INHALT
The primary goal of this thesis is to conduct an in-depth analysis of how data-driven optimization techniques, specifically Multi-Criteria Decision Making (MCDM) methods, can be applied to portfolio optimization in corporate management. The research aims to explore and evaluate existing methodologies that integrate multiple decision criteria—such as return, risk, liquidity, and sustainability—within portfolio management strategies. A key focus is on understanding how MCDM frameworks, such as Analytical Hierarchy Process (AHP) or Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), can enhance decision-making processes in the context of uncertainty and risk. By analyzing current literature and case studies, this work seeks to provide a comprehensive overview of state-of-the-art approaches and their practical implications in corporate portfolio management, identifying both opportunities and limitations of integrating MCDM into data-driven optimization. |
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