This thesis addresses the problem of optimizing asset allocation in a cryptocurrency portfolio using multi-criteria decision making methods. Due to high volatility, non-stationarity and heterogeneous characteristics of cryptocurrencies, classical portfolio approaches based solely on return and risk often fail to capture all relevant aspects of asset evaluation. Therefore, the AHP, TOPSIS and PROMETHEE~II methods are applied, where AHP is used to determine criteria weights, while the multi-criteria methods are used to rank alternatives and construct cryptocurrency portfolios by simultaneously considering both financial and market-related criteria. The empirical analysis shows that the results of multi-criteria evaluation are sensitive to changes in preferences, market conditions and the selected set of criteria while at the same time providing a more transparent and interpretable framework for cryptocurrency ranking compared to the traditional models such as mean-variance model.
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