Short-Term Trading Performance of Spot Freight Rates and Derivatives in the Tanker Shipping Market: Do Neural Networks Provide Suitable Results?

In this paper we investigate the forecasting and trading performance of linear and non-linear methods, in order to generate short-term forecasts in the dirty tanker shipping market. We attempt to uncover the benefits of using several time series models and the potential of neural networks. Maritime forecasting studies using neural networks are rare and only focus on spot rates. We build on this kind of investigation, but we extend our study on freight rates derivatives or Forward Freight Agreements (FFA) in a simple trading simulation. Our conclusion is, that non-linear methods like neural networks are suitable for short-term forecasting and trading freight rates, as their results match or improve on those of other models. Nevertheless, we think that further research with freight rates and corresponding derivatives is developable for decision and trading applications with enhanced forecasting models.