Optimal stopping time strategy for paying tax

Authors

  • Soufiene Author
  • Noureddine JILANI BEN NAOUARA Author
  • Wing-Keung Wong Department of Finance, Quantum AI Research Center, Fintech & Blockchain Research Center, and Big Data Research Center, Asia University, Taiwan , Department of Medical Research, China Medical University Hospital, Taiwan , Business, Economic and Public Policy Research Centre, Hong Kong Shue Yan University Corresponding Author

DOI:

https://doi.org/10.47654/v29y2025i1p55-82

Keywords:

Optimal stopping problem, Markov processes, tax payment, reinvestment, penalty

Abstract

Purpose: In this study, we examine the strategic timing for paying corporate taxes under varying financial market conditions.
Design/methodology/approach: We develop a model by using optimal stopping theory and considering three tax regimes: (i) no tax, the firm is never required to pay taxes; (ii) paid tax on a monthly basis, taxes paid based on revenues periodically; and (iii) deferred tax with penalties, delaying taxes but incurring financial penalties.
Findings: Our results indicate that the optimal timing of tax payments is highly sensitive to market parameters such as drift, volatility, and the structure of tax penalties. The evidence further suggests that, under certain risk conditions, tax deferral can serve as a legitimate and value-enhancing strategy, allowing firms to retain more earnings and allocate resources more efficiently.
Originality: Our main contribution is to identify how timing strategies are shaped by drift, volatility, and tax penalties. The model is developed theoretically and supported by a Monte Carlo simulation. We also address the legal and policy implications of tax deferral, namely that under certain risk conditions, tax deferral increases firm value. Overall, our conclusions are based on mathematically rigorous modeling and simulations.

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Published

2025-09-28

How to Cite

ASSIDI, S., JILANI BEN NAOUARA, N., & Wong, W.-K. (2025). Optimal stopping time strategy for paying tax. Advances in Decision Sciences, 29(1), 55-82. https://doi.org/10.47654/v29y2025i1p55-82