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Screen for collusive behavior: A machine learning approach

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  • Bantle, Melissa

Abstract

The paper uses a machine learning technique to build up a screen for collusive behavior. Such tools can be applied by competition authorities but also by companies to screen the behavior of their suppliers. The method is applied to the German retail gasoline market to detect anomalous behavior in the price setting of the filling stations. Therefore, the algorithm identifies anomalies in the data-generating process. The results show that various anomalies can be detected with this method. These anomalies in the price setting behavior are then discussed with respect to their implications for the competitiveness of the market.

Suggested Citation

  • Bantle, Melissa, 2024. "Screen for collusive behavior: A machine learning approach," Hohenheim Discussion Papers in Business, Economics and Social Sciences 01-2024, University of Hohenheim, Faculty of Business, Economics and Social Sciences.
    • Handle: RePEc:zbw:hohdps:285380
    as

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    References listed on IDEAS

    as
    1. Carsten J. Crede, 2019. "A Structural Break Cartel Screen for Dating and Detecting Collusion," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 54(3), pages 543-574, May.
    2. Abrantes-Metz, Rosa M. & Froeb, Luke M. & Geweke, John & Taylor, Christopher T., 2006. "A variance screen for collusion," International Journal of Industrial Organization, Elsevier, vol. 24(3), pages 467-486, May.
    3. Stephanie Assad & Robert Clark & Daniel Ershov & Lei Xu, 2020. "Algorithmic Pricing and Competition: Empirical Evidence from the German Retail Gasoline Market," CESifo Working Paper Series 8521, CESifo.
    4. 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.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Machine Learning; Cartel Screens; Fuel Retail Market;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • K21 - Law and Economics - - Regulation and Business Law - - - Antitrust Law
    • L44 - Industrial Organization - - Antitrust Issues and Policies - - - Antitrust Policy and Public Enterprise, Nonprofit Institutions, and Professional Organizations

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