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How to manage a multifactor-driven crude oil market more effectively? A revisit based on the multiple criteria perspective

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  • Yu, Yue
  • Wang, Jianzhou
  • Jiang, He
  • Lu, Haiyan

Abstract

With the complexity of the international crude oil market deepening, it is of profound significance for information recipients and actors to clarify the operation mechanism of the multi-factor-driven international crude oil market, grasp its crucial drivers, and establish a reasonable and effective analysis and early warning system. This paper reviewed the results of existing studies and proposed a collection of daily frequency proxies from five perspectives based on the principles of plurality, completeness and rationality. What's more, a three-dimensional assessment strategy was also developed based on causality, predictability, and necessity, which complemented and extended existing methodologies and findings on crude oil market drivers and theoretically quantified the economic and statistical properties of various proxies. After that, the most critical agents were extracted by relying on the logic of multi-criteria decision-making, which solved the problem of scattered attention in the analysis of the crude oil market. Finally, based on machine learning and artificial intelligence, a hybrid forecasting model that blended key driving agents with both error accuracy, as well as directional accuracy, was constructed. Taking Brent crude oil, the benchmark for more than two-thirds of the world's crude oil, as an example, the findings verified the importance and necessity of correctly grasping the key drivers and confirmed that this study can provide a more scientifically sound research methodology and theoretical basis for crude oil market analysis and early warning based on limited attention.

Suggested Citation

  • Yu, Yue & Wang, Jianzhou & Jiang, He & Lu, Haiyan, 2025. "How to manage a multifactor-driven crude oil market more effectively? A revisit based on the multiple criteria perspective," Resources Policy, Elsevier, vol. 100(C).
  • Handle: RePEc:eee:jrpoli:v:100:y:2025:i:c:s0301420724008134
    DOI: 10.1016/j.resourpol.2024.105446
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    1. Chen, Jing-Chao & Zhou, Yu & Wang, Xi, 2018. "Profitability of simple stationary technical trading rules with high-frequency data of Chinese Index Futures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 1664-1678.
    2. Wang, Yudong & Hao, Xianfeng, 2023. "Forecasting the real prices of crude oil: What is the role of parameter instability?," Energy Economics, Elsevier, vol. 117(C).
    3. Zhang, Li & Wang, Lu & Wang, Xunxiao & Zhang, Yaojie & Pan, Zhigang, 2022. "How macro-variables drive crude oil volatility? Perspective from the STL-based iterated combination method," Resources Policy, Elsevier, vol. 77(C).
    4. Ron Alquist & Lutz Kilian, 2010. "What do we learn from the price of crude oil futures?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 539-573.
    5. Ben Salem, Leila & Nouira, Ridha & Jeguirim, Khaled & Rault, Christophe, 2022. "The determinants of crude oil prices: Evidence from ARDL and nonlinear ARDL approaches," Resources Policy, Elsevier, vol. 79(C).
    6. Urolagin, Siddhaling & Sharma, Nikhil & Datta, Tapan Kumar, 2021. "A combined architecture of multivariate LSTM with Mahalanobis and Z-Score transformations for oil price forecasting," Energy, Elsevier, vol. 231(C).
    7. Wen, Danyan & Liu, Li & Wang, Yudong & Zhang, Yaojie, 2022. "Forecasting crude oil market returns: Enhanced moving average technical indicators," Resources Policy, Elsevier, vol. 76(C).
    8. Dieter, Peter & Caron, Matthew & Schryen, Guido, 2023. "Integrating driver behavior into last-mile delivery routing: Combining machine learning and optimization in a hybrid decision support framework," European Journal of Operational Research, Elsevier, vol. 311(1), pages 283-300.
    9. Jamie L. Cross & Bao H. Nguyen & Trung Duc Tran, 2022. "The role of precautionary and speculative demand in the global market for crude oil," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 882-895, August.
    10. Qadan, Mahmoud & Nama, Hazar, 2018. "Investor sentiment and the price of oil," Energy Economics, Elsevier, vol. 69(C), pages 42-58.
    11. Akdoğan, Kurmaş, 2020. "Fundamentals versus speculation in oil market: The role of asymmetries in price adjustment?," Resources Policy, Elsevier, vol. 67(C).
    12. Mishra, Aswini Kumar & Ghate, Kshitish & Renganathan, Jayashree & Kennet, Joushita J. & Rajderkar, Nilay Pradeep, 2022. "Rolling, recursive evolving and asymmetric causality between crude oil and gold prices: Evidence from an emerging market," Resources Policy, Elsevier, vol. 75(C).
    13. Dai, Zhifeng & Kang, Jie & Hu, Yangli, 2021. "Efficient predictability of oil price: The role of number of IPOs and U.S. dollar index," Resources Policy, Elsevier, vol. 74(C).
    14. Dario Caldara & Matteo Iacoviello, 2022. "Measuring Geopolitical Risk," American Economic Review, American Economic Association, vol. 112(4), pages 1194-1225, April.
    15. Chowdhury, Mohammad Ashraful Ferdous & Meo, Muhammad Saeed & Uddin, Ajim & Haque, Md. Mahmudul, 2021. "Asymmetric effect of energy price on commodity price: New evidence from NARDL and time frequency wavelet approaches," Energy, Elsevier, vol. 231(C).
    16. Daniele Valenti, 2022. "Modelling the Global Price of Oil: Is there any Role for the Oil Futures-spot Spread?," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2).
    17. Xiao, Di & Wang, Jun, 2020. "Dynamic complexity and causality of crude oil and major stock markets," Energy, Elsevier, vol. 193(C).
    18. James D. Hamilton, 2009. "Understanding Crude Oil Prices," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 179-206.
    19. Lv, Wendai & Wu, Qian, 2022. "Global economic conditions index and oil price predictability," Finance Research Letters, Elsevier, vol. 48(C).
    20. Christopher R. Knittel & Robert S. Pindyck, 2016. "The Simple Economics of Commodity Price Speculation," American Economic Journal: Macroeconomics, American Economic Association, vol. 8(2), pages 85-110, April.
    21. Sun, Jingyun & Zhao, Panpan & Sun, Shaolong, 2022. "A new secondary decomposition-reconstruction-ensemble approach for crude oil price forecasting," Resources Policy, Elsevier, vol. 77(C).
    22. Zhang, Yaojie & Wahab, M.I.M. & Wang, Yudong, 2023. "Forecasting crude oil market volatility using variable selection and common factor," International Journal of Forecasting, Elsevier, vol. 39(1), pages 486-502.
    23. Cheng, Xian & Wu, Peng & Liao, Stephen Shaoyi & Wang, Xuelian, 2023. "An integrated model for crude oil forecasting: Causality assessment and technical efficiency," Energy Economics, Elsevier, vol. 117(C).
    24. Zhu, Huiming & Chen, Weiyan & Hau, Liya & Chen, Qitong, 2021. "Time-frequency connectedness of crude oil, economic policy uncertainty and Chinese commodity markets: Evidence from rolling window analysis," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    25. Shrestha, Keshab, 2014. "Price discovery in energy markets," Energy Economics, Elsevier, vol. 45(C), pages 229-233.
    26. Yadav, Deepanshu & Nagar, Deepak & Ramu, Palaniappan & Deb, Kalyanmoy, 2023. "Visualization-aided multi-criteria decision-making using interpretable self-organizing maps," European Journal of Operational Research, Elsevier, vol. 309(3), pages 1183-1200.
    27. Ilyes Abid & Stéphane Goutte & Farid Mkaouar & Khaled Guesmi, 2019. "Optimal strategy between extraction and storage of crude oil," Annals of Operations Research, Springer, vol. 281(1), pages 3-26, October.
    28. Yang, Lu, 2019. "Connectedness of economic policy uncertainty and oil price shocks in a time domain perspective," Energy Economics, Elsevier, vol. 80(C), pages 219-233.
    29. S. Boubaker & Z. Liu & Y. Zhang, 2022. "Forecasting Oil Commodity Spot Price in a Data-Rich Environment," Post-Print hal-04445034, HAL.
    30. Mensi, Walid & Shafiullah, Muhammad & Vo, Xuan Vinh & Kang, Sang Hoon, 2022. "Asymmetric spillovers and connectedness between crude oil and currency markets using high-frequency data," Resources Policy, Elsevier, vol. 77(C).
    31. Christiane Baumeister & James D. Hamilton, 2019. "Structural Interpretation of Vector Autoregressions with Incomplete Identification: Revisiting the Role of Oil Supply and Demand Shocks," American Economic Review, American Economic Association, vol. 109(5), pages 1873-1910, May.
    32. Chai, Jian & Xing, Li-Min & Zhou, Xiao-Yang & Zhang, Zhe George & Li, Jie-Xun, 2018. "Forecasting the WTI crude oil price by a hybrid-refined method," Energy Economics, Elsevier, vol. 71(C), pages 114-127.
    33. Huang, Jianbai & Ding, Qian & Zhang, Hongwei & Guo, Yaoqi & Suleman, Muhammad Tahir, 2021. "Nonlinear dynamic correlation between geopolitical risk and oil prices: A study based on high-frequency data," Research in International Business and Finance, Elsevier, vol. 56(C).
    34. Chen, Yufeng & Xu, Jing & Miao, Jiafeng, 2023. "Dynamic volatility contagion across the Baltic dry index, iron ore price and crude oil price under the COVID-19: A copula-VAR-BEKK-GARCH-X approach," Resources Policy, Elsevier, vol. 81(C).
    35. Raza, Syed Ali & Guesmi, Khaled & Belaid, Fateh & Shah, Nida, 2022. "Time-frequency causality and connectedness between oil price shocks and the world food prices," Research in International Business and Finance, Elsevier, vol. 62(C).
    36. Li, Zhao & Luo, Zujiang & Wang, Yan & Fan, Guanyu & Zhang, Jianmang, 2022. "Suitability evaluation system for the shallow geothermal energy implementation in region by Entropy Weight Method and TOPSIS method," Renewable Energy, Elsevier, vol. 184(C), pages 564-576.
    37. Jan Bentzen, 2007. "Does OPEC influence crude oil prices? Testing for co-movements and causality between regional crude oil prices," Applied Economics, Taylor & Francis Journals, vol. 39(11), pages 1375-1385.
    38. Gkillas, Konstantinos & Manickavasagam, Jeevananthan & Visalakshmi, S., 2022. "Effects of fundamentals, geopolitical risk and expectations factors on crude oil prices," Resources Policy, Elsevier, vol. 78(C).
    39. Ahmed, Walid M.A., 2022. "On the higher-order moment interdependence of stock and commodity markets: A wavelet coherence analysis," The Quarterly Review of Economics and Finance, Elsevier, vol. 83(C), pages 135-151.
    40. Angela Poulakidas & Fred Joutz, 2009. "Exploring the link between oil prices and tanker rates," Maritime Policy & Management, Taylor & Francis Journals, vol. 36(3), pages 215-233, June.
    41. Alzahrani, Mohammed & Masih, Mansur & Al-Titi, Omar, 2014. "Linear and non-linear Granger causality between oil spot and futures prices: A wavelet based test," Journal of International Money and Finance, Elsevier, vol. 48(PA), pages 175-201.
    42. Zhang, Yue-Jun & Wei, Yi-Ming, 2010. "The crude oil market and the gold market: Evidence for cointegration, causality and price discovery," Resources Policy, Elsevier, vol. 35(3), pages 168-177, September.
    43. Ilyes Abid & Stéphane Goutte & Farid Mkaouar & Khaled Guesmi, 2019. "Optimal strategy between extraction and storage of crude oil," Annals of Operations Research, Springer, vol. 281(1), pages 3-26, October.
    44. repec:hal:journl:hal-04444805 is not listed on IDEAS
    45. Zhang, Yaojie & Ma, Feng & Wang, Yudong, 2019. "Forecasting crude oil prices with a large set of predictors: Can LASSO select powerful predictors?," Journal of Empirical Finance, Elsevier, vol. 54(C), pages 97-117.
    46. Corbet, Shaen & Hou, Yang (Greg) & Hu, Yang & Oxley, Les, 2021. "An analysis of investor behaviour and information flows surrounding the negative WTI oil price futures event," Energy Economics, Elsevier, vol. 104(C).
    47. Xing, Li-Min & Zhang, Yue-Jun, 2022. "Forecasting crude oil prices with shrinkage methods: Can nonconvex penalty and Huber loss help?," Energy Economics, Elsevier, vol. 110(C).
    48. Balcilar, Mehmet & Gungor, Hasan & Hammoudeh, Shawkat, 2015. "The time-varying causality between spot and futures crude oil prices: A regime switching approach," International Review of Economics & Finance, Elsevier, vol. 40(C), pages 51-71.
    49. Kaufmann, Robert K. & Bradford, Andrew & Belanger, Laura H. & Mclaughlin, John P. & Miki, Yosuke, 2008. "Determinants of OPEC production: Implications for OPEC behavior," Energy Economics, Elsevier, vol. 30(2), pages 333-351, March.
    50. Ji, Qiang & Fan, Ying, 2016. "Modelling the joint dynamics of oil prices and investor fear gauge," Research in International Business and Finance, Elsevier, vol. 37(C), pages 242-251.
    51. Loutia, Amine & Mellios, Constantin & Andriosopoulos, Kostas, 2016. "Do OPEC announcements influence oil prices?," Energy Policy, Elsevier, vol. 90(C), pages 262-272.
    52. Li, Sufang & Zhang, Hu & Yuan, Di, 2019. "Investor attention and crude oil prices: Evidence from nonlinear Granger causality tests," Energy Economics, Elsevier, vol. 84(C).
    53. Guo, Jingjun & Zhao, Zhengling & Sun, Jingyun & Sun, Shaolong, 2022. "Multi-perspective crude oil price forecasting with a new decomposition-ensemble framework," Resources Policy, Elsevier, vol. 77(C).
    54. Kisswani, Khalid M., 2016. "Does OPEC act as a cartel? Empirical investigation of coordination behavior," Energy Policy, Elsevier, vol. 97(C), pages 171-180.
    55. James D. Hamilton, 2009. "Understanding Crude Oil Prices," The Energy Journal, , vol. 30(2), pages 179-206, April.
    56. Chu, Pyung Kun & Hoff, Kristian & Molnár, Peter & Olsvik, Magnus, 2022. "Crude oil: Does the futures price predict the spot price?," Research in International Business and Finance, Elsevier, vol. 60(C).
    57. Kliber, Agata & Łęt, Blanka, 2022. "Degree of connectedness and the transfer of news across the oil market and the European stocks," Energy, Elsevier, vol. 239(PC).
    58. McHale, Ian G. & Holmes, Benjamin, 2023. "Estimating transfer fees of professional footballers using advanced performance metrics and machine learning," European Journal of Operational Research, Elsevier, vol. 306(1), pages 389-399.
    59. Salisu, Afees A. & Pierdzioch, Christian & Gupta, Rangan, 2021. "Geopolitical risk and forecastability of tail risk in the oil market: Evidence from over a century of monthly data," Energy, Elsevier, vol. 235(C).
    60. Yan Ding & Yue Liu & Pierre Failler, 2022. "The Impact of Uncertainties on Crude Oil Prices: Based on a Quantile-on-Quantile Method," Energies, MDPI, vol. 15(10), pages 1-35, May.
    61. 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.
    62. Yin, Libo & Yang, Qingyuan, 2016. "Predicting the oil prices: Do technical indicators help?," Energy Economics, Elsevier, vol. 56(C), pages 338-350.
    63. Chen, Yan & Qiao, Gaoxiu & Zhang, Feipeng, 2022. "Oil price volatility forecasting: Threshold effect from stock market volatility," Technological Forecasting and Social Change, Elsevier, vol. 180(C).
    64. Xu Wang & Xueyan Wu & Yingying Zhou, 2022. "Conditional Dynamic Dependence and Risk Spillover between Crude Oil Prices and Foreign Exchange Rates: New Evidence from a Dynamic Factor Copula Model," Energies, MDPI, vol. 15(14), pages 1-21, July.
    65. Bai, Yun & Li, Xixi & Yu, Hao & Jia, Suling, 2022. "Crude oil price forecasting incorporating news text," International Journal of Forecasting, Elsevier, vol. 38(1), pages 367-383.
    66. 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.
    67. Chen, Qitong & Zhu, Huiming & Yu, Dongwei & Hau, Liya, 2022. "How does investor attention matter for crude oil prices and returns? Evidence from time-frequency quantile causality analysis," The North American Journal of Economics and Finance, Elsevier, vol. 59(C).
    68. Amine Loutia & Constantin Mellios & Kostas Andriosopoulos, 2016. "Do OPEC announcements influence oil prices?," Post-Print hal-03968824, HAL.
    69. Guliyev, Hasraddin & Mustafayev, Eldayag, 2022. "Predicting the changes in the WTI crude oil price dynamics using machine learning models," Resources Policy, Elsevier, vol. 77(C).
    70. Zhang, Dayong, 2017. "Oil shocks and stock markets revisited: Measuring connectedness from a global perspective," Energy Economics, Elsevier, vol. 62(C), pages 323-333.
    71. Gallo, Andres & Mason, Paul & Shapiro, Steve & Fabritius, Michael, 2010. "What is behind the increase in oil prices? Analyzing oil consumption and supply relationship with oil price," Energy, Elsevier, vol. 35(10), pages 4126-4141.
    72. Li, Guohui & Yin, Shibo & Yang, Hong, 2022. "A novel crude oil prices forecasting model based on secondary decomposition," Energy, Elsevier, vol. 257(C).
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