Forecasting the Dubai financial market with a combination of momentum effect with a deep belief network. (22nd January 2019)

• Record Type: Journal Article
• Authors: Karathanasopoulos, Andreas; Osman, Mohammed
• Is Part Of: Journal of forecasting. Volume 38:Number 4(2019)
• Page Start: 346
• Page End: 353
• Publication Date: 2019-01-22
• Subjects: deep belief network -- input selection -- momentum -- transaction cost; Forecasting -- Periodicals; Forecasting -- Mathematical models -- Periodicals; 003.2
• DOI: 10.1002/for.2560
• Languages: English
• ISSNs: 0277-6693

Abstract

Abstract: Applying recent advances in machine learning techniques, we propose a hybrid model to forecast the Dubai financial market general index. Particularly, we exploit a deep belief networks model that applies a restricted Boltzmann machine as its main component in combination with momentum effects. We also introduce an innovative way of selecting the inputs by using momentum effects. With this hybrid methodology we generate a prediction model along with a comparison of three different linear models. The results obtained from the hybrid model are better and more stable than the three linear models. The findings support that the hybrid model we applied will find their way into finance because of their reliability and good performance.