Extended fuzzy portfolio optimization models considering financial uncertainties

The primary goal of investors in financial markets is to establish a balanced portfolio between risk and return. However, economic uncertainties and market fluctuations complicate this goal, necessitating more complex strategic approaches. Therefore, innovative portfolio optimization models that address the management of uncertainties have become essential. This study develops three new models based on fuzzy and hesitant fuzzy approaches to manage uncertain financial data more effectively. These innovative models, built upon the Konno-Yamazaki framework, analyze the closing values of stocks listed in the Borsa Istanbul 50 (BIST 50) index to offer alternative portfolio options for investors with different risk profiles. Specifically, these models utilize the mean absolute deviation as a risk measure, enabling investors to achieve the maximum expected return at a given risk level or minimize risk at a specified return level, thereby facilitating more informed decision-making. In conclusion, the portfolio models developed in this study support investors' strategic approaches in the face of uncertainties, making risk management processes more flexible and reliable. The alternative portfolio options offered by these models aim to provide more rational and effective solutions in financial decision-making processes.