Multicriteria Decision Aid in Financial Portfolio Management

Multicriteria decision aid (MCDA) has become a cornerstone of modern portfolio management, addressing the complex trade-offs between return, risk and qualitative factors such as corporate reputation or sustainability. Rather than relying on a single objective function, MCDA frameworks accommodate heterogeneous criteria through methods that integrate quantitative risk-return models with expert judgements. Recent advances emphasise the treatment of uncertainty via stochastic and fuzzy extensions, enabling portfolio managers to capture both statistical variability and imprecision in qualitative assessments. Hybrid approaches combine well-established tools—such as the Analytic Hierarchy Process, TOPSIS, ELECTRE and VIKOR—with metaheuristics or Bayesian models to derive robust asset allocations. These developments have broadened the applicability of MCDA across developed and emerging markets, offering decision-makers transparent, replicable pathways to balance multiple stakeholder objectives and adapt to volatile financial environments.

Research from Nature Portfolio

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Multicriteria Decision Aid in Financial Portfolio Management publication trend

The graph below shows the total number of articles in multicriteria decision aid in financial portfolio management across all publications each year (not limited to Nature Index journals).

Technical terms

Multicriteria Decision Analysis (MCDA): a suite of techniques for evaluating alternatives against multiple, often conflicting, quantitative and qualitative criteria.

Cardinal analysis: quantitative assessment of portfolio options based on numerical measures of risk and return.

Ordinal analysis: qualitative ranking of alternatives using expert judgements or preference orders.

Stochastic Multicriteria Acceptability Analysis (SMAA): a method that integrates probability distributions and preference uncertainty to derive acceptability indices for decision alternatives.

TOPSIS: Technique for Order Preference by Similarity to Ideal Solution, which ranks options by their distance from an ideal positive and an ideal negative solution.

Particle Swarm Optimization (PSO): a population-based metaheuristic inspired by social behaviour, used to search for optimal weightings in complex decision problems.

References

1. An approach to the integral optimization of investment portfolios. Journal of Open Innovation: Technology, Market, and Complexity (2024).
2. An integrated framework for classification and selection of stocks for portfolio construction: Evidence from NSE, India. Decision Making Applications in Management and Engineering (2023).
3. Stock portfolio selection using a new decision-making approach based on the integration of fuzzy CoCoSo with Heronian mean operator. Decision Making Applications in Management and Engineering (2022).