[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/a/wly/jforec/v41y2022i5p933-944.html
   My bibliography  Save this article

Volatility forecasting for crude oil based on text information and deep learning PSO‐LSTM model

Author

Listed:
  • Xingrui Jiao
  • Yuping Song
  • Yang Kong
  • Xiaolong Tang
Abstract
In this paper, a method to forecast the volatility of crude oil market based on text mining technology and deep learning model is proposed. First, the text mining technology is used to construct the textual features hidden in the news headlines of crude oil market, including the emotional polarity score, investor sentiment classification, and risk factors. Then the textual features with other features reflecting fluctuations of the crude oil market are combined as inputs of the deep learning long short‐term memory (LSTM) model for better forecasting results. Furthermore, the particle swarm optimization (PSO) is used to optimize the hyper‐parameters of the deep learning LSTM model to further improve the forecasting ability. The results in this study show that the LSTM model optimized by PSO algorithm has reduced 7.66%, 8.15%, 8.2%, 10.93%, and 23.46% on the mean absolute error (MAE) of crude oil volatility forecasting compared with other models. And it is more stable in multiple‐step forecasting results compared with the econometric model. In addition, compared with the PSO‐LSTM model without textual features, the forecasting accuracy of the PSO‐LSTM model with textual features has reduced by 6.77% and 5.12% in the MAE and the mean square error (MSE), respectively.

Suggested Citation

  • Xingrui Jiao & Yuping Song & Yang Kong & Xiaolong Tang, 2022. "Volatility forecasting for crude oil based on text information and deep learning PSO‐LSTM model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(5), pages 933-944, August.
    • Handle: RePEc:wly:jforec:v:41:y:2022:i:5:p:933-944
    DOI: 10.1002/for.2839
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/for.2839
    Download Restriction: no
    File URL: https://libkey.io/10.1002/for.2839?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Qadan, Mahmoud & Nama, Hazar, 2018. "Investor sentiment and the price of oil," Energy Economics, Elsevier, vol. 69(C), pages 42-58.
    2. Duc Khuong Nguyen & Thomas Walther, 2020. "Modeling and forecasting commodity market volatility with long‐term economic and financial variables," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(2), pages 126-142, March.
    3. Li, Xuerong & Shang, Wei & Wang, Shouyang, 2019. "Text-based crude oil price forecasting: A deep learning approach," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1548-1560.
    4. Yan‐ran Ma & Qiang Ji & Jiaofeng Pan, 2019. "Oil financialization and volatility forecast: Evidence from multidimensional predictors," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(6), pages 564-581, September.
    5. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    6. Park, Jungwook & Ratti, Ronald A., 2008. "Oil price shocks and stock markets in the U.S. and 13 European countries," Energy Economics, Elsevier, vol. 30(5), pages 2587-2608, September.
    7. Param Silvapulle & Imad A. Moosa, 1999. "The relationship between spot and futures prices: Evidence from the crude oil market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 19(2), pages 175-193, April.
    8. Sadorsky, Perry, 2006. "Modeling and forecasting petroleum futures volatility," Energy Economics, Elsevier, vol. 28(4), pages 467-488, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ouyang, Zisheng & Lu, Min & Lai, Yongzeng, 2023. "Forecasting stock index return and volatility based on GAVMD- Carbon-BiLSTM: How important is carbon emission trading?," Energy Economics, Elsevier, vol. 128(C).
    2. Du, Xiaoxu & Tang, Zhenpeng & Chen, Kaijie, 2023. "A novel crude oil futures trading strategy based on volume-price time-frequency decomposition with ensemble deep reinforcement learning," Energy, Elsevier, vol. 285(C).
    3. 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).
    4. Binrong Wu & Zhongrui Wang & Lin Wang, 2024. "Interpretable corn future price forecasting with multivariate time series," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(5), pages 1575-1594, August.
    5. Li, Jieyi & Qian, Shuangyue & Li, Ling & Guo, Yuanxuan & Wu, Jun & Tang, Ling, 2024. "A novel secondary decomposition method for forecasting crude oil price with twitter sentiment," Energy, Elsevier, vol. 290(C).
    6. Anurag Kulshrestha & Abhishek Yadav & Himanshu Sharma & Shikha Suman, 2024. "A deep learning‐based multivariate decomposition and ensemble framework for container throughput forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(7), pages 2685-2704, November.
    7. Wuyue An & Lin Wang & Dongfeng Zhang, 2023. "Comprehensive commodity price forecasting framework using text mining methods," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1865-1888, November.
    8. Lirong Gan & Wei-han Liu, 2024. "Option Pricing Based on the Residual Neural Network," Computational Economics, Springer;Society for Computational Economics, vol. 63(4), pages 1327-1347, April.
    9. Che, Zhongyuan & Peng, Chong & Yue, Chenxiao, 2024. "Optimizing LSTM with multi-strategy improved WOA for robust prediction of high-speed machine tests data," Chaos, Solitons & Fractals, Elsevier, vol. 178(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Xinya & Lucey, Brian & Huang, Shupei, 2022. "Can gold hedge against oil price movements: Evidence from GARCH-EVT wavelet modeling," Journal of Commodity Markets, Elsevier, vol. 27(C).
    2. Scarcioffolo, Alexandre R. & Etienne, Xiaoli L., 2021. "Regime-switching energy price volatility: The role of economic policy uncertainty," International Review of Economics & Finance, Elsevier, vol. 76(C), pages 336-356.
    3. Kang, Sang Hoon & Yoon, Seong-Min, 2013. "Modeling and forecasting the volatility of petroleum futures prices," Energy Economics, Elsevier, vol. 36(C), pages 354-362.
    4. Aloui, Riadh & Hammoudeh, Shawkat & Nguyen, Duc Khuong, 2013. "A time-varying copula approach to oil and stock market dependence: The case of transition economies," Energy Economics, Elsevier, vol. 39(C), pages 208-221.
    5. Miroslava Zavadska & Lucía Morales & Joseph Coughlan, 2018. "The Lead–Lag Relationship between Oil Futures and Spot Prices—A Literature Review," IJFS, MDPI, vol. 6(4), pages 1-22, October.
    6. Lang, Korbinian & Auer, Benjamin R., 2020. "The economic and financial properties of crude oil: A review," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    7. Dimitrios Kartsonakis-Mademlis & Nikolaos Dritsakis, 2020. "Does the Choice of the Multivariate GARCH Model on Volatility Spillovers Matter? Evidence from Oil Prices and Stock Markets in G7 Countries," International Journal of Energy Economics and Policy, Econjournals, vol. 10(5), pages 164-182.
    8. Cheong, Chin Wen, 2009. "Modeling and forecasting crude oil markets using ARCH-type models," Energy Policy, Elsevier, vol. 37(6), pages 2346-2355, June.
    9. Rim Ammar Lamouchi & Suha Mahmoud Alawi, 2020. "Dynamic Linkages Between the Oil Spot, Oil Futures, and Stock Markets: Evidence from Dubai," International Journal of Energy Economics and Policy, Econjournals, vol. 10(1), pages 377-383.
    10. Mário Correia Fernandes & José Carlos Dias & João Pedro Vidal Nunes, 2024. "Performance comparison of alternative stochastic volatility models and its determinants in energy futures: COVID‐19 and Russia–Ukraine conflict features," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(3), pages 343-383, March.
    11. Qadan, Mahmoud & Idilbi-Bayaa, Yasmeen, 2020. "Risk appetite and oil prices," Energy Economics, Elsevier, vol. 85(C).
    12. Muhammad Irfan Malik & Abdul Rashid, 2017. "Return And Volatility Spillover Between Sectoral Stock And Oil Price: Evidence From Pakistan Stock Exchange," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 12(02), pages 1-22, June.
    13. Qadan, Mahmoud & Idilbi-Bayaa, Yasmeen, 2021. "The day-of-the-week-effect on the volatility of commodities," Resources Policy, Elsevier, vol. 71(C).
    14. Muhammad Kashif & Thomas Leirvik, 2022. "The MAX Effect in an Oil Exporting Country: The Case of Norway," JRFM, MDPI, vol. 15(4), pages 1-16, March.
    15. C. P. Gupta & Sanjay Sehgal & Sahaj Wadhwa, 2018. "Agricultural Commodity Trading: Is it Destabilizing Spot Markets?," Vikalpa: The Journal for Decision Makers, , vol. 43(1), pages 47-57, March.
    16. Andriosopoulos, Kostas & Galariotis, Emilios & Spyrou, Spyros, 2017. "Contagion, volatility persistence and volatility spill-overs: The case of energy markets during the European financial crisis," Energy Economics, Elsevier, vol. 66(C), pages 217-227.
    17. Xiao, Jihong & Wen, Fenghua & He, Zhifang, 2023. "Impact of geopolitical risks on investor attention and speculation in the oil market: Evidence from nonlinear and time-varying analysis," Energy, Elsevier, vol. 267(C).
    18. Fan, Ying & Zhang, Yue-Jun & Tsai, Hsien-Tang & Wei, Yi-Ming, 2008. "Estimating 'Value at Risk' of crude oil price and its spillover effect using the GED-GARCH approach," Energy Economics, Elsevier, vol. 30(6), pages 3156-3171, November.
    19. repec:ipg:wpaper:2013-009 is not listed on IDEAS
    20. Omid Faseli, 2020. "The relationship between European Brent crude oil price development and the US macroeconomy," International Journal of Research in Business and Social Science (2147-4478), Center for the Strategic Studies in Business and Finance, vol. 9(1), pages 80-87, January.
    21. Yudong Wang & Li Liu, 2016. "Crude oil and world stock markets: volatility spillovers, dynamic correlations, and hedging," Empirical Economics, Springer, vol. 50(4), pages 1481-1509, June.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:jforec:v:41:y:2022:i:5:p:933-944. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www3.interscience.wiley.com/cgi-bin/jhome/2966 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.