| Abstrakt: |
This study presents a novel approach for ranking problems, named BWM-MOORA-N, which consists of a hybrid MCDM/A method based on BWM and MOORA. The proposed method is utilized to analyze a set of investment funds, considering the preferences of a financial analyst. The BWM-MOORA-N method utilizes BWM to determine the criteria weights, MOORA to order the alternatives, and an additional normalization procedure to ensure that the results are evaluated on a 0–1 scale. The final normalization prevents the presence of negative global evaluations, which makes it easier to interpret and visualize the results. In the application, 42 investment funds are ordered based on a set of criteria derived from real data provided by an investment advisory company. The decision-makers preferences, according to a financial expert, were modeled using an unstructured process of interviews conducted by a decision analyst. The results of the study include comparisons to risk-return metrics and a ranking obtained using a reference point approach that incorporates the Tchebycheff distance concept. Additionally, experiments were conducted to vary possible BWM input comparisons, and statistics from over 1000 simulations are provided. The proposed method produces coherent results, including a ranking of funds that reflects the significant importance given by the decision-maker to profitability and risk criteria. Furthermore, the additional normalization proves to be suitable for practical purposes. Finally, the simulation results provide detailed information for each alternative, including the mean and standard deviations of the global evaluation and the mode of the ranking position. [ABSTRACT FROM AUTHOR] |
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