Experimental XGBoost Method for Predicting the Ghana Cedi Exchange Rate Against Major Developed Currencies

Exchange rate prediction is a crucial aspect of international finance, impacting decisions by governments, investors, and businesses. Accurate prediction supports the development of sound monetary policies, effective risk management, and strategic international trade planning. According to literature, traditional econometric models like ARIMAX and VAR often struggle to capture the complex, non-linear dynamics of foreign exchange markets. In contrast, machine learning methods, particularly Extreme Gradient Boosting (XGBoost), have shown superior performance due to their ability to handle large datasets, model non-linear relationships, and resist overfitting. This study evaluates the efficacy of the Extreme Gradient Boosting (XGBoost) model by predicting the GHc/USD, GHc/GBP and GHc/EUR exchange rates. Four different types of XGBoost models were employed on the financial data to determine the best performed model. The four different XGBoost models include, the XGBoost all feature, the XGBoost difference feature, the XGBoost ratio feature and the XGBoost lagged feature. The data sourced from Bank of Ghana and World Bank websites spans from January 2015 to March 2025. Findings from the study reveals that the XGBoost lagged feature and XGBoost all feature models outperformed the other two models, with an average <I>R</I>²of 99%, RMSE of 0.05, and MAE of 0.01. Gold price was the biggest contributor to the GHc/USD exchange rate with the feature important score of 80% followed by monthly interest rate 60%, Government debt 25%, M2 20%, price of oil 20%, BCI 15%, and CCI 10%. This result provides valuable insight for financial analyst and policymakers seeking to forecast/predict exchange rates and develop policies aimed at addressing exchange rate menace in Ghana.