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New method audits algorithmic trading strategies for liquidity impact

By PulseAugur Editorial · [2 sources] ·

A new paper introduces a method to audit algorithmic trading strategies by analyzing their trade and price history. This approach can identify whether a strategy is a net consumer or provider of liquidity, drawing parallels to the Kyle (1985) informed-trader/market-maker dichotomy. The method also serves as a proxy for illiquidity, particularly during market stress events like the COVID-19 pandemic and the 2022 rate shocks, and can quantify welfare losses from correlated strategies. AI

IMPACT Provides a new quantitative method for analyzing financial market dynamics, potentially impacting algorithmic trading and risk management.

RANK_REASON The cluster contains a single academic paper published on arXiv. [lever_c_demoted from research: ic=1 ai=0.4]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New method audits algorithmic trading strategies for liquidity impact

COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Irene Aldridge ·

    Liquidity-Based Audit of Algorithmic Trading Strategies

    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 …

  2. arXiv stat.ML TIER_1 English(EN) · Irene Aldridge ·

    Liquidity-Based Audit of Algorithmic Trading Strategies

    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 strateg…

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