TY - UNPB

T1 - Bayesian Bandit Allocation with Risk-Aware Thompson Sampling

AU - Вукович, Дарко

AU - Далал, Адель

AU - Зиновьев, Вячеслав Андреевич

PY - 2026

Y1 - 2026

N2 - We develop a portfolio allocation framework that recasts monthly ETF rebalancing as a contextual bandit problem. At each decision date, a Bayesian linear model with shrinkage priors – Gaussian ridge and regularized horseshoe – produces a full posterior distribution over one-month-ahead expected returns conditioned on macroeconomic and factor predictors. The contribution is a portfolio learning system designed for realistic conditions where uncertainty, risk control, and execution frictions interact rather than being treated as separate modules.

AB - We develop a portfolio allocation framework that recasts monthly ETF rebalancing as a contextual bandit problem. At each decision date, a Bayesian linear model with shrinkage priors – Gaussian ridge and regularized horseshoe – produces a full posterior distribution over one-month-ahead expected returns conditioned on macroeconomic and factor predictors. The contribution is a portfolio learning system designed for realistic conditions where uncertainty, risk control, and execution frictions interact rather than being treated as separate modules.

M3 - Working paper

BT - Bayesian Bandit Allocation with Risk-Aware Thompson Sampling

ER -