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RESEARCH ARTICLE

Uncertain data envelopment analysis approach for handling negative values: An application to stock market

Pejman Peykani1* ,  Seyed Jafar Sadjadi2 ,  Seyed Ehsan Shojaie2 ,  Cristina Tanasescu3 ,  Zahra Abbasi4
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1 Department of Industrial Engineering, Faculty of Engineering, Khatam University, Tehran, Iran
2 School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
3 Faculty of Economic Sciences, Lucian Blaga University of Sibiu, Sibiu, Romania
4 Department of Mathematics, University of Qom, Qom, Iran
IJOCTA, 026170067 https://doi.org/10.36922/IJOCTA026170067
Received: 25 April 2026 | Revised: 7 June 2026 | Accepted: 15 June 2026 | Published online: 29 June 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
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Abstract

Classical Data Envelopment Analysis (DEA) models are traditionally built on the assumptions of certain and non‑negative data. In contrast, real‑world applications frequently involve negative values and epistemically uncertain information. Uncertainty theory offers a rigorous mathematical alternative to probability and fuzzy set theory for handling such indeterminacy. This study extends the Range Directional Measure (RDM) model by integrating uncertainty theory to simultaneously address negative data and uncertain environments. The proposed uncertain RDM model is operationalized and validated using an empirical dataset from the Tehran stock market, where traditional efficiency metrics often fail due to volatile financial ratios and negative returns. The findings demonstrate that the proposed framework yields more robust efficiency scores and provides actionable insights for stock evaluation under uncertainty. This integration advances the DEA literature by bridging the gap between performance measurement and uncertainty theory in negative‑data contexts.

Graphical abstract
Keywords
Uncertain data envelopment analysis
Negative data
Uncertainty theory
Range directional measure
Stock evaluation
Funding
None
Conflict of interest
None
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An International Journal of Optimization and Control: Theories & Applications, Electronic ISSN: 2146-5703 Print ISSN: 2146-0957, Published by AccScience Publishing
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