Scanning the horizon : forecasting and trading on forward freight agreements using long short-term memory neural networks and AIS-derived features
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- Master Thesis 
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.