Baltic dry index forecast using financial market data: Machine learning methods and SHAP explanations Machine learning and SHAP for baltic dry index forecasting

Abstract

The Baltic Dry Index (BDI) is a critical benchmark for assessing freight rates and chartering activity in the global shipping market. This study forecasts the BDI using diverse financial data, including commodities, currencies, stock markets, and volatility indices. Unlike previous research, our approach integrates financial indicators specific to major marine trading regions-the U.S., EU, and Hong Kong. We employ advanced machine learning methods, such as Extremely Randomized Trees, Categorical Boosting (CatBoost), and Random Forest, to achieve superior forecasting accuracy. Additionally, we utilize the Shapley Additive Explanations (SHAP) framework to analyze the contributions of financial features to BDI predictions. Key findings reveal that the S&P 500 index is the most influential factor, followed by significant contributions from iron ore and coal commodity indices and the dollar index, underscoring the interplay between the U.S. economy and the BDI. By integrating SHAP explanations, this study not only predicts market trends but also uncovers the economic drivers shaping the BDI. Practically, it supports the stability of the global shipping industry by enabling more informed decision-making for stakeholders. Academically, it introduces overlooked economic factors in BDI prediction, offering valuable insights and directions for future research.