فهرست نشریات علمی دسترسی آزاد ایران

The cryptocurrency market presents enticing yet high-risk investment opportunities, marked by rapid growth, extreme volatility, and substantial uncertainty. Traditional risk management models, which often rely on probabilistic assumptions and historical data, struggle to effectively capture the unique dynamics and unpredictability of this market. In light of these challenges, this paper examines the application of Conditional Drawdown at Risk (CDaR), a prominent downside risk measure, for optimizing cryptocurrency portfolios. Recognizing that portfolio optimization alone may not yield optimal results, this study emphasizes the importance of a rigorous asset pre-selection process to identify assets with strong fundamentals, growth potential, and resilience to market fluctuations. To address this need, we introduce a novel asset pre-selection approach using Multi-Attribute Decision Making (MADM) methods, enabling a systematic evaluation and selection of high-potential cryptocurrency assets prior to applying the CDaR optimization model. By addressing both asset quality and risk-adjusted allocation, this approach bridges a critical gap in current cryptocurrency portfolio management practices, providing a comprehensive tool that aligns with the unique demands of digital asset investing. Tested within the cryptocurrency market, this framework demonstrates promising results, underscoring its effectiveness as a tailored approach for navigating the complexities of digital asset investments