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Integrating Linear Regression and Multi-Criteria Decision Making for Assessing Financial Statement Risks in Manufacturing Firms

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  • Duaa Abdullah
  • Marwa Abdullah

Abstract

Evaluating the financial performance of manufacturing firms requires consideration of both the time value of money and the relative importance of multiple decision criteria. Conventional approaches relying solely on deterministic discounting often fail to account for interactions among economic, operational, and managerial factors. This study proposes an integrated framework that combines time-discounted economic analysis with linear regression to evaluate control system efficiency. A theoretical discounting model is first developed to convert costs and benefits occurring at different times into present-value terms using compound interest functions. The model accommodates one-time expenditures, time-proportional costs, and complex cost structures arising during system development and commissioning. To empirically assess how discounted economic performance is influenced by multiple criteria, linear regression serves as the approximation method.

Suggested Citation

  • Duaa Abdullah & Marwa Abdullah, 2026. "Integrating Linear Regression and Multi-Criteria Decision Making for Assessing Financial Statement Risks in Manufacturing Firms," Papers 2602.04563, arXiv.org.
    • Handle: RePEc:arx:papers:2602.04563
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    References listed on IDEAS

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    1. Niklas Kühl & Max Schemmer & Marc Goutier & Gerhard Satzger, 2022. "Artificial intelligence and machine learning," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 2235-2244, December.
    2. Marwa Abdullah & Revzon Oksana Anatolyevna & Duaa Abdullah, 2025. "Assessing Financial Statement Risks among $\mathrm{MCDM}$ Techniques," Papers 2512.04035, arXiv.org, revised Dec 2025.
    3. Benjamin M. Abdel-Karim & Nicolas Pfeuffer & Oliver Hinz, 2021. "Machine learning in information systems - a bibliographic review and open research issues," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(3), pages 643-670, September.
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    5. repec:dar:wpaper:135656 is not listed on IDEAS
    6. Kühl, Niklas & Schemmer, Max & Goutier, Marc & Satzger, Gerhard, 2022. "Artificial intelligence and machine learning," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 154356, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    7. Rainer Alt, 2018. "Electronic Markets and current general research," Electronic Markets, Springer;IIM University of St. Gallen, vol. 28(2), pages 123-128, May.
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