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Evaluating the Role of Information Disclosure on Bidding Behavior in Wholesale Electricity Markets

Author

Listed:
  • David P. Brown

    (University of Alberta, Department of Economics)

  • Daniel O. Cajueiro

    (University of Brasilia)

  • Andrew Eckert

    (University of Alberta, Department of Economics)

  • Douglas Silveira

    (University of Alberta, Department of Economics)

Abstract

Real-time information has the potential to improve market outcomes in wholesale electricity markets. However, transparency can also facilitate coordination between firms, raising questions over the appropriate extent of information disclosure. Despite this ongoing debate, there is a lack of understanding of the information employed by firms when bidding in wholesale electricity markets. We use data from Alberta’s wholesale market and leverage machine learning techniques to evaluate the real-time information firms use when forming their bidding decisions. We find that aggregate market-level variables emerge as important predictors, while detailed firm-specific information does not lead to a material improvement in predicting firms’ bidding decisions. These results suggest that firm-specific information, which has raised concerns because of its potential use in facilitating coordinated behavior, may not be required to promote efficient market outcomes.

Suggested Citation

  • David P. Brown & Daniel O. Cajueiro & Andrew Eckert & Douglas Silveira, 2024. "Evaluating the Role of Information Disclosure on Bidding Behavior in Wholesale Electricity Markets," Working Papers 2024-02, University of Alberta, Department of Economics.
    • Handle: RePEc:ris:albaec:2024_002
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    References listed on IDEAS

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    1. David P. Brown & Andrew Eckert, 2022. "Pricing Patterns in Wholesale Electricity Markets: Unilateral Market Power or Coordinated Behavior?," Journal of Industrial Economics, Wiley Blackwell, vol. 70(1), pages 168-216, March.
    2. Jon Kleinberg & Jens Ludwig & Sendhil Mullainathan & Ziad Obermeyer, 2015. "Prediction Policy Problems," American Economic Review, American Economic Association, vol. 105(5), pages 491-495, May.
    3. repec:cdl:agrebk:qt0g79j32p is not listed on IDEAS
    4. Bergheimer, Stefan & Cantillon, Estelle & Reguant, Mar, 2023. "Price and quantity discovery without commitment," International Journal of Industrial Organization, Elsevier, vol. 90(C).
    5. Gaurab Aryal & Federico Ciliberto & Benjamin T Leyden, 2022. "Coordinated Capacity Reductions and Public Communication in the Airline Industry," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 89(6), pages 3055-3084.
    6. Green, Edward J & Porter, Robert H, 1984. "Noncooperative Collusion under Imperfect Price Information," Econometrica, Econometric Society, vol. 52(1), pages 87-100, January.
    7. Marcjasz, Grzegorz & Narajewski, Michał & Weron, Rafał & Ziel, Florian, 2023. "Distributional neural networks for electricity price forecasting," Energy Economics, Elsevier, vol. 125(C).
    8. Xinlei Mi & Baiming Zou & Fei Zou & Jianhua Hu, 2021. "Permutation-based identification of important biomarkers for complex diseases via machine learning models," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    9. Girish Godekere Panchakshara Murthy & Vijayalakshmi Sedidi, 2014. "Forecasting Electricity Prices in Deregulated Wholesale Spot Electricity Market: A Review," International Journal of Energy Economics and Policy, Econjournals, vol. 4(1), pages 32-42.
    10. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    11. Csereklyei, Zsuzsanna & Khezr, Peyman, 2024. "How do changes in settlement periods affect wholesale market prices? Evidence from Australia's National Electricity Market," Energy Economics, Elsevier, vol. 132(C).
    12. Arkadiusz Jk{e}drzejewski & Jesus Lago & Grzegorz Marcjasz & Rafa{l} Weron, 2022. "Electricity Price Forecasting: The Dawn of Machine Learning," Papers 2204.00883, arXiv.org.
    13. Susan Athey & Guido W. Imbens, 2019. "Machine Learning Methods That Economists Should Know About," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 685-725, August.
    14. Clements, A.E. & Hurn, A.S. & Li, Z., 2016. "Strategic bidding and rebidding in electricity markets," Energy Economics, Elsevier, vol. 59(C), pages 24-36.
    15. Brown, David P. & Eckert, Andrew & Shaffer, Blake, 2023. "Evaluating the impact of divestitures on competition: Evidence from Alberta’s wholesale electricity market," International Journal of Industrial Organization, Elsevier, vol. 89(C).
    16. Paige Weber & Matt Woerman, 2024. "Intermittency or Uncertainty? Impacts of Renewable Energy in Electricity Markets," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 11(6), pages 1351-1385.
    17. Hirth, Lion & Schlecht, Ingmar, 2020. "Market-Based Redispatch in Zonal Electricity Markets: The Preconditions for and Consequence of Inc-Dec Gaming," EconStor Preprints 194292, ZBW - Leibniz Information Centre for Economics, revised 2020.
    18. Ricardo P. Masini & Marcelo C. Medeiros & Eduardo F. Mendes, 2023. "Machine learning advances for time series forecasting," Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 76-111, February.
    19. David P. Brown & Daniel O. Cajueiro & Andrew Eckert & Douglas Silveira, 2023. "Information and Transparency: Using Machine Learning to Detect Communication," Working Papers 2023-06, University of Alberta, Department of Economics.
    20. Nils-Henrik M. von der Fehr, 2013. "Transparency in Electricity Markets," Economics of Energy & Environmental Policy, International Association for Energy Economics, vol. 0(Number 2).
    21. Derek W. Bunn and Stefan O.E. Kermer, 2021. "Statistical Arbitrage and Information Flow in an Electricity Balancing Market," The Energy Journal, International Association for Energy Economics, vol. 0(Number 5).
    22. Joseph E. Harrington Jr. & Andrzej Skrzypacz, 2007. "Collusion under monitoring of sales," RAND Journal of Economics, RAND Corporation, vol. 38(2), pages 314-331, June.
    23. Hannes Wallimann & David Imhof & Martin Huber, 2023. "A Machine Learning Approach for Flagging Incomplete Bid-Rigging Cartels," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1669-1720, December.
    24. Derek W. Bunn & Stefan O.E. Kermer, 2021. "Statistical Arbitrage and Information Flow in an Electricity Balancing Market," The Energy Journal, , vol. 42(5), pages 19-40, September.
    25. Silveira, Douglas & de Moraes, Lucas B. & Fiuza, Eduardo P.S. & Cajueiro, Daniel O., 2023. "Who are you? Cartel detection using unlabeled data," International Journal of Industrial Organization, Elsevier, vol. 88(C).
    26. Christie, William G. & Schultz, Paul H., 1999. "The initiation and withdrawal of odd-eighth quotes among Nasdaq stocks: an empirical analysis," Journal of Financial Economics, Elsevier, vol. 52(3), pages 409-442, June.
    27. Syntetos, Aris A. & Boylan, John E., 2005. "The accuracy of intermittent demand estimates," International Journal of Forecasting, Elsevier, vol. 21(2), pages 303-314.
    28. David P. Byrne & Nicolas de Roos, 2019. "Learning to Coordinate: A Study in Retail Gasoline," American Economic Review, American Economic Association, vol. 109(2), pages 591-619, February.
    29. Pär Holmberg & Thomas Tangerås, 2023. "A Survey of Capacity Mechanisms: Lessons for the Swedish Electricity Market," The Energy Journal, , vol. 44(6), pages 275-304, November.
    30. Rosa Abrantes-Metz & Sofia Villas-Boas & George Judge, 2011. "Tracking the Libor rate," Applied Economics Letters, Taylor & Francis Journals, vol. 18(10), pages 893-899.
    31. David P. Brown & Andrew Eckert & Douglas Silveira, 2023. "Strategic interaction between wholesale and ancillary service markets," Competition and Regulation in Network Industries, , vol. 24(4), pages 174-198, December.
    32. Marcelo C. Medeiros & Gabriel F. R. Vasconcelos & Álvaro Veiga & Eduardo Zilberman, 2021. "Forecasting Inflation in a Data-Rich Environment: The Benefits of Machine Learning Methods," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(1), pages 98-119, January.
    33. David P. Brown & Andrew Eckert & James Lin, 2018. "Information and transparency in wholesale electricity markets: evidence from Alberta," Journal of Regulatory Economics, Springer, vol. 54(3), pages 292-330, December.
    34. Katarzyna Maciejowska & Bartosz Uniejewski & Rafa{l} Weron, 2022. "Forecasting Electricity Prices," Papers 2204.11735, arXiv.org.
    35. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
    36. Matthew S. Lewis, 2015. "Odd Prices at Retail Gasoline Stations: Focal Point Pricing and Tacit Collusion," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 24(3), pages 664-685, September.
    37. Doumpos, Michalis & Zopounidis, Constantin & Gounopoulos, Dimitrios & Platanakis, Emmanouil & Zhang, Wenke, 2023. "Operational research and artificial intelligence methods in banking," European Journal of Operational Research, Elsevier, vol. 306(1), pages 1-16.
    38. Pär Holmberg & Frank A. Wolak, 2018. "Comparing auction designs where suppliers have uncertain costs and uncertain pivotal status," RAND Journal of Economics, RAND Corporation, vol. 49(4), pages 995-1027, December.
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    JEL classification:

    • D43 - Microeconomics - - Market Structure, Pricing, and Design - - - Oligopoly and Other Forms of Market Imperfection
    • L13 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Oligopoly and Other Imperfect Markets
    • L50 - Industrial Organization - - Regulation and Industrial Policy - - - General
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General

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