IDEAS home Printed from https://ideas.repec.org/a/spr/jcsosc/v7y2024i1d10.1007_s42001-024-00259-6.html
   My bibliography  Save this article

Analyzing the influence of web search behavior on electricity market price: a case study of Japan electric power exchange

Author

Listed:
  • Ryosuke Gotoh

    (Shiga University)

Abstract

The Japan Electric Power Exchange (JEPX) has introduced a feed-in premium to promote the trading of renewable energy electricity in the market; thus, the exchange has become increasingly important for RE companies to maintain profitability in market trading. However, electricity prices are not only affected by directly measurable factors such as electricity demand, fuel prices, and weather but also by corporate bidding strategies, social conditions, and other human behaviors, making it difficult to predict electricity prices. Given that electricity demand is related to human behavior, this study focuses on web search behavior and clarifies the relationship between keyword search volumes and electricity market prices in Japan. Correlation and vector autoregression analyses results show a moderately strong positive correlation between the logarithmic difference of the keyword search volume and that of the electricity price. In addition, we find that the logarithmic difference of the electricity price tends to increase when that of the keyword search volume on the previous day increases. These results suggest that search volumes of specific keywords can be effective explanatory variables for area price prediction models and can help identify signs of price spikes.

Suggested Citation

  • Ryosuke Gotoh, 2024. "Analyzing the influence of web search behavior on electricity market price: a case study of Japan electric power exchange," Journal of Computational Social Science, Springer, vol. 7(1), pages 837-876, April.
    • Handle: RePEc:spr:jcsosc:v:7:y:2024:i:1:d:10.1007_s42001-024-00259-6
    DOI: 10.1007/s42001-024-00259-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s42001-024-00259-6
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s42001-024-00259-6?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Herbert A. Simon, 1955. "A Behavioral Model of Rational Choice," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 69(1), pages 99-118.
    2. Bikeri Adline & Kazushi Ikeda, 2023. "A Hawkes Model Approach to Modeling Price Spikes in the Japanese Electricity Market," Energies, MDPI, vol. 16(4), pages 1-20, February.
    3. Salm, Sarah & Hille, Stefanie Lena & Wüstenhagen, Rolf, 2016. "What are retail investors' risk-return preferences towards renewable energy projects? A choice experiment in Germany," Energy Policy, Elsevier, vol. 97(C), pages 310-320.
    4. Wataru Souma & Irena Vodenska & Hideaki Aoyama, 2019. "Enhanced news sentiment analysis using deep learning methods," Journal of Computational Social Science, Springer, vol. 2(1), pages 33-46, January.
    5. Daniel Kahneman & Amos Tversky, 2013. "Prospect Theory: An Analysis of Decision Under Risk," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 6, pages 99-127, World Scientific Publishing Co. Pte. Ltd..
    6. Klein, Martin & Deissenroth, Marc, 2017. "When do households invest in solar photovoltaics? An application of prospect theory," Energy Policy, Elsevier, vol. 109(C), pages 270-278.
    7. Tversky, Amos & Kahneman, Daniel, 1992. "Advances in Prospect Theory: Cumulative Representation of Uncertainty," Journal of Risk and Uncertainty, Springer, vol. 5(4), pages 297-323, October.
    8. Nofer, Michael & Hinz, Oliver, 2015. "Using Twitter to Predict the Stock Market: Where is the Mood Effect?," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 77140, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    9. Tayebeh Sadat Tabatabaei & Pedram Asef, 2021. "Evaluation of Energy Price Liberalization in Electricity Industry: A Data-Driven Study on Energy Economics," Energies, MDPI, vol. 14(22), pages 1-19, November.
    10. Ryosuke Gotoh & Tetsuo Tezuka & Benjamin C. McLellan, 2022. "Study on Behavioral Decision Making by Power Generation Companies Regarding Energy Transitions under Uncertainty," Energies, MDPI, vol. 15(2), pages 1-29, January.
    11. Deniz Ersan & Chifumi Nishioka & Ansgar Scherp, 2020. "Comparison of machine learning methods for financial time series forecasting at the examples of over 10 years of daily and hourly data of DAX 30 and S&P 500," Journal of Computational Social Science, Springer, vol. 3(1), pages 103-133, April.
    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. Daniel Fonseca Costa & Francisval Carvalho & Bruno César Moreira & José Willer Prado, 2017. "Bibliometric analysis on the association between behavioral finance and decision making with cognitive biases such as overconfidence, anchoring effect and confirmation bias," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1775-1799, June.
    2. Stefano DellaVigna, 2009. "Psychology and Economics: Evidence from the Field," Journal of Economic Literature, American Economic Association, vol. 47(2), pages 315-372, June.
    3. Klein, Martin & Deissenroth, Marc, 2017. "When do households invest in solar photovoltaics? An application of prospect theory," Energy Policy, Elsevier, vol. 109(C), pages 270-278.
    4. Leković Milјan, 2020. "Cognitive Biases as an Integral Part of Behavioral Finance," Economic Themes, Sciendo, vol. 58(1), pages 75-96, March.
    5. Thomas J. Brennan & Andrew W. Lo & Ruixun Zhang, 2018. "Variety Is the Spice of Life: Irrational Behavior as Adaptation to Stochastic Environments," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 8(03), pages 1-39, September.
    6. Schunk, Daniel, 2009. "Behavioral heterogeneity in dynamic search situations: Theory and experimental evidence," Journal of Economic Dynamics and Control, Elsevier, vol. 33(9), pages 1719-1738, September.
    7. Tian, Ye & Li, Yudi & Sun, Jian, 2022. "Stick or carrot for traffic demand management? Evidence from experimental economics," Transportation Research Part A: Policy and Practice, Elsevier, vol. 160(C), pages 235-254.
    8. Ng, Yew-Kwang & Wang, Jianguo, 2001. "Attitude choice, economic change, and welfare," Journal of Economic Behavior & Organization, Elsevier, vol. 45(3), pages 279-291, July.
    9. Ji, Xiangfeng & Chu, Yanyu, 2020. "A target-oriented bi-attribute user equilibrium model with travelers’ perception errors on the tolled traffic network," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 144(C).
    10. Sandri, Serena & Schade, Christian & Mußhoff, Oliver & Odening, Martin, 2010. "Holding on for too long? An experimental study on inertia in entrepreneurs' and non-entrepreneurs' disinvestment choices," Journal of Economic Behavior & Organization, Elsevier, vol. 76(1), pages 30-44, October.
    11. Shi, Hai-Liu & Chen, Sheng-Qun & Chen, Lei & Wang, Ying-Ming, 2021. "A neutral cross-efficiency evaluation method based on interval reference points in consideration of bounded rational behavior," European Journal of Operational Research, Elsevier, vol. 290(3), pages 1098-1110.
    12. repec:cup:judgdm:v:2:y:2007:i::p:115-125 is not listed on IDEAS
    13. Yang, Luhe & Zhang, Lianzhong & Yang, Duoxing, 2022. "Asymmetric micro-dynamics in spatial anonymous public goods game," Applied Mathematics and Computation, Elsevier, vol. 415(C).
    14. Enrico G. De Giorgi & David B. Brown & Melvyn Sim, 2010. "Dual representation of choice and aspirational preferences," University of St. Gallen Department of Economics working paper series 2010 2010-07, Department of Economics, University of St. Gallen.
    15. repec:cup:judgdm:v:16:y:2021:i:6:p:1324-1369 is not listed on IDEAS
    16. Pope, Robin & Selten, Reinhard, 2009. "Risk in a Simple Temporal Framework for Expected Utility Theory and for SKAT, the Stages of Knowledge Ahead Theory," Bonn Econ Discussion Papers 27/2009, University of Bonn, Bonn Graduate School of Economics (BGSE).
    17. Wu, Yunna & Xu, Chuanbo & Zhang, Ting, 2018. "Evaluation of renewable power sources using a fuzzy MCDM based on cumulative prospect theory: A case in China," Energy, Elsevier, vol. 147(C), pages 1227-1239.
    18. F. Knobloch & J. -F. Mercure, 2016. "The behavioural aspect of green technology investments: a general positive model in the context of heterogeneous agents," Papers 1603.06888, arXiv.org.
    19. Ralf Elbert & Lowis Seikowsky, 2017. "The influences of behavioral biases, barriers and facilitators on the willingness of forwarders’ decision makers to modal shift from unimodal road freight transport to intermodal road–rail freight tra," Journal of Business Economics, Springer, vol. 87(8), pages 1083-1123, November.
    20. Torgler, Benno, 2006. "The importance of faith: Tax morale and religiosity," Journal of Economic Behavior & Organization, Elsevier, vol. 61(1), pages 81-109, September.
    21. Zamri Ahmad & Haslindar Ibrahim & Jasman Tuyon, 2017. "Institutional investor behavioral biases: syntheses of theory and evidence," Management Research Review, Emerald Group Publishing Limited, vol. 40(5), pages 578-603, May.
    22. Chris Starmer, 1999. "Cycling with Rules of Thumb: An Experimental Test for a new form of Non-Transitive Behaviour," Theory and Decision, Springer, vol. 46(2), pages 139-157, April.

    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:spr:jcsosc:v:7:y:2024:i:1:d:10.1007_s42001-024-00259-6. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.