quadratic costs on inventories and trading rates. The un…
We study a continuous-time version of the intermediation model of Grossman and Miller (1988). To wit, we solve for the competitive equilibrium prices at which liquidity takers' demands are absorbed by dealers with quadratic inventory costs, who can in turn gradually transfer these positions to an exogenous open market …
In this paper we extend the market-making models with inventory constraints of Avellaneda and Stoikov ("High-frequency trading in a limit-order book", Quantitative Finance Vol.8 No.3 2008) and Gueant, Lehalle and Fernandez-Tapia ("Dealing with inventory risk", Preprint 2011) to the case of a rather general class of mid…
Smart Contract-Enabled Procurement under Bounded Demand Variability: A Truncated Normal Approachq-fin.GN
Study develops smart contract framework for procurement under demand variability.
problem Operational and economic implications of smart contract adoption under moderate uncertainty.
method Multi-supplier model with endogenized adoption costs, supplier readiness, and inventory penalties; analytical and numerical results.
result Partial adoption strategies support moderate demand variability, while excessive digital investment reduces profitability.
Model optimizes trading strategy with unobservable toxicity.
problem Maximizing daily trading profit with unobservable toxicity.
method Formulated as a partially observable stochastic control problem, solved in two steps.
result P&L performance gap is negligible (0.01%) in all scenarios.
We study optimal trading in an Almgren-Chriss model with running and terminal inventory costs and general predictive signals about price changes. As a special case, this allows to treat optimal liquidation in "target zone models": asset prices with a reflecting boundary enforced by regulatory interventions. In this cas…
This paper offers a mathematical framework to manage inventory risk in FX cash markets.
problem Inventory risk in FX cash markets due to flow uncertainty and volatility.
method Mathematical framework and approximation techniques for scalability.
result Maximizing expected profit while controlling inventory risk.
Study optimal pricing and inventory control in dynamic settings with censored demand.
problem Optimal pricing and inventory control in dynamic settings with censored demand.
method Approximate optimal policy via high-order MDP, propose novel algorithms for solving Bellman equations.
result Established finite-sample regret bounds and demonstrated efficacy through numerical experiments.
Policy certifies inventory levels meeting service requirements.
problem Maintaining stock levels meeting service requirements despite unknown demand.
method Data-driven order policy using online learning and integral action.
result Valid inference method for finite samples.
This paper optimizes perpetual contract liquidity by accounting for funding rates.
problem Optimal liquidity provision for perpetual contracts with stochastic funding rates.
method Formulated a control problem, solved with a HJB scheme, and calibrated on real data.
result Funding-aware market making improves performance and reduces inventory risk.
A contextual bandit method evaluates and improves inventory control policies.
problem Evaluating and improving periodic review inventory control policies with nonstationary demand.
method Contextual bandit-based algorithm to evaluate and tweak policies.
result The method achieves favorable guarantees in both theory and practice.
Modeling dealer competition, internalisation and externalisation impact market dynamics and costs.
problem Understanding and optimizing dealer competition in risky asset markets.
method Variational approach to derive Nash equilibrium for dealer competition.
result Dealers using internalisation are incentivized to increase externalisation, leading to higher hedging costs and wider spreads.
Proposes a Big Data framework for SC forecasting, including data preprocessing and machine learning.
problem Improving SC forecasting accuracy and efficiency.
method Data collection, preprocessing, machine learning model training, hyperparameter tuning, performance evaluation.
result Optimized SC forecasting models enhance workforce, inventory, and overall SC performance.
Short-term incentives lead to riskier trading strategies.
problem Optimal execution with performance barriers.
method Analyzes the impact of short-term performance incentives on trading behavior.
result Short-term incentives result in more aggressive but less risky trading strategies in the short term, but poorer performance over long periods.
Value-Based Inventory Managementq-fin.PM
The basic financial purpose of a firm is to maximize its value. An inventory management system should also contribute to realization of this basic aim. Many current asset management models currently found in financial management literature were constructed with the assumption of book profit maximization as basic aim. H…
We consider a continuous-time model for inventory management with Markov modulated non-stationary demands. We introduce active learning by assuming that the state of the world is unobserved and must be inferred by the manager. We also assume that demands are observed only when they are completely met. We first derive t…
Algorithm improves reinforcement learning in MDPs with partial order policies.
problem Improving reinforcement learning in MDPs with partial order policies.
method Epoch-based reinforcement learning algorithm leveraging a partial order over policy class.
result Achieves an regret bound, independent of state and action space sizes.
The paper analyzes optimal execution strategies for traders with inventory processes influenced by Brownian motion.
problem Optimal execution strategies for traders with inventory processes influenced by Brownian motion.
method Statistical tests and empirical analysis of intra-day data from the Toronto Stock Exchange.
result Empirical evidence supports the presence of a non-zero Brownian motion component in inventories and wealth processes.
Agent optimizes perpetual contract liquidation with transaction costs and risk.
problem Optimizing perpetual contract liquidation with transaction costs and risk.
method Solving stochastic control problem for optimal trading strategy.
result Closed-form expression and approximations for optimal strategy.
Optimal hidden-target learning for online inventory optimization on general convex sets.
problem Online inventory optimization (OIO) on arbitrary bounded convex capacity sets.
method Maintaining a hidden target and projecting it onto the feasible order-up-to set.
result The method improves the best known regret guarantee for OIO on general convex sets from inverse to inverse-square-root dependence on the common-demand probability.
Optimal trading using signalsq-fin.TR
In this paper we propose a mathematical framework to address the uncertainty emergingwhen the designer of a trading algorithm uses a threshold on a signal as a control. We rely ona theorem by Benveniste and Priouret to deduce our Inventory Asymptotic Behaviour (IAB)Theorem giving the full distribution of the inventory …
We study the cross-correlation matrix of inventory variations of the most active individual and institutional investors in an emerging market to understand the dynamics of inventory variations. We find that the distribution of cross-correlation coefficient has a power-law form in the bulk followed by …
Paper tackles inventory management with deep learning, improving performance and adherence to constraints.
problem Managing inventory with limited resources and constraints.
method Proposes a novel method to sample from a distribution of possible constraint paths, extends exo-IDP formulation, introduces neural coordinator, and uses modified DirectBackprop algorithm.
result Deep reinforcement learning policies with a neural coordinator outperform classic baselines in terms of performance and adherence to constraints.
Model cash management under ambiguity using maxmin preferences and diffusion.
problem Optimizing cash reserves in the presence of ambiguity.
method Singular control model with maxmin preferences, verified using Dynkin games.
result Higher expected costs and narrower inaction region under increased ambiguity.
This paper tackles inventory control with general arrival dynamics and post-processing, improving profitability.
problem Inventory control with arbitrary arrival dynamics and post-processing constraints.
method Formulated as an exogenous decision process, incorporating deep generative models for arrivals, and applying supervised learning techniques.
result Improves profitability over production baselines and real-world A/B test data.
Study risk-sensitive market making with entropy regularization for better quote control.
problem Risk-sensitive market making with exponential utility and penalties.
method Entropy-regularized certainty-equivalent Bellman policies for discrete-time market dynamics.
result Proves convergence and performance bounds for entropy-regularized policies.