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Forecasting Crude Oil Price Movements With Oil-Sensitive Stocks

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  • SHIU-SHENG CHEN

Abstract

type="main" xml:lang="en"> 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 (1-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 1-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 on the AMEX oil index is significantly 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.(JEL G17, Q43, Q47, C53)

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  • Shiu-Sheng Chen, 2014. "Forecasting Crude Oil Price Movements With Oil-Sensitive Stocks," Economic Inquiry, Western Economic Association International, vol. 52(2), pages 830-844, April.
  • Handle: RePEc:bla:ecinqu:v:52:y:2014:i:2:p:830-844
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    2. 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.
    3. Liu, Li & Wang, Yudong & Yang, Li, 2018. "Predictability of crude oil prices: An investor perspective," Energy Economics, Elsevier, vol. 75(C), pages 193-205.
    4. 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.
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    6. 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).
    7. 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.
    8. Dai, Zhifeng & Kang, Jie, 2021. "Bond yield and crude oil prices predictability," Energy Economics, Elsevier, vol. 97(C).
    9. Funk, Christoph, 2018. "Forecasting the real price of oil - Time-variation and forecast combination," Energy Economics, Elsevier, vol. 76(C), pages 288-302.
    10. 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).
    11. 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.
    12. 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.
    13. Yin, Libo & Yang, Qingyuan, 2016. "Predicting the oil prices: Do technical indicators help?," Energy Economics, Elsevier, vol. 56(C), pages 338-350.
    14. 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.
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    16. 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.

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    More about this item

    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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