A New Clusterless DEA Cross-Efficiency Evaluation in the Presence of Negative Data and its Application in Portfolio Selection

Kumari, Reenu

doi:10.22054/jmmf.2026.89074.1229

Articles in Press

Document Type : Original Article

Author

Reenu Kumari

Maharaja Surajmal Institute of Technology, Janakpuri, Delhi, India

10.22054/jmmf.2026.89074.1229

Abstract

This study examines the impact of clustered Decision-Making Units (DMUs) in DEA cross-efficiency evaluation, taking into account variables with both positive and negative values. The Range Directional Measure (RDM) model is often used when DMUs have both positive and negative data. However, its application frequently results in clustered DMUs, where multiple units obtain identical RDM cross-efficiency scores, thereby reducing discriminatory power and limiting the reliability of rankings. To overcome this drawback and enhance ranking reliability, we characterize clustered DMUs as scenarios in which identical scores lead to groups of DMUs being termed as ‘clustered’. The necessity of this research lies in improving the robustness of efficiency analysis, especially in situations where both positive and negative data are involved. We then present an algorithm to identify potential clusters and propose a novel clusterless cross-efficiency evaluation method, which restores discrimination and provides more credible performance analysis in decision-making contexts. To demonstrate the practical relevance and advantages of the proposed method, a case study on stock selection in Iran’s stock market portfolio is provided.

Keywords

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Journal of Mathematics and Modeling in Finance

A New Clusterless DEA Cross-Efficiency Evaluation in the Presence of Negative Data and its Application in Portfolio Selection

References

References

Articles in Press, Accepted Manuscript
Available Online from 15 February 2026

A New Clusterless DEA Cross-Efficiency Evaluation in the Presence of Negative Data and its Application in Portfolio Selection

References

References

Articles in Press, Accepted Manuscript Available Online from 15 February 2026

Articles in Press, Accepted Manuscript
Available Online from 15 February 2026