Main Article Content
Abstract
The global advertising industry is undergoing structural transformation amid rapid digital disruption and economic uncertainty. This study examines the impact of technological disruption including the adoption of artificial intelligence (AI), big data, and automated advertising systems and digital economy indicators on the stock volatility of three multinational advertising companies: WPP plc (UK), Omnicom Group Inc. (US), and Dentsu Group Inc. (Japan). Using monthly data from 2008 up to 2024, the research employs a hybrid econometric approach integrating the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model to capture short-term volatility dynamics and the Vector Error Correction Model (VECM) to assess long-term equilibrium relationships among variables. Analyzed variables include technology indices (NASDAQ-100, FTSE Techmark, Nikkei-225), inflation (CPI), digital advertising expenditure, and revenue. The integrated GARCH-VECM model is evaluated against individual GARCH and VECM models using performance metrics such as MAE, RMSE, MAPE, confidence intervals, and cross-validation tests. Results show that technological disruption significantly increases stock volatility across all firms, while inflation effects vary depending on the country context. Sector-specific indicators, including digital advertising spending and revenue, exhibit limited short-term influence. Long-run cointegration is confirmed, particularly for WPP and Omnicom, indicating structural sensitivity to global digital transformation. The integrated GARCH-VECM model demonstrates superior predictive accuracy, stability, and robustness compared to single models. This study contributes novel empirical evidence and practical insights for effective risk management in the highly digitized and volatile advertising sector, benefiting policymakers, investors, and corporate strategists.
Keywords
Article Details

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
References
- Andersen, T. G., Davis, R. A., Kreiss, J.-P., & Mikosch, T. (Eds.). (2009). Handbook of Financial Time Series. Springer-Verlag Berlin Heidelberg.
- Anusha, G. (2016). Effectiveness of Online Advertising. International Journal of Research – Granthaalayah, 4(3), 14–21. https://granthaalayahpublication.org/journals/index.php/granthaalayah/article/view/IJRG16_SE03_03
- Arifin, A. H. (2023). Firm value volatility in view of profit performance and practices good corporate governance. SEIKO: Journal of Management & Business, 6(1), 296–310. https://journal.stiemahardhika.ac.id/index.php/seiko/article/view/935
- Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327. https://doi.org/10.1016/0304-4076(86)90063-1
- Bonilla, C. A., & Sepúlveda, J. (2011). Stock returns in emerging markets and the use of GARCH models. Applied Economics Letters, 18(14), 1321–1325. https://doi.org/10.1080/13504851.2010.537615
- Box, G. E. P., Jenkins, G. M., & Reinsel, G. C. (2016). Time Series Analysis: Forecasting and Control (5th ed.). Wiley.
- Chen, H., De, P., Hu, Y. J., & Hwang, B.-H. (2014). Wisdom of crowds: The value of stock opinions transmitted through social media. The Review of Financial Studies, 27(5), 1367–1403. https://doi.org/10.1093/rfs/hhu001
- Christensen, C. M., Ojomo, E., & Dillon, K. (2019). The Prosperity Paradox: How Innovation Can Lift Nations Out of Poverty. Harper Business.
- Downes, L., & Nunes, P. (2014). Big Bang Disruption: Strategy in the Age of Devastating Innovation. Harvard Business Review Press.
- Engle, R. F., & Granger, C. W. J. (1987). Co-integration and error correction: Representation, estimation, and testing. Econometrica, 55(2), 251–276.
- Gordon, B. R., Zettelmeyer, F., Bhargava, N., & Chapsky, D. (2019). A comparison of approaches to advertising measurement: Evidence from big field experiments at Facebook. Marketing Science, 38(2), 193–225. https://doi.org/10.1287/mksc.2018.1135
- Gounopoulos, D., Molyneux, P., Staikouras, S. K., Wilson, J. O. S., & Zhao, G. (2012). Exchange rate risk and the equity performance of financial intermediaries. International Review of Financial Analysis, 24, 29–39. https://doi.org/10.1016/j.irfa.2012.04.001
- Hossain, M. R. (2024). The integration of AI in small enterprises and its impact on productivity and innovation. International Journal for Multidisciplinary Research, 6(5).
- Mankiw, N. G. (2021). Principles of Economics (9th ed.). Cengage Learning.
- Mann, G., Karanasios, S., & Breidbach, C. F. (2022). Orchestrating the digital transformation of a business ecosystem. The Journal of Strategic Information Systems, 31(3), 101733. https://doi.org/10.1016/j.jsis.2022.101733
- McAleer, M. (2005). Automated inference and learning in modeling volatility. Econometric Theory, 21(1), 232–236.
- Mubarok, F., & Faturrahman, M. F. (2021). Modeling of Jakarta Islamic Index stock volatility and forecasting using realized GARCH model. Al-Tijary Journal, 7(2), 101–110. https://e-journal.iainsurakarta.ac.id/index.php/tijary/article/view/4004
- Mullins, G. E. (2000). Stock Market Volatility: Measures and Results.
- Rogers, E. M. (2003). Diffusion of Innovations (5th ed.). Free Press.
- Rosário, D., & Dias, Á. (2023). How has data-driven marketing evolved: Challenges and opportunities with emerging technologies. International Journal of Information Management Data Insights, 100203. https://doi.org/10.1016/j.jjimei.2023.100203
- Wang, Z., & Kim, H.-G. (2017). Can social media marketing improve customer relationship capabilities and firm performance? Dynamic capability perspective. Journal of Interactive Marketing, 39, 15–26. https://doi.org/10.1016/j.intmar.2017.02.004
- Westerman, G., & Webster, M. (2025). Generate value from GenAI with ‘small t’ transformations.
- Wu, K., Fu, Y., & Kong, D. (2022). Does the digital transformation of enterprises affect stock price crash risk? Finance Research Letters, 48, 102888.
References
Andersen, T. G., Davis, R. A., Kreiss, J.-P., & Mikosch, T. (Eds.). (2009). Handbook of Financial Time Series. Springer-Verlag Berlin Heidelberg.
Anusha, G. (2016). Effectiveness of Online Advertising. International Journal of Research – Granthaalayah, 4(3), 14–21. https://granthaalayahpublication.org/journals/index.php/granthaalayah/article/view/IJRG16_SE03_03
Arifin, A. H. (2023). Firm value volatility in view of profit performance and practices good corporate governance. SEIKO: Journal of Management & Business, 6(1), 296–310. https://journal.stiemahardhika.ac.id/index.php/seiko/article/view/935
Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327. https://doi.org/10.1016/0304-4076(86)90063-1
Bonilla, C. A., & Sepúlveda, J. (2011). Stock returns in emerging markets and the use of GARCH models. Applied Economics Letters, 18(14), 1321–1325. https://doi.org/10.1080/13504851.2010.537615
Box, G. E. P., Jenkins, G. M., & Reinsel, G. C. (2016). Time Series Analysis: Forecasting and Control (5th ed.). Wiley.
Chen, H., De, P., Hu, Y. J., & Hwang, B.-H. (2014). Wisdom of crowds: The value of stock opinions transmitted through social media. The Review of Financial Studies, 27(5), 1367–1403. https://doi.org/10.1093/rfs/hhu001
Christensen, C. M., Ojomo, E., & Dillon, K. (2019). The Prosperity Paradox: How Innovation Can Lift Nations Out of Poverty. Harper Business.
Downes, L., & Nunes, P. (2014). Big Bang Disruption: Strategy in the Age of Devastating Innovation. Harvard Business Review Press.
Engle, R. F., & Granger, C. W. J. (1987). Co-integration and error correction: Representation, estimation, and testing. Econometrica, 55(2), 251–276.
Gordon, B. R., Zettelmeyer, F., Bhargava, N., & Chapsky, D. (2019). A comparison of approaches to advertising measurement: Evidence from big field experiments at Facebook. Marketing Science, 38(2), 193–225. https://doi.org/10.1287/mksc.2018.1135
Gounopoulos, D., Molyneux, P., Staikouras, S. K., Wilson, J. O. S., & Zhao, G. (2012). Exchange rate risk and the equity performance of financial intermediaries. International Review of Financial Analysis, 24, 29–39. https://doi.org/10.1016/j.irfa.2012.04.001
Hossain, M. R. (2024). The integration of AI in small enterprises and its impact on productivity and innovation. International Journal for Multidisciplinary Research, 6(5).
Mankiw, N. G. (2021). Principles of Economics (9th ed.). Cengage Learning.
Mann, G., Karanasios, S., & Breidbach, C. F. (2022). Orchestrating the digital transformation of a business ecosystem. The Journal of Strategic Information Systems, 31(3), 101733. https://doi.org/10.1016/j.jsis.2022.101733
McAleer, M. (2005). Automated inference and learning in modeling volatility. Econometric Theory, 21(1), 232–236.
Mubarok, F., & Faturrahman, M. F. (2021). Modeling of Jakarta Islamic Index stock volatility and forecasting using realized GARCH model. Al-Tijary Journal, 7(2), 101–110. https://e-journal.iainsurakarta.ac.id/index.php/tijary/article/view/4004
Mullins, G. E. (2000). Stock Market Volatility: Measures and Results.
Rogers, E. M. (2003). Diffusion of Innovations (5th ed.). Free Press.
Rosário, D., & Dias, Á. (2023). How has data-driven marketing evolved: Challenges and opportunities with emerging technologies. International Journal of Information Management Data Insights, 100203. https://doi.org/10.1016/j.jjimei.2023.100203
Wang, Z., & Kim, H.-G. (2017). Can social media marketing improve customer relationship capabilities and firm performance? Dynamic capability perspective. Journal of Interactive Marketing, 39, 15–26. https://doi.org/10.1016/j.intmar.2017.02.004
Westerman, G., & Webster, M. (2025). Generate value from GenAI with ‘small t’ transformations.
Wu, K., Fu, Y., & Kong, D. (2022). Does the digital transformation of enterprises affect stock price crash risk? Finance Research Letters, 48, 102888.
