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Area of Science:

  • Economics
  • Financial Markets
  • Maritime Logistics

Background:

  • The Baltic Dry Index (BDI) is a key indicator of global shipping freight rates and chartering activity.
  • Accurate BDI forecasting is crucial for stakeholders in the maritime and financial sectors.
  • Previous research has often overlooked the impact of specific regional financial indicators on the BDI.

Purpose of the Study:

  • To forecast the Baltic Dry Index (BDI) with enhanced accuracy.
  • To identify and analyze the influence of diverse financial indicators, including regional ones, on BDI movements.
  • To provide a deeper understanding of the economic drivers behind BDI fluctuations.

Main Methods:

  • Utilized advanced machine learning algorithms: Extremely Randomized Trees, Categorical Boosting (CatBoost), and Random Forest.
  • Integrated a comprehensive dataset including commodities, currencies, stock markets, and volatility indices.
  • Employed the Shapley Additive Explanations (SHAP) framework for feature importance analysis.

Main Results:

  • The S&P 500 index was identified as the most significant predictor of the BDI.
  • Commodity indices (iron ore, coal) and the dollar index also demonstrated substantial influence.
  • The study successfully integrated regional financial indicators from the U.S., EU, and Hong Kong.

Conclusions:

  • The U.S. economy, reflected in the S&P 500 and dollar index, plays a pivotal role in BDI trends.
  • Machine learning models, augmented by SHAP analysis, offer superior BDI forecasting capabilities.
  • This research provides actionable insights for improving decision-making and stability within the global shipping industry.