IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i3p691-d1582429.html
   My bibliography  Save this article

Application of State Models in a Binary–Temporal Representation for the Prediction and Modelling of Crude Oil Prices

Author

Listed:
  • Michał Dominik Stasiak

    (Department of Investment and Real Estate, Poznań University of Economics and Business, al. Niepodleglosci 10, 61-875 Poznań, Poland)

  • Żaneta Staszak

    (The Faculty of Civil and Transport Engineering, Poznan University of Technology, 5 M. Skłodowska-Curie Square, 60-965 Poznań, Poland)

  • Joanna Siwek

    (Faculty of Mathematics and Computer Science, Department of Artificial Intelligence, Adam Mickiewicz University, Uniwersytetu Poznańskiego 4, 61-614 Poznań, Poland)

  • Dawid Wojcieszak

    (Department of Biosystems Engineering, Poznań University of Life Sciences, ul. Wojska Polskiego 50, 60-627 Poznań, Poland)

Abstract

Crude oil prices have a key meaning for the economies of most countries. Their levels shape the general production costs in many sectors. Oil prices are also a base for financial derivatives like CFD contracts, which are popular nowadays. Due to these reasons, the possibility of an effective prediction of the direction of future changes in the price of crude oil is especially significant. Most existing works focus on the analysis of daily closing prices. This kind of approach results, on the one hand, in losing important information about the dynamics of changes during the day. On the other hand, it does not allow for the modelling of short-term price changes that are especially important in cases of financial derivatives having crude oil as their base instrument. The goal of the following article is the analysis of possible applications of a binary–temporal representation in the modelling and construction of effective decision support systems on the crude oil market. The analysis encompasses all researched state models, e.g., those applying mean and trend analysis. Also, the selection of parameters was optimized for Brent crude oil rates. The presented research confirms the high effectiveness of our state modelling system in predicting oil prices on a level that allows for the construction of financially effective investment decision support systems. The obtained results were verified based on proper backtests from different quotation periods. The presented results can be used both in scientific analyses and in the construction of investment support tools for the crude oil market.

Suggested Citation

  • Michał Dominik Stasiak & Żaneta Staszak & Joanna Siwek & Dawid Wojcieszak, 2025. "Application of State Models in a Binary–Temporal Representation for the Prediction and Modelling of Crude Oil Prices," Energies, MDPI, vol. 18(3), pages 1-14, February.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:3:p:691-:d:1582429
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/3/691/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/18/3/691/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Michał Dominik Stasiak, 2022. "Algoritmic Trading System Based on State Model for Moving Average in a Binary-Temporal Representation," Risks, MDPI, vol. 10(4), pages 1-15, March.
    2. Degiannakis, Stavros & Filis, George, 2018. "Forecasting oil prices: High-frequency financial data are indeed useful," Energy Economics, Elsevier, vol. 76(C), pages 388-402.
    3. Weijermars, R. & Sun, Z., 2018. "Regression analysis of historic oil prices: A basis for future mean reversion price scenarios," Global Finance Journal, Elsevier, vol. 35(C), pages 177-201.
    4. van de Ven, Dirk Jan & Fouquet, Roger, 2017. "Historical energy price shocks and their changing effects on the economy," Energy Economics, Elsevier, vol. 62(C), pages 204-216.
    5. Yin, Libo & Yang, Qingyuan, 2016. "Predicting the oil prices: Do technical indicators help?," Energy Economics, Elsevier, vol. 56(C), pages 338-350.
    6. Michail Filippidis & George Filis & Georgios Magkonis, 2024. "Evaluating Oil Price Forecasts: A Meta-analysis," The Energy Journal, , vol. 45(2), pages 71-89, March.
    7. Michał Dominik Stasiak, 2018. "A study on the influence of the discretisation unit on the effectiveness of modelling currency exchange rates using the binary-temporal representation," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 28(2), pages 57-70.
    8. Lutz Kilian, 2008. "The Economic Effects of Energy Price Shocks," Journal of Economic Literature, American Economic Association, vol. 46(4), pages 871-909, December.
    9. Tiwari, Aviral Kumar & Trabelsi, Nader & Alqahtani, Faisal & Hammoudeh, Shawkat, 2019. "Analysing systemic risk and time-frequency quantile dependence between crude oil prices and BRICS equity markets indices: A new look," Energy Economics, Elsevier, vol. 83(C), pages 445-466.
    10. Dbouk, Wassim & Jamali, Ibrahim, 2018. "Predicting daily oil prices: Linear and non-linear models," Research in International Business and Finance, Elsevier, vol. 46(C), pages 149-165.
    11. Mensi, Walid & Yousaf, Imran & Vo, Xuan Vinh & Kang, Sang Hoon, 2022. "Asymmetric spillover and network connectedness between gold, BRENT oil and EU subsector markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 76(C).
    12. Michał Dominik Stasiak & Żaneta Staszak, 2024. "Modelling and Forecasting Crude Oil Prices Using Trend Analysis in a Binary-Temporal Representation," Energies, MDPI, vol. 17(14), pages 1-13, July.
    13. Cristiana Tudor & Andrei Anghel, 2021. "The Financialization of Crude Oil Markets and Its Impact on Market Efficiency: Evidence from the Predictive Ability and Performance of Technical Trading Strategies," Energies, MDPI, vol. 14(15), pages 1-19, July.
    Full references (including those not matched with items on IDEAS)

    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. Michał Dominik Stasiak & Żaneta Staszak, 2024. "Modelling and Forecasting Crude Oil Prices Using Trend Analysis in a Binary-Temporal Representation," Energies, MDPI, vol. 17(14), pages 1-13, July.
    2. 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).
    3. Hillard G. Huntington, 2017. "The Historical Roots of U.S. Energy Price Shocks," The Energy Journal, International Association for Energy Economics, vol. 0(Number 5).
    4. Huntington, Hillard, 2016. "The Historical “Roots” of U.S. Energy Price Shocks: Supplemental Results," MPRA Paper 74701, University Library of Munich, Germany.
    5. Lin, Boqiang & Bai, Rui, 2021. "Oil prices and economic policy uncertainty: Evidence from global, oil importers, and exporters’ perspective," Research in International Business and Finance, Elsevier, vol. 56(C).
    6. Zhu, Huiming & Huang, Xi & Ye, Fangyu & Li, Shuang, 2024. "Frequency spillover effects and cross-quantile dependence between crude oil and stock markets: Evidence from BRICS and G7 countries," The North American Journal of Economics and Finance, Elsevier, vol. 70(C).
    7. Bouteska, Ahmed & Hajek, Petr & Fisher, Ben & Abedin, Mohammad Zoynul, 2023. "Nonlinearity in forecasting energy commodity prices: Evidence from a focused time-delayed neural network," Research in International Business and Finance, Elsevier, vol. 64(C).
    8. Jihoon Lee & Hong Chong Cho, 2021. "Impact of Structural Oil Price Shock Factors on the Gasoline Market and Macroeconomy in South Korea," Sustainability, MDPI, vol. 13(4), pages 1-23, February.
    9. Nicholas Apergis, 2023. "Forecasting energy prices: Quantile‐based risk models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(1), pages 17-33, January.
    10. Naeem, Muhammad Abubakr & Pham, Linh & Senthilkumar, Arunachalam & Karim, Sitara, 2022. "Oil shocks and BRIC markets: Evidence from extreme quantile approach," Energy Economics, Elsevier, vol. 108(C).
    11. Gatfaoui, Hayette, 2015. "Pricing the (European) option to switch between two energy sources: An application to crude oil and natural gas," Energy Policy, Elsevier, vol. 87(C), pages 270-283.
    12. Stavros Degiannakis & George Filis & Vipin Arora, 2018. "Oil Prices and Stock Markets: A Review of the Theory and Empirical Evidence," The Energy Journal, , vol. 39(5), pages 85-130, September.
    13. Soma Patra, 2022. "Oil price shocks, firm entry and exit in a heterogeneous firm model," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 55(1), pages 349-378, February.
    14. Wang, Suhui, 2023. "Tail dependence, dynamic linkages, and extreme spillover between the stock and China's commodity markets," Journal of Commodity Markets, Elsevier, vol. 29(C).
    15. 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).
    16. 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.
    17. Song, Yixuan & He, Mengxi & Wang, Yudong & Zhang, Yaojie, 2023. "Forecasting crude oil prices: A reduced-rank approach," International Review of Economics & Finance, Elsevier, vol. 88(C), pages 698-711.
    18. Hanif, Waqas & Hadhri, Sinda & El Khoury, Rim, 2024. "Quantile spillovers and connectedness between oil shocks and stock markets of the largest oil producers and consumers," Journal of Commodity Markets, Elsevier, vol. 34(C).
    19. Turco, Enrico & Bazzana, Davide & Rizzati, Massimiliano & Ciola, Emanuele & Vergalli, Sergio, 2022. "Energy price shocks and stabilization policies in a multi-agent macroeconomic model for the Euro Area," FEEM Working Papers 324171, Fondazione Eni Enrico Mattei (FEEM).
    20. Zhang, Yaojie & He, Mengxi & Wen, Danyan & Wang, Yudong, 2023. "Forecasting crude oil price returns: Can nonlinearity help?," Energy, Elsevier, vol. 262(PB).

    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:gam:jeners:v:18:y:2025:i:3:p:691-:d:1582429. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.