arXiv research

Proposes a new sampling policy for ranking and selection problems.

Annealed Entropic Allocation improves ranking and selection by mitigating hard switching and improving finite-budget discrimination.

The paper introduces a portfolio construction method using Black-Litterman model and factors.

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

Unified formula for arbitrary liquidity operations in weighted AMMs

Deep learning improves portfolio management by optimizing asset weights.

Facing the FRTB, banks need to allocate their capital to each business units or risk positions to evaluate the capital efficiency of their strategies. This paper proposes two computationally efficient allocation methods which are weighted according to liquidity horizon. Both methods provide more stable and less negativ…

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

We discuss investment allocation to multiple alpha streams traded on the same execution platform with internal crossing of trades and point out differences with allocating investment when alpha streams are traded on separate execution platforms with no crossing. First, in the latter case allocation weights are non-nega…

New portfolios outperform traditional methods by using factor weights.

New algorithm optimizes online network resource allocation with long-term constraints.

Diversified risk parity strategies outperform equally-weighted portfolios in various asset universes.

Improves content allocation in educational platforms with sparse data.

The paper proposes a machine learning approach for state-dependent asset allocation.

Optimal strategy found for identifying best arm in bandits with small gap.

Develops FGL for better portfolio allocation under common factor influence.

New method improves portfolio allocation using local Gaussian correlation.

The study shows how probability weighting can lead to betting in a risk-averse economy.

RL learns to ignore factors in factor investing portfolios.

The paper extends game theory using Hodge theory on graphs.

Best-of-$\infty$ improves LLM performance by efficiently allocating inference-time computation.

Paper improves asset allocation using machine learning for regime detection.

clusterBMA combines clustering results from multiple models using Bayesian model averaging.

Modified CTGAN-Plus-Features method optimizes asset allocation with CVaR constraint.

PT network optimizes asset weights without forecasting returns.

Unified framework for optimizing portfolios with distributions over weights, returns, and parameters.

We present an online approach to portfolio selection. The motivation is within the context of algorithmic trading, which demands fast and recursive updates of portfolio allocations, as new data arrives. In particular, we look at two online algorithms: Robust-Exponentially Weighted Least Squares (R-EWRLS) and a regulari…

Scheduling and power allocation improve federated learning efficiency in NOMA networks.

The paper proposes an online algorithm for network resource allocation with reduced costs.

This paper proposes a continuous timing strategy for growth vs. defensive style allocation.

A new asset allocation model uses Markov states from clustered efficient frontier coefficients.

The paper tackles resource allocation for arms with unknown and random rewards, achieving optimal regret bounds.

The paper proposes a new portfolio allocation method combining RMT and machine learning.

Radio on Free Space Optics (RoFSO), as a universal platform for heterogeneous wireless services, is able to transmit multiple radio frequency signals at high rates in free space optical networks. This paper investigates the optimal design of power allocation for Wavelength Division Multiplexing (WDM) transmission in Ro…

Deep RL optimizes US stock allocations with better performance.

The paper introduces new portfolio rules beyond mean-variance, addressing asymmetry and uncertainty.

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…

In these notes we discuss investment allocation to multiple alpha streams traded on the same execution platform, including when trades are crossed internally resulting in turnover reduction. We discuss approaches to alpha weight optimization where one maximizes P&L subject to bounds on volatility (or Sharpe ratio). The…

The paper optimizes stock portfolios with constraints based on performance attribution.

We give an explicit algorithm and source code for combining alpha streams via bounded regression. In practical applications typically there is insufficient history to compute a sample covariance matrix (SCM) for a large number of alphas. To compute alpha allocation weights, one then resorts to (weighted) regression ove…

The paper identifies a mesoscopic market structure and uses it to improve portfolio optimization.

We characterize the class of exchangeable feature allocations assigning probability $V_{n,k}\prod_{l=1}^{k}W_{m_{l}}U_{n-m_{l}}$ to a feature allocation of $n$ individuals, displaying $k$ features with counts $(m_{1},\ldots,m_{k})$ for these features. Each element of this class is parametrized by a countable matrix $V$…

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

Federated learning optimizes task and resource allocation in balloon networks.

Optimizes budgeted evaluations of LLMs by allocating queries to judges efficiently.

Framework for online resource allocation using social welfare functions.

DeepAries optimizes rebalancing intervals and asset allocations for better portfolio performance.

The paper introduces a new class of multivariate mixtures for actuarial applications.