Forecasting the Baltic Dry Index by using an artificial neural network approach

Authors : Bekir ŞAHİN, Samet GÜRGEN, Bedir ÜNVER, İsmail ALTIN

Pages : 1673-1684

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Publication Date : 0000-00-00

Article Type : Research Paper

Abstract :The Baltic Dry Index insert ignore into journalissuearticles values(BDI); is a robust indicator in the shipping sector in terms of global economic activities, future world trade, transport capacity, freight rates, ship demand, ship orders, etc. It is hard to forecast the BDI because of its high volatility and complexity. This paper proposes an artificial neural network insert ignore into journalissuearticles values(ANN); approach for BDI forecasting. Data from January 2010 to December 2016 are used to forecast the BDI. Three different ANN models are developed: insert ignore into journalissuearticles values(i); the past weekly observation of the BDI, insert ignore into journalissuearticles values(ii); the past two weekly observations of the BDI, and insert ignore into journalissuearticles values(iii); the past weekly observation of the BDI with crude oil price. While the performance parameters of these three models are close to each other, the most consistent model is found to be the second one. Results show that the ANN approach is a significant method for modeling and forecasting the BDI and proving its applicability.
Keywords : Baltic Dry Index, forecasting, artificial neural network, crude oil, shipping industry