arXiv:2606.29018v1 Announce Type: cross Abstract: We show that net demand for liquidity by algo strategies is identifiable from its trade and price history alone, with no knowledge of its signal or optimization problem. An exact multi-period regret decomposition implies that the sign of this statistic classifies a linear strategy as a net liquidity consumer or provider, recovering the Kyle (1985) informed-trader/market-maker dichotomy from observables alone. Under an AR(1) cost process, the same statistic equals the product of strategy size and the squared Roll (1984) implied spread, making th
This research provides a real-time, observable method for classifying algorithmic trading strategies, leveraging advancements in computational finance and the increasing prevalence of AI in markets.
A strategic reader should care because this development offers enhanced transparency and risk management capabilities in complex financial markets dominated by algorithms.
The ability to identify net liquidity consumers or providers from trade data alone shifts the understanding of algorithmic market impact and potentially informs regulatory oversight.
- · Regulatory bodies
- · Quantitative analysts
- · Market data providers
- · Risk management platforms
- · Undisclosed predatory algorithms
- · High-frequency traders relying on opacity
- · Legacy market surveillance systems
Financial institutions can better assess the liquidity impact of their own and competitors' algorithmic trading strategies.
This could lead to new market regulations around algorithmic transparency and potentially influence market making incentives.
The increased transparency might foster more stable markets, but could also lead to more sophisticated adversarial algorithms attempting to obscure their liquidity impact.
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Read at arXiv cs.LG