How good are different machine and deep learning models in forecasting the future price of metals? Full sample versus sub-sample

Abstract

This study aims to forecast metal futures in commodity markets, including gold, silver, copper, platinum, palladium, and aluminium, using different machine and deep learning models. Prevalent models such as Stacked Long-Short Term Memory, Convolutional LSTM, Bidirectional LSTM, Support Vector Regressor, Extreme Gradient Boosting, and Gated Recurrent Unit are utilized. The model performance is assessed by multiple factors such as Root Mean Squared Error, Mean Absolute Error, and Mean Absolute Percentage Error. The study stands out by considering multiple metal commodity futures simultaneously, incorporating both Machine Learning and Deep Learning models, and conducting two sets of experiments with a full sample and subsample analysis. In addition, it uses different inputs of 30- and 60-days periods for robustness checks. Mean Absolute Percentage Error values suggest that different machine and deep learning models are efficient on prediction the future metal prices. However, the model performance varies significantly with the influence of metal choice, sample period, and inputs on prediction performance. Therefore, it suggests that constructing a theory based on machine and deep learning models is challenging.