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Crude Oil Spot Price Forecasting Based on Multiple Crude Oil Markets and Timeframes

Author

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  • Shangkun Deng

    (Graduate School of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan)

  • Akito Sakurai

    (Graduate School of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan)

Abstract

This study proposes a multiple kernel learning (MKL)-based regression model for crude oil spot price forecasting and trading. We used a well-known trend-following technical analysis indicator, the moving average convergence and divergence (MACD) indicator, for extracting features from original spot prices. Additionally, we factored in the possibility that movements of target crude oil prices may be related to other important crude oil markets besides the target market for the prediction time horizon since traders may find price movement information within other relevant crude oil markets useful. We also considered multiple timeframes in this study since trends may differ across different timeframes and, in fact, traders may use their own timeframes. Therefore, for forecasting target crude oil prices, this study emphasizes on features pertaining to other important crude oil markets and different timeframes in addition to features of the target crude oil market and target timeframe. Moreover, the MKL framework has been used to fuse information extracted from different sources and timeframes of the same data source. Experimental results show that out-of-sample forecasting using the MKL method is superior to benchmark methods in terms of root mean square error (RMSE) and average percentage profit ( APP ). They also show that the information from multiple timeframes is useful for prediction, but that from another crude oil market is not.

Suggested Citation

  • Shangkun Deng & Akito Sakurai, 2014. "Crude Oil Spot Price Forecasting Based on Multiple Crude Oil Markets and Timeframes," Energies, MDPI, vol. 7(5), pages 1-19, April.
    • Handle: RePEc:gam:jeners:v:7:y:2014:i:5:p:2761-2779:d:35518
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    Cited by:

    1. Lu-Tao Zhao & Li-Na Liu & Zi-Jie Wang & Ling-Yun He, 2019. "Forecasting Oil Price Volatility in the Era of Big Data: A Text Mining for VaR Approach," Sustainability, MDPI, vol. 11(14), pages 1-20, July.
    2. Issouf Fofana & John Sabau & Amidou Betie, 2015. "Measurement of the Relative Free Radical Content of Insulating Oils of Petroleum Origin," Energies, MDPI, vol. 8(8), pages 1-13, July.
    3. Josué M. Polanco-Martínez & Luis M. Abadie, 2016. "Analyzing Crude Oil Spot Price Dynamics versus Long Term Future Prices: A Wavelet Analysis Approach," Energies, MDPI, vol. 9(12), pages 1-19, December.

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