arXiv research

In this paper, we provide a representation theorem for dynamic capital allocation under It{ô}-L{é}vy model. We consider the representation of dynamic risk measures defined under Backward Stochastic Differential Equations (BSDE) with generators that grow quadratic-exponentially in the control variables. Dynamic capital …

Study dynamic Pareto-optimal allocations in multi-period economies with time-consistent risk measures.

Study optimizes resource allocation in noisy systems for better control.

Paper introduces a new method for allocating capital based on risk measures from ruin theory.

New dynamic allocation methods for multi-armed bandit models.

Optimal dynamic allocation of carbon allowances reduces emissions efficiently.

Onflow optimizes portfolio allocation with gradient flows, robust to transaction fees.

Robo-advisors use MPC to create dynamic investment strategies.

Optimizes retirement spending and asset allocation to maximize withdrawals and shortfall.

Model predicts global financial market risks and asset allocation.

In this letter, an age of information (AoI)-aware transmission power and resource block (RB) allocation technique for vehicular communication networks is proposed. Due to the highly dynamic nature of vehicular networks, gaining a prior knowledge about the network dynamics, i.e., wireless channels and interference, in o…

This study uses HMM and RL to dynamically allocate equities, Treasuries, and gold based on market regimes.

Adaptive RL optimizes testing resource allocation for dynamic software environments.

Develops a new method for risk diversification using dynamic risk measures.

Dynamic risk factor model improves portfolio performance in high dimensions.

We consider the problem of optimal budget allocation for crowdsourcing problems, allocating users to tasks to maximize our final confidence in the crowdsourced answers. Such an optimized worker assignment method allows us to boost the efficacy of any popular crowdsourcing estimation algorithm. We consider a mutual info…

Blend-ASC improves self-consistency efficiency by dynamically allocating samples, reducing costs.

Optimal asset allocation strategy outperforms stochastic benchmark.

This paper tackles federated incremental learning with dynamic memory allocation for improved model performance in non-IID data.

This paper proposes a decentralized reinforcement learning method for multi-agent resource allocation.

The paper analyzes risk measures and optimal reserve allocation strategies.

Optimal online learning for joint pricing and resource allocation.

This paper proposes a new method to optimize portfolio allocation with transaction costs using Wiener chaos expansion.

Optimizes portfolios with constraints and stochastic factors, deriving explicit solutions.

Dynamic model considers private asset markets' complexities.

The article uses dynamic factor allocation to improve portfolio performance by integrating regime-switching signals.

A new model forecasts optimal portfolio weights from high-frequency data.

This research improves forecasting and testing of risk contributions using Expected Shortfall.

Framework uses RL with dynamic embedding to outperform benchmarks in volatile markets.

This paper optimizes cybersecurity resource allocation in networks with heterogeneous attacker and defender valuations.

We present a simulation-and-regression method for solving dynamic portfolio allocation problems in the presence of general transaction costs, liquidity costs and market impacts. This method extends the classical least squares Monte Carlo algorithm to incorporate switching costs, corresponding to transaction costs and t…

Study dynamic asset allocation in incomplete markets using game theory and nonlocal BSDEs.

Modeling dynamic groundwater markets with price formation and trading strategies.

Portfolio management problems are often divided into two types: active and passive, where the objective is to outperform and track a preselected benchmark, respectively. Here, we formulate and solve a dynamic asset allocation problem that combines these two objectives in a unified framework. We look to maximize the exp…

This study uses OPE methods to quickly assess auction policies.

Meta-DRL improves resource allocation in O-RAN networks.

Study allocates resources to strategic agents while balancing cost and incentives.

RL optimizes resource allocation in MG by balancing experience and exploration.

The paper analyzes how wealth affects investment strategies in incomplete markets.

Batch Thompson Sampling reduces exploration-exploitation trade-off in online decision making.

Paper uses DRL to optimize portfolios, balancing risk and return.

In the coming years, the satellite broadband market will experience significant increases in the service demand, especially for the mobility sector, where demand is burstier. Many of the next generation of satellites will be equipped with numerous degrees of freedom in power and bandwidth allocation capabilities, makin…

Adaptive AI delegation framework for dynamic decision authority allocation.

Sequential processing biases asset allocation in artificial stock markets.

Scalar dynamic risk measures for univariate positions in continuous time are commonly represented as backward stochastic differential equations. In the multivariate setting, dynamic risk measures have been defined and studied as families of set-valued functionals in the recent literature. There are two possible extensi…

We introduce a generic solver for dynamic portfolio allocation problems when the market exhibits return predictability, price impact and partial observability. We assume that the price modeling can be encoded into a linear state-space and we demonstrate how the problem then falls into the LQG framework. We derive the o…

Optimizes trade execution with reinforcement learning for limit orders.

ANN-based dynamic portfolio optimization outperforms traditional methods.