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Predicting the oil prices: Do technical indicators help?

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  1. Libo Yin & Qingyuan Yang & Zhi Su, 2017. "Predictability of structural co-movement in commodity prices: the role of technical indicators," Quantitative Finance, Taylor & Francis Journals, vol. 17(5), pages 795-812, May.
  2. Henriques, Irene & Sadorsky, Perry, 2023. "Forecasting rare earth stock prices with machine learning," Resources Policy, Elsevier, vol. 86(PA).
  3. Qingxiang Han & Mengxi He & Yaojie Zhang & Muhammad Umar, 2023. "Default return spread: A powerful predictor of crude oil price returns," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1786-1804, November.
  4. 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).
  5. Wen, Chufu & Zhu, Haoyang & Dai, Zhifeng, 2023. "Forecasting commodity prices returns: The role of partial least squares approach," Energy Economics, Elsevier, vol. 125(C).
  6. Nicholas Apergis, 2023. "Forecasting energy prices: Quantile‐based risk models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(1), pages 17-33, January.
  7. Zhang, Yaojie & Wang, Yudong, 2023. "Forecasting crude oil futures market returns: A principal component analysis combination approach," International Journal of Forecasting, Elsevier, vol. 39(2), pages 659-673.
  8. Conlon, Thomas & Cotter, John & Eyiah-Donkor, Emmanuel, 2022. "The illusion of oil return predictability: The choice of data matters!," Journal of Banking & Finance, Elsevier, vol. 134(C).
  9. 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).
  10. 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).
  11. 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).
  12. Tian, Guangning & Peng, Yuchao & Meng, Yuhao, 2023. "Forecasting crude oil prices in the COVID-19 era: Can machine learn better?," Energy Economics, Elsevier, vol. 125(C).
  13. Han, Liyan & Lv, Qiuna & Yin, Libo, 2017. "Can investor attention predict oil prices?," Energy Economics, Elsevier, vol. 66(C), pages 547-558.
  14. Ma, Feng & Liu, Jing & Wahab, M.I.M. & Zhang, Yaojie, 2018. "Forecasting the aggregate oil price volatility in a data-rich environment," Economic Modelling, Elsevier, vol. 72(C), pages 320-332.
  15. Dai, Zhifeng & Kang, Jie, 2021. "Bond yield and crude oil prices predictability," Energy Economics, Elsevier, vol. 97(C).
  16. Funk, Christoph, 2018. "Forecasting the real price of oil - Time-variation and forecast combination," Energy Economics, Elsevier, vol. 76(C), pages 288-302.
  17. 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).
  18. Batista Soares, David & Borocco, Etienne, 2022. "Rational destabilization in commodity markets," Journal of Commodity Markets, Elsevier, vol. 25(C).
  19. Zhang, Yaojie & He, Mengxi & Wen, Danyan & Wang, Yudong, 2023. "Forecasting crude oil price returns: Can nonlinearity help?," Energy, Elsevier, vol. 262(PB).
  20. Xuluo Yin & Jiangang Peng & Tian Tang, 2018. "Improving the Forecasting Accuracy of Crude Oil Prices," Sustainability, MDPI, vol. 10(2), pages 1-9, February.
  21. Zhang, Yaojie & Wei, Yu & Zhang, Yi & Jin, Daxiang, 2019. "Forecasting oil price volatility: Forecast combination versus shrinkage method," Energy Economics, Elsevier, vol. 80(C), pages 423-433.
  22. Wang, Yudong & Liu, Li & Wu, Chongfeng, 2017. "Forecasting the real prices of crude oil using forecast combinations over time-varying parameter models," Energy Economics, Elsevier, vol. 66(C), pages 337-348.
  23. Degiannakis, Stavros & Filis, George, 2017. "Forecasting oil prices," MPRA Paper 77531, University Library of Munich, Germany.
  24. Zhang, Ditian & Tang, Pan, 2023. "Forecasting European Union allowances futures: The role of technical indicators," Energy, Elsevier, vol. 270(C).
  25. Zhang, Yue-Jun & Chevallier, Julien & Guesmi, Khaled, 2017. "“De-financialization” of commodities? Evidence from stock, crude oil and natural gas markets," Energy Economics, Elsevier, vol. 68(C), pages 228-239.
  26. Syed Abul, Basher & Perry, Sadorsky, 2022. "Forecasting Bitcoin price direction with random forests: How important are interest rates, inflation, and market volatility?," MPRA Paper 113293, University Library of Munich, Germany.
  27. Bekiroglu, Korkut & Duru, Okan & Gulay, Emrah & Su, Rong & Lagoa, Constantino, 2018. "Predictive analytics of crude oil prices by utilizing the intelligent model search engine," Applied Energy, Elsevier, vol. 228(C), pages 2387-2397.
  28. Mensi, Walid & Rehman, Mobeen Ur & Maitra, Debasish & Al-Yahyaee, Khamis Hamed & Vo, Xuan Vinh, 2021. "Oil, natural gas and BRICS stock markets: Evidence of systemic risks and co-movements in the time-frequency domain," Resources Policy, Elsevier, vol. 72(C).
  29. Li, Ye & Chen, Yiyan & Lean, Hooi Hooi, 2024. "Geopolitical risk and crude oil price predictability: Novel decomposition ensemble approach based ternary interval number series," Resources Policy, Elsevier, vol. 92(C).
  30. Yi, Yongsheng & Ma, Feng & Zhang, Yaojie & Huang, Dengshi, 2018. "Forecasting the prices of crude oil using the predictor, economic and combined constraints," Economic Modelling, Elsevier, vol. 75(C), pages 237-245.
  31. Xiaoye Jin, 2022. "Evaluating the predictive power of intraday technical trading in China's crude oil market," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(7), pages 1416-1432, November.
  32. F. Benedetto & L. Mastroeni & P. Vellucci, 2021. "Modeling the flow of information between financial time-series by an entropy-based approach," Annals of Operations Research, Springer, vol. 299(1), pages 1235-1252, April.
  33. Gong, Xue & Ye, Xin & Zhang, Weiguo & Zhang, Yue, 2023. "Predicting energy futures high-frequency volatility using technical indicators: The role of interaction," Energy Economics, Elsevier, vol. 119(C).
  34. Degiannakis, Stavros & Filis, George, 2018. "Forecasting oil prices: High-frequency financial data are indeed useful," Energy Economics, Elsevier, vol. 76(C), pages 388-402.
  35. Yu, Dan & Chen, Chuang & Wang, Yudong & Zhang, Yaojie, 2023. "Hedging pressure momentum and the predictability of oil futures returns," Economic Modelling, Elsevier, vol. 121(C).
  36. James Ming Chen & Mobeen Ur Rehman, 2021. "A Pattern New in Every Moment: The Temporal Clustering of Markets for Crude Oil, Refined Fuels, and Other Commodities," Energies, MDPI, vol. 14(19), pages 1-58, September.
  37. David Batista Soares & Etienne Borocco, 2022. "Rational destabilization in commodity markets [Déstabilisation rationnelle des marchés de matières premières]," Post-Print hal-03256534, HAL.
  38. Nonejad, Nima, 2021. "Predicting the return on the spot price of crude oil out-of-sample by conditioning on news-based uncertainty measures: Some new empirical results," Energy Economics, Elsevier, vol. 104(C).
  39. Jia, Xiaolan & Ruan, Xinfeng & Zhang, Jin E., 2023. "Carr and Wu’s (2020) framework in the oil ETF option market," Journal of Commodity Markets, Elsevier, vol. 31(C).
  40. Dai, Zhifeng & Zhang, Xiaotong & Liang, Chao, 2024. "Efficient predictability of oil price: The role of VIX-based panic index shadow line difference," Energy Economics, Elsevier, vol. 129(C).
  41. Benedetto, Francesco & Mastroeni, Loretta & Quaresima, Greta & Vellucci, Pierluigi, 2020. "Does OVX affect WTI and Brent oil spot variance? Evidence from an entropy analysis," Energy Economics, Elsevier, vol. 89(C).
  42. 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).
  43. Guo, Lili & Huang, Xinya & Li, Yanjiao & Li, Houjian, 2023. "Forecasting crude oil futures price using machine learning methods: Evidence from China," Energy Economics, Elsevier, vol. 127(PA).
  44. Czudaj, Robert L., 2019. "Crude oil futures trading and uncertainty," Energy Economics, Elsevier, vol. 80(C), pages 793-811.
  45. Zhang, Yaojie & Ma, Feng & Shi, Benshan & Huang, Dengshi, 2018. "Forecasting the prices of crude oil: An iterated combination approach," Energy Economics, Elsevier, vol. 70(C), pages 472-483.
  46. Perry Sadorsky, 2021. "Predicting Gold and Silver Price Direction Using Tree-Based Classifiers," JRFM, MDPI, vol. 14(5), pages 1-21, April.
  47. Ren, Xiaohang & Duan, Kun & Tao, Lizhu & Shi, Yukun & Yan, Cheng, 2022. "Carbon prices forecasting in quantiles," Energy Economics, Elsevier, vol. 108(C).
  48. Sadorsky, Perry, 2022. "Forecasting solar stock prices using tree-based machine learning classification: How important are silver prices?," The North American Journal of Economics and Finance, Elsevier, vol. 61(C).
  49. Drachal, Krzysztof, 2018. "Comparison between Bayesian and information-theoretic model averaging: Fossil fuels prices example," Energy Economics, Elsevier, vol. 74(C), pages 208-251.
  50. Liu, Jing & Ma, Feng & Tang, Yingkai & Zhang, Yaojie, 2019. "Geopolitical risk and oil volatility: A new insight," Energy Economics, Elsevier, vol. 84(C).
  51. Zhang, Zhikai & He, Mengxi & Zhang, Yaojie & Wang, Yudong, 2022. "Geopolitical risk trends and crude oil price predictability," Energy, Elsevier, vol. 258(C).
  52. 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).
  53. Zhang, Yaojie & Ma, Feng & Wei, Yu, 2019. "Out-of-sample prediction of the oil futures market volatility: A comparison of new and traditional combination approaches," Energy Economics, Elsevier, vol. 81(C), pages 1109-1120.
  54. Yin, Libo & Feng, Jiabao, 2019. "Can investors attention on oil markets predict stock returns?," The North American Journal of Economics and Finance, Elsevier, vol. 48(C), pages 786-800.
  55. 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.
  56. Song, Yixuan & He, Mengxi & Wang, Yudong & Zhang, Yaojie, 2022. "Forecasting crude oil market volatility: A newspaper-based predictor regarding petroleum market volatility," Resources Policy, Elsevier, vol. 79(C).
  57. He, Mengxi & Zhang, Yaojie & Wen, Danyan & Wang, Yudong, 2021. "Forecasting crude oil prices: A scaled PCA approach," Energy Economics, Elsevier, vol. 97(C).
  58. Chia-Lin Chang & Jukka Ilomäki & Hannu Laurila & Michael McAleer, 2018. "Moving Average Market Timing in European Energy Markets: Production Versus Emissions," Energies, MDPI, vol. 11(12), pages 1-24, November.
  59. Wu, Junhao & Dong, Jinghan & Wang, Zhaocai & Hu, Yuan & Dou, Wanting, 2023. "A novel hybrid model based on deep learning and error correction for crude oil futures prices forecast," Resources Policy, Elsevier, vol. 83(C).
  60. Lan Bai & Xiafei Li & Yu Wei & Guiwu Wei, 2022. "Does crude oil futures price really help to predict spot oil price? New evidence from density forecasting," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(3), pages 3694-3712, July.
  61. Tan, Xueping & Sirichand, Kavita & Vivian, Andrew & Wang, Xinyu, 2022. "Forecasting European carbon returns using dimension reduction techniques: Commodity versus financial fundamentals," International Journal of Forecasting, Elsevier, vol. 38(3), pages 944-969.
  62. Liu, Li & Wang, Yudong & Yang, Li, 2018. "Predictability of crude oil prices: An investor perspective," Energy Economics, Elsevier, vol. 75(C), pages 193-205.
  63. Soroush Omranpour & Guillaume Rabusseau & Reihaneh Rabbany, 2024. "Higher Order Transformers: Enhancing Stock Movement Prediction On Multimodal Time-Series Data," Papers 2412.10540, arXiv.org.
  64. Perry Sadorsky, 2021. "A Random Forests Approach to Predicting Clean Energy Stock Prices," JRFM, MDPI, vol. 14(2), pages 1-20, January.
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