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Tax planning and financial performance of conglomerate firms in Nigeria

Author

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  • Henry Kehinde Fasua

    (Department of Accounting, Achievers University, Owo, Ondo State, Nigeria)

  • Toluwa Ohidoa

    (Department of Accounting, Faculty of Management Sciences, University of Benin, Benin, Edo, Nigeria)

Abstract

This study explores the influence of tax planning on the financial performance of conglomerate firms in Nigeria between 2014 and 2023. The study focuses on the roles of effective tax rate (ETR), tunneling incentive (TI), and bonus mechanism (BM). A fixed effects panel regression model was employed, validated through the Hausman test (p = 0.0036). Findings indicate that ETR and log of gross profit (LOGGP) significantly and positively affect firm performance, showing that profitable firms often bear higher tax burdens but also display stronger operational efficiency. Conversely, TI and BM recorded negative and positive coefficients, respectively, though both were statistically insignificant. The model explained 46.57% of the variation in performance, and all diagnostic checks confirmed its robustness. These findings highlight the importance of tax management, corporate governance, and incentive systems in shaping the financial outcomes of conglomerates in Nigeria.

Suggested Citation

  • Henry Kehinde Fasua & Toluwa Ohidoa, 2026. "Tax planning and financial performance of conglomerate firms in Nigeria," Oradea Journal of Business and Economics, University of Oradea, Faculty of Economics, vol. 11(1), pages 1-6, March.
    • Handle: RePEc:ora:jrojbe:v:11:y:2026:i:1:p:1-6
    DOI: http://doi.org/10.47535/1991ojbe222
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    Keywords

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    JEL classification:

    • G34 - Financial Economics - - Corporate Finance and Governance - - - Mergers; Acquisitions; Restructuring; Corporate Governance
    • L25 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Firm Performance
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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