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Prediction of Financial Distress for Electricity Sectors Using Data Mining

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Book cover Recent Trends in Information and Communication Technology (IRICT 2017)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 5))

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Abstract

This article addresses the financial aspects surrounding the stability of the electricity sector. We apply a variety of data mining techniques to build financial distress warning models based on the financial statement analysis method. The analysis reveal that neural networks with the accuracy of 80% and above in different scenarios were found to be relatively more accurate compared to decision trees and support vector machines. Additionally, in order to assess the ability of financial indicators, we applied feature selection. The financial ratios analyses proved the significance of profitability, liquidity and financial leverage for the default prediction models. Therefore, it is exigent that companies utilize their assets, liquidity and solvency as the core of their management policy regulations. The key contribution of this paper is the formulation of a proper model for financial distress prediction among electricity sector companies in Iran.

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Author information

Authors and Affiliations

  1. Faculty of Business, Multimedia University, 75450, Malacca, Malaysia

    Maryam Mirzaei, Seyed Mehrshad Parvin Hosseini, Goh Guan Gan & Pritish Kumar Sahu

Authors
  1. Maryam Mirzaei
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  2. Seyed Mehrshad Parvin Hosseini
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  3. Goh Guan Gan
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  4. Pritish Kumar Sahu
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Corresponding author

Correspondence to Maryam Mirzaei .

Editor information

Editors and Affiliations

  1. Faculty of Computing Information Systems Department, Universiti Teknologi Malaysia, Johar, Skudai, Malaysia

    Faisal Saeed

  2. Faculty of Computing Information Systems Department, Universiti Teknologi Malaysia, Johar, Skudai, Malaysia

    Nadhmi Gazem

  3. Department of Computer Science and Engineering, SOA University, Bhubaneswar, Odisha, India

    Srikanta Patnaik

  4. Faculty of Computing, ISSI Research Lab, University of Technology Malaysia, Skudai, Malaysia

    Ali Saleh Saed Balaid

  5. Faculty of Computing, University of Technology Malaysia, Skudai, Malaysia

    Fathey Mohammed

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Mirzaei, M., Hosseini, S.M.P., Gan, G.G., Sahu, P.K. (2018). Prediction of Financial Distress for Electricity Sectors Using Data Mining. In: Saeed, F., Gazem, N., Patnaik, S., Saed Balaid, A., Mohammed, F. (eds) Recent Trends in Information and Communication Technology. IRICT 2017. Lecture Notes on Data Engineering and Communications Technologies, vol 5. Springer, Cham. https://doi.org/10.1007/978-3-319-59427-9_1

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  • DOI: https://doi.org/10.1007/978-3-319-59427-9_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59426-2

  • Online ISBN: 978-3-319-59427-9

  • eBook Packages: EngineeringEngineering (R0)

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