Forecasting

The Forecasting theme brings together researchers and practitioners to advance the science and application of forecasting across sectors and decision contexts. Building on expertise at Durham University, and connected to the Hazard, Risk and Resilience community, this theme focuses on developing robust forecasting methods that support decision-making under uncertainty.

Our work spans statistical and machine learning approaches, scenario modelling, risk assessment, and early warning systems. We are particularly interested in how forecasting can inform policy, organisational strategy, and operational planning in complex and rapidly changing environments.

By combining methodological innovation with real-world application, the Forecasting theme aims to improve predictive accuracy, enhance transparency in modelling, and strengthen the practical impact of forecasts in areas such as climate risk, infrastructure resilience, health, energy, and supply chains.

Members

Kostas Nikolopoulos, https://www.durham.ac.uk/business/our-people/kostas-nikolopoulos/ 

Associated research (selection)

Litsioua, K., & Nikolopoulos, K. (in press). Social Collateral and consumer payment media during the economic crisis in Europe. Journal of Quantitative Finance and Economics.

Quariguasi Frota Net, J., Dutordoir, M., Bozos, K., & Nikolopoulos, K. (2025). Are acquirer stock price reactions to M&A announcements in any way predictable? A Machine-Learning analysis. Journal of the Operational Research Society. Advance online publication. https://doi.org/10.1080/01605682.2025.2562956 

Karamatzanis, G., Tilba, A., & Nikolopoulos, K. (2025). Corporate Governance Reporting, Disclosures, Monitoring, and Decision‐Making: The Role of Big Data Analytics and Technological Tools. Corporate Governance: An International Review. Advance online publication. https://doi.org/10.1111/corg.12646 

Aljuneidi, T., Punia, S., Jebali, A., & Nikolopoulos, K. (2024). Forecasting and Planning for a critical infrastructure sector during a pandemic: empirical evidence from a food supply chain. European Journal of Operational Research, 317(3), 936-952. https://doi.org/10.1016/j.ejor.2024.04.009 

Schaefers, A., Bougioukos, V., Karamatzanis, G., & Nikolopoulos, K. (2024). Prediction-led prescription: optimal Decision-Making in times of Turbulence and business performance improvement. Journal of Business Research, 182, Article 114805. https://doi.org/10.1016/j.jbusres.2024.114805