Forecasting Tanker Market Using Artificial Neural Networks

Abstract

Investing in the tanker market, especially in the VLCC sector constitutes a risky undertaking due to the volatility of tanker freight rates. This paper attempts to uncover the benefits of using Artificial Neural Networks (ANNs) in forecasting VLCC spot freight rates. This is achieved by analysing the period from October 1979 to December 2002, in order to detect possible causes of fluctuations, thus determine the independent variables of the analysis, and then use them to construct reliable ANNs. The aim is to reduce error and, most important, allow the model to maintain a stable error variance during high volatility periods. Among the findings are: ANNs can, with the appropriate architecture and training, constitute valuable decision-making tools especially when the tanker market is volatile; the use of variables in differential form enhances the ANN performance in high volatility periods while variables in normal form demonstrated better performance in median periods; ANN demonstrated mean errors comparable to the naive model for 1-month forecasts but significantly outperformed it in the 3-, 6-, 9- and 12-month cases; finally, the use of informative variables such as the arbitrage between types of crude oil as well as Capesize rates can improve ANN performance.