In this paper, we propose formulations and algorithms for robust portfolio optimization under both aleatory uncertainty (i.e., natural variability) and epistemic uncertainty (i.e., imprecise probabilistic information) arising from interval data. Epistemic uncertainty is represented using two approaches: (1) moment bounding approach and (2) likelihood-based approach. This paper first proposes a ...

index tracking is the process of developing a portfolio that reproduces the performance of an index. the tracker portfolio has relatively good diversity and low turnover and low transaction costs. in this paper we applied a binary programming model for index tracking problem. in this model the number of assets for portfolio construction is defined by portfolio manager. the robust optimization f...

One of the most important problems faced by every investor is asset allocation. An investor during making investment decisions has to search for equilibrium between risk and returns. Risk ‎and ‎return are uncertain parameters in ‎the ‎suggested portfolio optimization models and should be estimated to solve the‎problem. The estimation might‎ lead ‎to ‎large ‎error in the final decision. One of t...

this paper proposes a family of robust counterpart for uncertain linear programs (lp) which is obtained for a general definition of the uncertainty region. the relationship between uncertainty sets using norm bod-ies and their corresponding robust counterparts defined by dual norms is presented. those properties lead us to characterize primal and dual robust counterparts. the researchers show t...

1. Abstract In practical engineering applications, there exist two different types of uncertainties: aleatory and epistemic uncertainties. Aleatory uncertainty is classified as objective and irreducible uncertainty with sufficient information on input uncertainty data, whereas epistemic uncertainty is a subjective and reducible uncertainty that stems from lack of knowledge on input uncertainty ...

Many portfolio optimization problems deal with allocation of assets which carry a relatively high market price. Therefore, it is necessary to determine the integer value of assets when we deal with portfolio optimization. In addition, one of the main concerns with most portfolio optimization is associated with the type of constraints considered in different models. In many cases, the resulted p...

this paper discusses the portfolio selection based on robust optimization. since the parameters values of the portfolio optimization problem such as price of the stock, dividends, returns, etc. of per share are unknown, variable and their distributions are uncertain because of the market and price volatility, therefore, there is a need for the development and application of methodologies for de...

A novel data-driven stochastic robust optimization (DDSRO) framework is proposed for optimization under uncertainty leveraging labeled multi-class uncertainty data. Uncertainty data in large datasets are often collected from various conditions, which are encoded by class labels. Machine learning methods including Dirichlet process mixture model and maximum likelihood estimation are employed for...