Portfolio optimization with robust contextual mean absolute deviation model

作者: Huang, YW
单位: 华中科技大学经济学院
期刊：Applied Economics Letters
Abstract: This article introduces a novel portfolio optimization framework that integrates the mean absolute deviation model with contextual decision-making and robust optimization techniques. First, the mean absolute deviation model is improved by incorporating contextual information. To further consider the uncertainty of contextual information, this article proposes Robust Contextual Mean Absolute Deviation (RCMAD) model by optimizing the worst-case performance under uncertainty. Furthermore, it is theoretically proven that the RCMAD model is equivalent to a linear program, allowing it to be efficiently solved by off-the-shelf solvers. Finally, real financial data is used to empirically validate the performance of the proposed RCMAD model.
DOI: 10.1080/13504851.2025.2480725
链接: https://doi.org/10.1080/13504851.2025.2480725