Baltic Dry Index Estimation With NARX Neural Network Model

Abstract

BDI is a global trade indicator followed by those interested in maritime trade. But it has volatility, seasonality, and uncertain cyclicality. For this reason, in this study, the BDI has been estimated to provide preliminary information to those interested in maritime trade. NARX Neural Network which performs successfully in complex and nonlinear real-life problems is used. In addition, the NARX neural network model has not been found in a previous study used for BDI estimation. Eleven independent variables are used in this study, what increases the predictive power. Independent variables are Bloomberg Commodities Index (BCOM), Twitter-Based Economic Uncertainty Index (TEU), Twitter-Based Market Uncertainty Index (TMU), S&P 500 Index, MSCI World Index, €/$ Parity, VIX (CBOE), US 10-Year Bond Yield (%), Brent Oil (USD/Barrel), Economic Uncertainty Index and World Trade Volume (USD Billion). The Twitter-Based Economic Uncertainty Index (TEU) and Twitter-Based Market Uncertainty Index (TMU), which were not used before in BDI estimation studies, were included in the analysis and contributed to the literature. The data set contains daily data for the period 9.07.2012–31.08.2020. 11-day estimate values covering 1.09.2020–15.09.2020 are calculated. MAPE, MAE and RMSE performance criteria were calculated for the estimation values. Value of MAPE (2.96%), value of MAE (36.6%) and value of RMSE (46.68) were obtained. As a result, the estimate values were compared with the actual values.