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Forecasting Crude Oil Price Movements with Oil-Sensitive Stocks

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  • Chen, Shiu-Sheng

Abstract

This paper uses monthly data from 1984:M10 to 2012:M8 to show that oil-sensitive stock price indices, particularly those in the energy sector, have strong power in predicting nominal and real crude oil prices at short horizons (one-month-ahead predictions), using both in- and out-of-sample tests. In particular, the forecasts based on oil-sensitive stock price indices are able to outperform significantly the no-change forecasts. For example, using the NYSE Arca (AMEX) oil index as a predictor, the one-month-ahead forecasts for nominal crude oil prices reduce the mean squared prediction error by between 22% (for the West Texas Intermediate oil price) and 28% (for the Dubai oil price). Moreover, we find that the directional forecast based the AMEX oil index is ignificantly better than a 50:50 coin toss. The novelty of this analysis is that it proposes a new and valuable predictor that both reflects timely market information and is readily available for forecasting the spot oil price.

Suggested Citation

  • Chen, Shiu-Sheng, 2013. "Forecasting Crude Oil Price Movements with Oil-Sensitive Stocks," MPRA Paper 49240, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:49240
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    Cited by:

    1. Baumeister, Christiane & Kilian, Lutz & Lee, Thomas K., 2014. "Are there gains from pooling real-time oil price forecasts?," Energy Economics, Elsevier, vol. 46(S1), pages 33-43.
    2. Shiu‐Sheng Chen, 2016. "Commodity prices and related equity prices," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 49(3), pages 949-967, August.
    3. Christiane Baumeister & Lutz Kilian & Thomas K. Lee, 2017. "Inside the Crystal Ball: New Approaches to Predicting the Gasoline Price at the Pump," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(2), pages 275-295, March.
    4. Zhang, Yue-Jun & Wang, Jin-Li, 2019. "Do high-frequency stock market data help forecast crude oil prices? Evidence from the MIDAS models," Energy Economics, Elsevier, vol. 78(C), pages 192-201.
    5. Stavros Degiannakis, George Filis, and Vipin Arora, 2018. "Oil Prices and Stock Markets: A Review of the Theory and Empirical Evidence," The Energy Journal, International Association for Energy Economics, vol. 0(Number 5).
    6. Dai, Zhifeng & Kang, Jie, 2021. "Bond yield and crude oil prices predictability," Energy Economics, Elsevier, vol. 97(C).
    7. Funk, Christoph, 2018. "Forecasting the real price of oil - Time-variation and forecast combination," Energy Economics, Elsevier, vol. 76(C), pages 288-302.
    8. Phan, Dinh Hoang Bach & Narayan, Paresh Kumar & Gong, Qiang, 2021. "Terrorist attacks and oil prices: Hypothesis and empirical evidence," International Review of Financial Analysis, Elsevier, vol. 74(C).
    9. Ding Du & Ronald J Gunderson & Xiaobing Zhao, 2016. "Investor sentiment and oil prices," Journal of Asset Management, Palgrave Macmillan, vol. 17(2), pages 73-88, March.
    10. Baumeister, Christiane & Guérin, Pierre & Kilian, Lutz, 2015. "Do high-frequency financial data help forecast oil prices? The MIDAS touch at work," International Journal of Forecasting, Elsevier, vol. 31(2), pages 238-252.
    11. Yonghong Jiang & Gengyu Tian & Bin Mo, 2020. "Spillover and quantile linkage between oil price shocks and stock returns: new evidence from G7 countries," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-26, December.
    12. Liao, Shujie & Wang, Fengxia & Wu, Ting & Pan, Wei, 2016. "Crude oil price decision under considering emergency and release of strategic petroleum reserves," Energy, Elsevier, vol. 102(C), pages 436-443.
    13. Yin, Libo & Yang, Qingyuan, 2016. "Predicting the oil prices: Do technical indicators help?," Energy Economics, Elsevier, vol. 56(C), pages 338-350.
    14. Arunanondchai, Panit & Senia, Mark C. & Capps, Oral Jr, 2017. "Can U.S. EIA Retail Gasoline Price Forecasts Be Improved Upon?," 2017 Annual Meeting, February 4-7, 2017, Mobile, Alabama 252717, Southern Agricultural Economics Association.
    15. Ding Du & Xiaobing Zhao, 2017. "Financial investor sentiment and the boom/bust in oil prices during 2003–2008," Review of Quantitative Finance and Accounting, Springer, vol. 48(2), pages 331-361, February.
    16. Liu, Li & Wang, Yudong & Yang, Li, 2018. "Predictability of crude oil prices: An investor perspective," Energy Economics, Elsevier, vol. 75(C), pages 193-205.

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    More about this item

    Keywords

    oil-sensitive stock prices; oil prices; out-of-sample prediction;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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