The Value of Foresight in the Drybulk Freight Market

Abstract

We analyze the value of foresight in the drybulk freight market when repositioning a vessel through space and time. In order to do that, we apply an optimization model on a network with dynamic regional freight rate differences and stochastic travel times. We evaluate the value of the geographical switching option for three cases: the upper bound based on having perfect foresight, the lower bound based on a "coin flip", and the case of perfect foresight but only for a limited horizon. By combining a neural network with optimization, we can assess the impact of varying foresight horizon on economic performance. In a simple but realistic two-region case, we show empirically that the upper bound for large vessels can be as high as 25% cumulative outperformance, and that a significant portion of this theoretical value can be captured with limited foresight of several weeks. Our research sheds light on the important issue of spatial efficiency in global ocean freight markets and provides a benchmark for the value of investing in predictive analysis.

Keywords: Dry bulk market, dynamic programming, neural network, foresight

JEL Classification: C44, C60, C61