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Forecasting the Dubai financial market with a combination of momentum effect with a deep belief network

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  • Andreas Karathanasopoulos
  • Mohammed Osman

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.

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  • Andreas Karathanasopoulos & Mohammed Osman, 2019. "Forecasting the Dubai financial market with a combination of momentum effect with a deep belief network," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(4), pages 346-353, July.
    • Handle: RePEc:wly:jforec:v:38:y:2019:i:4:p:346-353
    DOI: 10.1002/for.2560
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    Cited by:

    1. Flavio Barboza & Geraldo Nunes Silva & José Augusto Fiorucci, 2023. "A review of artificial intelligence quality in forecasting asset prices," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1708-1728, November.
    2. Fengyu Han & Yue Wang, 2022. "Predicting Stock Price Movement after Disclosure of Corporate Annual Reports: A Case Study of 2021 China CSI 300 Stocks," Papers 2206.12528, arXiv.org, revised Jul 2022.
    3. Zeynep Ceylan, 2020. "Assessment of agricultural energy consumption of Turkey by MLR and Bayesian optimized SVR and GPR models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(6), pages 944-956, September.

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