This study presents an enhanced framework for portfolio performance evaluation by refining Jensens alpha to incorporate dynamic conditional beta. Traditional models rely on static beta assumptions, often overlooking the time-varying nature of portfolio risk and its influence on performance metrics. By integrating dynamic conditional beta, this research provides a more precise measure of risk-adjusted returns, offering deeper insights into investment performance. The methodology is applied to subsidiaries of the Golrang Industrial GroupKimiatek, Padideh, Ofoghe Kourosh, and Pakshoo-analyzing their financial performance under varying market conditions. The results demonstrate the superiority of adjusted dynamic conditional Jensens alpha, particularly during periods of heightened market volatility. This advancement equips investors and portfolio managers with more reliable performance assessment tools, supporting strategic decision-making and improving risk-return analysis. By addressing limitations in traditional evaluation models, this study contributes to the development of robust financial metrics and emphasizes the importance of incorporating time-sensitive risk factors for comprehensive portfolio analysis.
Bayati et al. (Tue,) studied this question.