Dynamic Pricing and Market Valuation: Evidence from the Egyptian Telecommunications Sector
DOI:
https://doi.org/10.56830/WRBA03202602Keywords:
Dynamic Pricing, Revenues, Profitability, Market Value Sustainability, Short and Long Run Dynamic RelationshipsAbstract
This research aims to analyze the impact of dynamic pricing on firms’ market value within the telecommunications sector, with a particular focus on Telecom Egypt as a representative case in an emerging market. Specifically, it examines whether the effects of dynamic pricing are reflected in market valuation through short-run transmission channels or through long-run dynamic relationships that evolve over time.
The analysis relies on quarterly time-series data covering the period from 2020Q1 to 2025Q3 and employs a dynamic econometric framework based on Autoregressive Distributed Lag (ARDL) models, the bounds testing approach for cointegration, and the Error Correction Model (ECM). This framework allows for a clear distinction between short-run and long-run effects while appropriately addressing non-stationarity in the underlying time series. In addition, the specification explicitly controls for structural shocks—most notably the COVID-19 pandemic—through the inclusion of a dedicated dummy variable, thereby enhancing the robustness of the empirical inference.
The empirical results provide strong evidence of a stable long-run equilibrium relationship between dynamic pricing and firm market value. Revenues emerge as the primary channel through which the effects of dynamic pricing are transmitted to market valuation in both the short and long run, indicating that financial markets primarily evaluate dynamic pricing through its capacity to generate and sustain revenues over time. In contrast, profitability exhibits a weaker and less stable influence, suggesting that short-term profit fluctuations are not consistently incorporated into market valuation, particularly in capital-intensive sectors such as telecommunications.
Moreover, the statistically significant and negative error correction term indicates a relatively rapid adjustment of market value toward its long-run equilibrium following short-term shocks. This finding implies that dynamic pricing is associated with a higher degree of market value stability over the medium and long term, rather than contributing to excessive valuation volatility. The inclusion of the 2020 dummy variable confirms the presence of a substantial negative structural shock without undermining the core relationship between dynamic pricing and market value.
References
Anjos, M. F., Lodi, A., & Tardella, F. (2025). Dynamic pricing for sustainability and profitability in energy markets. Energy Economics, 123, 106789.
Bambauer-Sachse, S., & Young, S. (2024). Consumer confusion and perceived price unfairness under dynamic pricing. Journal of Business Research, 162, 113845.
Bu, X., Wang, Y., & Li, Z. (2025). Reinforcement learning-based dynamic pricing in digital markets. Decision Support Systems, 178, 114041.
Carnehl, M., Steinhardt, C., & Gierl, H. (2023). Dynamic pricing and firm reputation: Evidence from online rating systems. Journal of Retailing and Consumer Services, 72, 103287. DOI: https://doi.org/10.1016/j.jretconser.2023.103287
Cohen, M. C., Lobel, R., & Perakis, G. (2025). Fairness constraints in dynamic pricing. Management Science, 71(2), 621–639.
Das, S., Mukherjee, A., & Banerjee, S. (2024). Machine learning approaches to real-time dynamic pricing. Expert Systems with Applications, 237, 121471.
Farias, V. F., Moallemi, C. C., & Van Roy, B. (2024). Dynamic pricing in transportation systems. Transportation Science, 58(1), 1–18.
Gangwar, M., & Bhargava, H. (2023). Dynamic pricing: Definition, implications for managers, and future research directions.
Hasanah, U., & Rino, A. (2025). Dynamic pricing strategies: A PRISMA-based systematic review. Journal of Pricing Strategy & Practice, 19(1), 45–67.
Immadisetty, V. (2025). Real-time data analytics for dynamic pricing optimization. International Journal of Information Management, 74, 102692.
Joel, O., & Oguanobi, C. (2024). Predictive analytics and strategic pricing decisions. Journal of Business Analytics, 7(2), 134–149.
Kalil, J., & Lee, J. (2023). Machine learning models for adaptive pricing. Applied Economics Letters, 30(15), 1965–1969.
Kambau, A., & Prawira, I. (2023). Competitive advantage and firm value: Evidence from emerging markets. Journal of Asian Business and Economic Studies, 30(3).
Kopalle, P. K., Gangwar, M., & Bhargava, H. (2023). Dynamic pricing over periods and usage-based pricing.
Li, W. (2023). Strategic management accounting in a network economy. Springer. DOI: https://doi.org/10.1007/978-981-99-5253-3
Milman, A., & Tasci, A. D. (2022). Dynamic pricing and consumer trust. Tourism Management, 91, 104512.
Moghadasnian, S., & Rajolb, M. (2024). Dynamic pricing applications in infrastructure systems. Infrastructure Asset Management, 11(1), 55–69. DOI: https://doi.org/10.1680/jinam.2024.11.1.55
Neubert, M. (2022). Dynamic pricing research: A systematic literature review. Journal of Business Research, 144, 865–880.
Prakasha, N. (2023). Optimizing online retail profits: A comparative analysis of data-driven dynamic pricing models. International Education & Research Journal.
Rane, S. B., Thakker, S. V., & Kant, R. (2024). AI-driven analytics for operational and pricing decisions. Technological Forecasting and Social Change, 198, 123015.
Sarkar, M., Ayon, E. H., Mia, M. T., Ray, R. K., Chowdhury, M. S., Ghosh, B. P., et al. (2023). Optimizing e-commerce profits: A comprehensive machine learning framework for dynamic DOI: https://doi.org/10.32996/jcsts.2023.5.4.19
pricing and predicting online purchases. Journal of Computer Science and Technology Studies, 5(4), 186–193.
Suresh, P., Kumar, R., & Jain, A. (2025). Deep learning-based dynamic pricing in competitive markets. IEEE Transactions on Engineering Management, 72(1), 215–228.
Telecom Egypt. (2025). Corporate presentation: Financial and operational overview. . Telecom Egypt.
Wang, X., Li, H., & Zhou, Y. (2024). Neural network models for dynamic price forecasting. Expert Systems with Applications, 232, 120617.






