International Journal of Information Engineering and Electronic Business (IJIEEB)
IJIEEB Vol. 17, No. 4, 8 Aug. 2025
Cover page and Table of Contents: PDF (size: 4640KB)
Comprehensive Intellectual Analysis of Statistical Data on Leading Energy Companies’ Actions
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Author(s)
Viktoriia Bulatova
1
Sofiia Popp
1
Victoria Vysotska
1
Yuriy Ushenko
2,3,*
Zhengbing Hu
4
Dmytro Uhryn
3
Index Terms
Energy Stocks, Time Series, Cumulative Return, Rolling Volatility, Correlation Analysis, K-Means Method, Financial Metrics (CAGR, Sharpe, Sortino, Max Drawdown, Calmar)
Abstract
The paper conducted a comprehensive analysis of the time series of stock prices of three leading energy companies – Shell, BP and ExxonMobil – for the period from January 2021 to January 2025. At the initial stage, data quality was checked: dates were set as indices, the absence of duplicates and missing values was confirmed, and descriptive statistics (mean, variance, skewness and kurtosis) were calculated. Next, the trends of adjusted closing prices (AdjClose) were analysed using moving averages (SMA14, SMA50), exponential smoothing, moving volatility (30-day standard deviation) and cumulative returns. It was found that еhe price dynamics growth has accelerated since 2022 against the background of the energy crisis caused by the war in Ukraine: ExxonMobil’s cumulative return reached ≈250% by mid-2022 and ≈350% at the beginning of 2025, Shell and BP, respectively ≈220% and ≈200% by 2024. Correlation analysis showed that BP and Shell have the most significant interdependence (r = 0.87, R² = 0.75). The autocorrelation method established high non-stationarity of the time series (ACF about one at low lags). K-Means clustering (k = 2) allowed us to distinguish periods of active growth and relative price consolidation, although the feature selection behind this clustering requires further clarification. The initially reported financial metrics (Sharpe, Sortino, and Calmar ratios) were significantly overstated due to unit errors, specifically, using percentage values as absolute figures. After applying appropriate annualization and decimal scaling performance indicators were obtained for ExxonMobil – CAGR = 36.84%, Sharpe ≈ 1.24, Sortino ≈ 1.9–2.5, Max Drawdown = 20.51%, Calmar ≈ 1.80; Shell: CAGR = 21.29%, Sharpe ≈ 0.76, Sortino ≈ 1.2–1.5, Max Drawdown = 25.04%, Calmar ≈ 0.85; BP: CAGR = 14.54%, Sharpe ≈ 0.53, Sortino ≈ 0.9–1.2, Max Drawdown = 26.23%, Calmar ≈ 0.55. The study confirms that ExxonMobil showed the most stable and substantial growth during the examined period, while BP exhibited the highest volatility. Shell demonstrated an intermediate performance level. The close correlation between Shell and BP is attributed to the similarity in their geographical market activity and stock behaviour. The choice of these methods of analysis is due to the desire to assess the behaviour of stocks during the period of increased market volatility caused by the energy crisis, geopolitical risks and changes in investor priorities. Technical analysis allows you to identify short- and medium-term patterns, clustering allows you to automatically separate market phases without the need for subjective hypotheses, and statistical metrics will enable you to compare the performance of assets within the industry. This research contributes to the broader field of financial analysis by demonstrating how machine learning and technical analytics tools can be applied to assess the resilience and relationships of assets during periods of market turmoil. The results can be helpful for institutional investors, financial analysts, and portfolio managers looking to adapt strategies to dynamic energy market conditions.
Cite This Paper
Viktoriia Bulatova, Sofiia Popp, Victoria Vysotska, Yuriy Ushenko, Zhengbing Hu, Dmytro Uhryn, "Comprehensive Intellectual Analysis of Statistical Data on Leading Energy Companies’ Actions", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.17, No.4, pp. 82-144, 2025. DOI:10.5815/ijieeb.2025.04.07
Reference
[1]Ly, S.; Sarwat, S.; Wong, W.K.; et al. A static and dynamic copula-based ARIMA-fGARCH approach to determinants of carbon dioxide emissions in Argentina. Environ. Sci. Pollut. Res. 2022, 29, 73241–73261. https://doi.org/10.1007/s11356-022-20906-7
[2]Heen, J.F.R.; Bachke, C. A comprehensive analysis of Brent Crude oil forecasting methods combining machine learning and quantitative models with application. MS thesis, Handelshøyskolen BI, Norway, 2023. https://biopen.bi.no/bi-xmlui/handle/11250/3106043
[3]Stasiak, M.D.; Staszak, Ż.; Siwek, J.; Wojcieszak, D. Application of State Models in a Binary–Temporal Representation for the Prediction and Modelling of Crude Oil Prices. Energies 2025, 18, 691. https://doi.org/10.3390/en18030691
[4]Przekota, G.; Szczepańska-Przekota, A. Directions of Price Transmission on the Diesel Oil Market in Poland. Energies 2025, 18, 139. https://doi.org/10.3390/en18010139
[5]Xiao, L.; Sioofy Khoojine, A. Dynamic Anomaly Detection in the Chinese Energy Market During Financial Turbulence Using Ratio Mutual Information and Crude Oil Price Movements. Energies 2024, 17, 5852. https://doi.org/10.3390/en17235852
[6]Stamatova, K.L. ExxonMobil Equity Research—Adapting to a Transitioning Energy Market. MS thesis, Universidade NOVA de Lisboa, Portugal, 2022. https://www.proquest.com/openview/735ce3235c2a4bb3ac2b1d9930130e27/1?cbl=2026366&diss=y&pq-origsite=gscholar
[7]Shah, M.; Kshirsagar, A.; Panchal, J. Applications of Artificial Intelligence (AI) and Machine Learning (ML) in the Petroleum Industry. CRC Press: Boca Raton, FL, USA, 2022. https://doi.org/10.1201/9781003279532
[8]Zuo, Z. Forecasting of Oil and Gas Stock Market in Major US. Theor. Nat. Sci. 2024, 42, 36–45. https://doi.org/10.54254/2753-8818/42/2024CH0211
[9]Rahman, M.K.; et al. Assessing the Effectiveness of Machine Learning Models in Predicting Stock Price Movements During Energy Crisis: Insights from Shell's Market Dynamics. J. Bus. Manag. Stud. 2025, 7, 44–61. https://doi.org/10.32996/jbms.2025.7.1.4
[10]Benali, M.; Lahboub, K. Modelling Stock Prices of Energy Sector Using Supervised Machine Learning Techniques. Int. J. Energy Econ. Policy 2024, 14, 594–602. https://doi.org/10.32479/ijeep.15553
[11]Agbede, O.O.; Akhigbe, E.E.; Ajayi, A.J.; Stanley, N. Financial Modeling for Global Energy Market Impacts of Geopolitical Events and Economic Regulations. Mod. Sci. Arch. Res. Rep. 2024, 10, 49. https://doi.org/10.30574/msarr.2024.10.2.0049
[12]Damås, J.H.; Inderberg, A. Incorporating News Sentiment in Intraday Crude Oil Futures Price Volatility Forecasting Using Deep Recurrent Neural Networks. MS thesis, University of Stavanger (UIS), Norway, 2024.
[13]Ghosh, I.; Sanyal, M.K.; Jana, R.K. Co-movement and Dynamic Correlation of Financial and Energy Markets: An Integrated Framework of Nonlinear Dynamics, Wavelet Analysis and DCC-GARCH. Comput. Econ. 2021, 57, 503–527. https://doi.org/10.1007/s10614-019-09965-0
[14]Kamalov, F. Forecasting significant stock price changes using neural networks. Neural Comput & Applic 2020, 32, 17655–17667. https://doi.org/10.1007/s00521-020-04942-3
[15]Feng, F.; Chen, H.; He, X.; Ding, J.; Sun, M.; Chua, T.S. Enhancing Stock Movement Prediction with Adversarial Training. arXiv preprint 2018, arXiv:1810.09936 https://doi.org/10.48550/arXiv.1810.09936
[16]Khaidem, L.; Saha, S.; Dey, S.R. Predicting the direction of stock market prices using random forest. arXiv preprint 2016, arXiv:1605.00003 https://doi.org/10.48550/arXiv.1605.00003
[17]Bublyk, M.; Kowalska-Styczeń, A.; Lytvyn, V.; Vysotska, V. The Ukrainian Economy Transformation into the Circular Based on Fuzzy-Logic Cluster Analysis. Energies 2021, 14, 5951. https://doi.org/10.3390/en14185951
[18]Huang, S.; et al. Co-movement of Coherence between Oil Prices and the Stock Market from the Joint Time-Frequency Perspective. Appl. Energy 2018, 221, 122–130. https://doi.org/10.1016/j.apenergy.2018.03.172
[19]Silva, E.G.S.; Legey, L.F.L.; Silva, E.A.S. Forecasting Oil Price Trends Using Wavelets and Hidden Markov Models. Energy Econ. 2010, 32, 1507–1519. https://doi.org/10.1016/j.eneco.2010.08.006
[20]Vysotska, V.; et al. Predicting the Effects of News on the Financial Market Based on Machine Learning Technology. In Proceedings of the 2023 IEEE 5th International Conference on Advanced Information and Communication Technologies (AICT), Lviv, Ukraine, 22–24 November 2023. https://doi.org/10.1109/AICT61584.2023.10452706
[21]Chen, J.M.; Rehman, M.U. A Pattern New in Every Moment: The Temporal Clustering of Markets for Crude Oil, Refined Fuels, and Other Commodities. Energies 2021, 14, 6099. https://doi.org/10.3390/en14196099
[22]Li, L.; Chen, Y. The Impact of Intellectual Property Protection on the Performance of Fossil Fuel Extraction and Production Companies in Developing Countries. Resour. Policy 2024, 89, 104617. https://doi.org/10.1016/j.resourpol.2023.104617
[23]Olajiga, O.K.; et al. Data Analytics in Energy Corporations: Conceptual Framework for Strategic Business Outcomes. World J. Adv. Res. Rev. 2024, 21, 952–963. https://doi.org/10.30574/wjarr.2024.21.3.0783
[24]Shahbaz, M.H.; Ahmad, S.; Malik, S.A. Green Intellectual Capital Heading towards Green Innovation and Environmental Performance: Assessing the Moderating Effect of Green Creativity in SMEs of Pakistan. Int. J. Innov. Sci. 2024. https://doi.org/10.1108/IJIS-08-2023-0169
[25]Zong, Z.; Guan, Y. AI-Driven Intelligent Data Analytics and Predictive Analysis in Industry 4.0: Transforming Knowledge, Innovation, and Efficiency. J. Knowl. Econ. 2024. https://doi.org/10.1007/s13132-024-02001-z
[26]Fida, K.; Abbasi, U.; Adnan, M.; Iqbal, S.; Mohamed, S.E.G. A Comprehensive Survey on Load Forecasting Hybrid Models: Navigating the Futuristic Demand Response Patterns through Experts and Intelligent Systems. Results Eng. 2024, 102773. https://doi.org/10.1016/j.rineng.2024.102773
[27]Ma, S.; Ding, W.; Liu, Y.; Zhang, Y.; Ren, S.; Kong, X.; Leng, J. Industry 4.0 and Cleaner Production: A Comprehensive Review of Sustainable and Intelligent Manufacturing for Energy-Intensive Manufacturing Industries. J. Clean. Prod. 2024, 142879. https://doi.org/10.101/10.1016/j.jclepro.2024.142879
[28]Kolotov, V.; Kudriavtsev, V.; Taranov, V.; Chumak, O.; Chumak O. European Security Architecture: the Need to Updat. Journal of Interdisc Iplinary Research 2022, 2(31), 178-183. https://www.magnanimitas.cz/ADALTA/120231/papers/A_31.pdf
[29]Montgomery, D. C., & Runger, G. C. (2019). Applied statistics and probability for engineers. John Wiley & Sons.
[30]Brockwell, P. J., & Davis, R. A. (1991). Time series: Theory and methods. Springer Science & Business Media.
[31]Brown, R. G. (1959). Statistical forecasting for inventory control. McGraw-Hill.
[32]Bodie, Z., Kane, A., & Marcus, A. J. (2018). Investments. McGraw-Hill Education.
[33]Pollard, D. (1990). Empirical processes: Theory and applications. Institute of Mathematical Statistics.
[34]Tukey, J. W. (1977). Exploratory data analysis. Reading, MA: Addison-Wesley.
[35]Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: Forecasting and control. John Wiley & Sons.
[36]Jain, A. K. (2010). "Data clustering: 50 years beyond K-means." Pattern Recognition Letters, 31(8), 651–666. https://doi.org/10.1016/j.patrec.2009.09.011
[37]Kang, W., de Gracia, F. P., & Ratti, R. A. (2017). "Oil price shocks, policy uncertainty, and stock returns of oil and gas corporations." Journal of International Money and Finance, 70, 344–359. https://doi.org/10.1016/j.jimonfin.2016.10.003
[38]Dutta, A. (2017). "Oil price uncertainty and clean energy stock returns: New evidence from crude oil volatility index." Journal of Cleaner Production, 164, 1157–1166. https://doi.org/10.1016/j.jclepro.2017.07.017
[39]Zhao, X. (2020). "Do the stock returns of clean energy corporations respond to oil price shocks and policy uncertainty?" Journal of Economic Structures, 9(1), 53. https://doi.org/10.1186/s40008-020-00229-x
[40]Caporale, G. M., Ali, F. M., & Spagnolo, N. (2015). "Oil price uncertainty and sectoral stock returns in China: A time-varying approach." China Economic Review, 34, 311–321. https://doi.org/10.1016/j.chieco.2015.02.001
[41]Caldara, D., & Iacoviello, M. (2022). "Measuring geopolitical risk." American Economic Review, 112(4), 1194–1225. https://doi.org/10.1257/aer.20191823
[42]Iacoviello, M. (2022). "Measuring geopolitical risk." Board of Governors of the Federal Reserve System. https://www.federalreserve.gov/econres/notes/feds-notes/measuring-geopolitical-risk-20220415.html
[43]Wang, W., Sun, Z., Wang, W., Hua, Q., & Wu, F. (2023). "The impact of environmental uncertainty on ESG performance: Emotional vs. rational." Journal of Cleaner Production, 397, 136528. https://doi.org/10.1016/j.jclepro.2023.136528
[44]Zyznarska-Dworczak, B. (2022). "Financial and ESG reporting in times of uncertainty." The Theoretical Journal of Accounting, 46(4), 161–180. https://doi.org/10.5604/01.3001.0016.0321
[45]Zhang, C., Farooq, U., Jamali, D., & Alam, M. M. (2024). "The role of ESG performance in the nexus between economic policy uncertainty and corporate investment." Research in International Business and Finance, 70, 102358. https://doi.org/10.1016/j.ribaf.2024.102358