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Forecasting ability of the investor sentiment endurance index: The case of oil service stock returns and crude oil prices

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  • He, Ling T.
  • Casey, K.M.

Abstract

Using a binomial probability distribution model this paper creates an endurance index of oil service investor sentiment. The index reflects the probability of the high or low stock price being the close price for the PHLX Oil Service Sector Index. Results of this study reveal the substantial forecasting ability of the sentiment endurance index. Monthly and quarterly rolling forecasts of returns of oil service stocks have an overall accuracy as high as 52% to 57%. In addition, the index shows decent forecasting ability on changes in crude oil prices, especially, WTI prices. The accuracy of 6-quarter rolling forecasts is 55%. The sentiment endurance index, along with the procedure of true forecasting and accuracy ratio, applied in this study provides investors and analysts of oil service sector stocks and crude oil prices as well as energy policy-makers with effective analytical tools.

Suggested Citation

  • He, Ling T. & Casey, K.M., 2015. "Forecasting ability of the investor sentiment endurance index: The case of oil service stock returns and crude oil prices," Energy Economics, Elsevier, vol. 47(C), pages 121-128.
    • Handle: RePEc:eee:eneeco:v:47:y:2015:i:c:p:121-128
    DOI: 10.1016/j.eneco.2014.11.005
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    References listed on IDEAS

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    Cited by:

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    2. Jiang, Zhe & Zhang, Lin & Zhang, Lingling & Wen, Bo, 2022. "Investor sentiment and machine learning: Predicting the price of China's crude oil futures market," Energy, Elsevier, vol. 247(C).
    3. Yajie Qi & Huajiao Li & Sui Guo & Sida Feng, 2019. "Dynamic Transmission of Correlation between Investor Attention and Stock Price: Evidence from China’s Energy Industry Typical Stocks," Complexity, Hindawi, vol. 2019, pages 1-15, December.
    4. Jiang, He & Hu, Weiqiang & Xiao, Ling & Dong, Yao, 2022. "A decomposition ensemble based deep learning approach for crude oil price forecasting," Resources Policy, Elsevier, vol. 78(C).
    5. Ouyang, Zi-sheng & Liu, Meng-tian & Huang, Su-su & Yao, Ting, 2022. "Does the source of oil price shocks matter for the systemic risk?," Energy Economics, Elsevier, vol. 109(C).
    6. Duangnate, Kannika & Mjelde, James W., 2017. "Comparison of data-rich and small-scale data time series models generating probabilistic forecasts: An application to U.S. natural gas gross withdrawals," Energy Economics, Elsevier, vol. 65(C), pages 411-423.
    7. Pan, Zhiyuan & Fu, Ziqian & Wang, Yudong & Dong, Qingma, 2024. "Exploiting the sentiments: A simple approach for improving cross hedging effectiveness," Energy Economics, Elsevier, vol. 134(C).
    8. Yue-Jun Zhang & Shu-Hui Li, 2019. "The impact of investor sentiment on crude oil market risks: evidence from the wavelet approach," Quantitative Finance, Taylor & Francis Journals, vol. 19(8), pages 1357-1371, August.

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    More about this item

    Keywords

    Endurance index of oil service investor sentiment; Forecasting ability; Rolling forecast; Accuracy ratio;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • M21 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Economics - - - Business Economics

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