Deep learning systems for forecasting the prices of crude oil and precious metals  

作　　者：Parisa Foroutan Salim Lahmiri 

机构地区：[1]Department of Supply Chain and Business Technology Management,Concordia University,Montreal,Canada

出　　处：《Financial Innovation》2024年第1期781-820,共40页金融创新(英文)

摘　　要：Commodity markets,such as crude oil and precious metals,play a strategic role in the economic development of nations,with crude oil prices influencing geopolitical relations and the global economy.Moreover,gold and silver are argued to hedge the stock and cryptocurrency markets during market downsides.Therefore,accurate forecasting of crude oil and precious metals prices is critical.Nevertheless,due to the nonlinear nature,substantial fluctuations,and irregular cycles of crude oil and precious metals,predicting their prices is a challenging task.Our study contributes to the commodity market price forecasting literature by implementing and comparing advanced deep-learning models.We address this gap by including silver alongside gold in our analysis,offering a more comprehensive understanding of the precious metal markets.This research expands existing knowledge and provides valuable insights into predicting commodity prices.In this study,we implemented 16 deep-and machine-learning models to forecast the daily price of the West Texas Intermediate(WTI),Brent,gold,and silver markets.The employed deep-learning models are long short-term memory(LSTM),BiLSTM,gated recurrent unit(GRU),bidirectional gated recurrent units(BiGRU),T2V-BiLSTM,T2V-BiGRU,convolutional neural networks(CNN),CNN-BiLSTM,CNN-BiGRU,temporal convolutional network(TCN),TCN-BiLSTM,and TCN-BiGRU.We compared the forecasting performance of deep-learning models with the baseline random forest,LightGBM,support vector regression,and k-nearest neighborhood models using mean absolute error(MAE),mean absolute percentage error,and root mean squared error as evaluation criteria.By considering different sliding window lengths,we examine the forecasting performance of our models.Our results reveal that the TCN model outperforms the others for WTI,Brent,and silver,achieving the lowest MAE values of 1.444,1.295,and 0.346,respectively.The BiGRU model performs best for gold,with an MAE of 15.188 using a 30-day input sequence.Furthermore,LightGBM exhibits comparable perf

关 键 词：Crude oil forecasting Precious metal forecasting Deep learning Temporal convolutional networks Time2Vector LightGBM 

分 类 号：F42[经济管理—产业经济]