The purpose of this study has been to predict Forward Freight Agreement (FFA) prices using
machine learning techniques, investigate the additional forecasting power of Automatic
Identification System (AIS) derived features, and to evaluate the profitability of applying
forecasted directional movements to trading strategies.
A Long-Short-Term Memory (LSTM) neural network is used to predict price movements for
the two closest quarterly, and the closest calendar year Capesize 5 Time Charter (5TC) FFAs.
We have derived features from AIS data to generate proxies for supply, demand and
geographical distribution for a subset of Capesize vessels. Additionally, we have included
commodity prices and macroeconomic variables. The forecasting horizon investigated has
been one week, two weeks, and one month ahead. To benchmark the LSTM model, we have
included Vector Autoregressive (VAR) models, Autoregressive Integrated Moving Average
(ARIMA) models and a Random Walk.
The VAR models were found to be superior at forecasting FFA prices, and the results showed
that the LSTM neural network and VAR show potential for predicting directional movements
of prices. The results further indicate that AIS data holds predictive capabilities regarding
directional movements of prices. Lastly, the trading results give implications of increased
profitability compared to buy-and-hold and trend-following benchmarks, by utilizing the
trading signals from the models
