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Machine learning, knowledge risk, and principal-agent problems in automated trading

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  • Borch, Christian

Abstract

Present-day securities trading is dominated by fully automated algorithms. These algorithmic systems are characterized by particular forms of knowledge risk (adverse effects relating to the use or absence of certain forms of knowledge) and principal-agent problems (goal conflicts and information asymmetries arising from the delegation of decision-making authority). Where automated trading systems used to be based on human-defined rules, increasingly, machine-learning (ML) techniques are being adopted to produce machine-generated strategies. Drawing on 213 interviews with market participants involved in automated trading, this study compares the forms of knowledge risk and principal-agent relations characterizing both human-defined and ML-based automated trading systems. It demonstrates that certain forms of ML-based automated trading lead to a change in knowledge risks, particularly concerning dramatically changing market settings, and that they are characterized by a lack of insight into how and why trading rules are being produced by the ML systems. This not only intensifies but also reconfigures principal-agent problems in financial markets.

Suggested Citation

  • Borch, Christian, 2022. "Machine learning, knowledge risk, and principal-agent problems in automated trading," Technology in Society, Elsevier, vol. 68(C).
    • Handle: RePEc:eee:teinso:v:68:y:2022:i:c:s0160791x21003274
    DOI: 10.1016/j.techsoc.2021.101852
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    References listed on IDEAS

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    Cited by:

    1. Paraná, Edemilson, 2024. "AI as financial infrastructure?," SocArXiv ub92z, Center for Open Science.
    2. Lin Meng & George Xianzhi Yuan & Huiqi Wang, 2025. "Dynamic Incentive Strategies for Principal‐Agent Coopetition: A Stochastic Approach to Managing Risks and Performance," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 46(7), pages 3976-3997, October.
    3. Xu, Juan & Chen, Yu & Yang, Nan & Shao, Shuai, 2025. "The impact of artificial intelligence on the energy transition: evidence from Chinese cities," World Development, Elsevier, vol. 195(C).
    4. Qiang Wang & Tingting Sun & Rongrong Li, 2025. "Does artificial intelligence promote green innovation? An assessment based on direct, indirect, spillover, and heterogeneity effects," Energy & Environment, , vol. 36(2), pages 1005-1037, March.
    5. repec:osf:socarx:ub92z_v1 is not listed on IDEAS

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