Financıal risk and return analysıs in mega projects: A panel data approach with econometric modeling

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Type of the article: Research Article

Abstract
Mega project, defined as infrastructure investments exceeding USD one billion, play a central role in national development but often face significant financial risks. This study explores the dynamic relationship between macroeconomic risk factors and return on investment (ROI) in mega projects using a panel data econometric approach. A balanced dataset from 2000 to 2024 was constructed, covering five economies with significant mega infrastructure investment: Turkey, China, the United States, India, and Germany. The analysis incorporates both internal project risks (cost overruns, completion delays) and external macroeconomic shocks (exchange rate volatility, inflation, interest rates). Representative infrastructure projects in transport, energy, and urban sectors were selected based on official national data and international financial databases. Pedroni cointegration tests confirmed the existence of long-term equilibrium relationships, justifying the application of a panel Generalized Method of Moments (GMM) model to address endogeneity, heterogeneity, and dynamic effects. The results indicate that a 1% increase in budget allocation leads to a 0.21% rise in ROI (p < 0.001), while cost overruns are associated with a 0.84% ROI increase per 1% overspend (p = 0.003), suggesting potential value in strategic overspending. In contrast, a 1% increase in exchange rate volatility reduces ROI by 0.69% (p < 0.001). No significant effects were found for inflation or completion delays. These findings reflect aggregated financial behavior rather than individual project cases, offering generalizable insights into mega project finance. The study contributes to the literature by constructing a risk-adjusted, multi-country econometric model and offers policy guidance for enhancing financial resilience in large-scale infrastructure investments.

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    • Table 1. Descriptive statistics
    • Table 2. Correlation analysis results
    • Table 3. Unit root test results
    • Table 4. Pedroni’s cointegration test results
    • Table 5. Results of the panel GMM regression estimating ROI
    • Table 6. Hypothesis results summary
    • Table 7. Panel Granger causality test results
    • Conceptualization
      Mustafa Özyeşil, Havane Tembelo
    • Data curation
      Mustafa Özyeşil, Havane Tembelo
    • Formal Analysis
      Mustafa Özyeşil, Havane Tembelo
    • Methodology
      Mustafa Özyeşil
    • Project administration
      Mustafa Özyeşil
    • Software
      Mustafa Özyeşil
    • Supervision
      Mustafa Özyeşil, Havane Tembelo
    • Validation
      Mustafa Özyeşil, Havane Tembelo
    • Visualization
      Mustafa Özyeşil
    • Writing – original draft
      Mustafa Özyeşil
    • Funding acquisition
      Havane Tembelo
    • Investigation
      Havane Tembelo
    • Resources
      Havane Tembelo
    • Writing – review & editing
      Havane Tembelo