Machine Learning in FX Carry Basket Prediction

Artificial Neural Networks (ANN), Support Vector Machines (SVM) and Relevance Vector Machines (RVM) were used to predict daily returns for an FX carry basket. Market observable exogenous variables known to have a relationship with the basket along with lags of the basket's return were used as inputs into these methods. Combinations of these networks were used in a committee and simple trading rules based on this amalgamated output were used to predict when carry basket returns would be negative for a day and hence a trader should go short this long-biased asset. The effect of using the networks for regression to predict actual returns was compared to their use as classifiers to predict whether the following day's return would be up or down. Assuming highly conservative estimates of trading costs, over the 10 year (approximately 3000 trading day) rolling out of sample period investigated, improvements of 120% in MAR ratio, 110% in Sortino and 80% in Sharpe relative to the 'Always In' benchmark were found. Furthermore, the extent of the maximum draw-down was reduced by 19% and the longest draw-down period was 53% shorter