Abstract
Research on mean–variance portfolio optimization (MVPO) has made significant progress in the application of advanced solution algorithms or multiple criteria decision-making methods to achieve superior outcomes. However, there is little specific guidance on how to approach individually preferred optimization bounded by rationality. Therefore, the main goal of this study is to extend MVPO research by incorporating an investor’s preferences and rational expectations during optimizations. Using a set of equities from the Taiwan stock market, this study proposes an interactive MVPO model guided by personal risk-return preferences and bounded by rational fuzzy constraints. Furthermore, this study adopts a rule-based bipolar model to prioritize candidate equities and support the interactive optimization process. This hybrid model is an early attempt to investigate bounded rationality in MVPO, which integrates multiple rule-based decision-making with multiple objective decision-making. The obtained portfolio outperformed two benchmark indexes in 2020, suggesting the feasibility of this approach.
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