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Social Sciences › Economics, Econometrics and Finance › Finance
AI-Powered Trading, Algorithmic Collusion, and Price Efficiency
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Conflicts of Interest
Identified Weaknesses
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Paper Summary
Paperzilla title
AI Algorithms Collude in Simulated Trading, But Can They Do It in the Real World?
This paper shows that AI-powered trading algorithms can learn to collude in simulated financial markets, leading to supra-competitive profits and reduced market efficiency. The study identifies two distinct algorithmic mechanisms underlying AI collusion: one based on price-trigger strategies, and the other driven by over-pruning bias in learning. The authors also show how different market parameters, such as noise trading risk and the presence of information-insensitive investors, can affect the emergence and type of AI collusion.
Possible Conflicts of Interest
None identified
Identified Weaknesses
Assumption of Perfect Knowledge
The model assumes that informed speculators have perfect knowledge of the fundamental value of the asset, which is unrealistic in real-world markets. This assumption simplifies the analysis but may not accurately reflect the dynamics of AI trading in practice.
Simplified Market Maker Objective
The market maker's objective function is a simplified representation of real-world market making practices. In practice, market makers consider a wider range of factors beyond inventory costs and pricing errors, such as order book dynamics and risk management.
Stylized Laboratory Setting
The simulation experiments are conducted in a stylized laboratory setting, which may not fully capture the complexity of real-world financial markets. Factors such as market microstructure, regulatory constraints, and the presence of other market participants are not fully incorporated in the simulations.
Rating Explanation
This paper presents a novel and insightful analysis of AI collusion in securities trading. The model and simulation experiments are well-designed and provide valuable insights into the potential mechanisms and consequences of AI collusion. However, the stylized nature of the model and the limitations of the simulation experiments warrant a slightly lower rating.
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File Information
Original Title:
AI-Powered Trading, Algorithmic Collusion, and Price Efficiency
File Name:
w34054.pdf
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File Size:
1.30 MB
Uploaded:
July 31, 2025 at 04:52 PM
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