Scenario-based portfolio optimization via bootstrapping and machine learning methods: Theory development and empirical evidence from the Tehran Stock Market.

ABSTRACT: Predicting future returns and modeling return uncertainty are essential yet challenging tasks in portfolio optimization. To address these issues, this study proposes a hybrid approach combining machine learning for return prediction, bootstrapping for uncertainty modeling, and scenario optimization for portfolio selection in the presence of uncertainty. Bootstrap samples are used to generate multiple return trajectories via machine learning, with each trajectory treated as a distinct scenario in a scenario-based mean-variance optimization framework. Empirical results using data from the Tehran Stock Market demonstrate that the proposed model produces optimal portfolios that align more closely with those derived from actual prices, compared to those generated by traditional techniques. Our

SUBMITTER: Amini M 

PROVIDER: S-EPMC12919843 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature