A new research paper proposes a method to audit algorithmic trading strategies by analyzing their trade and price history. The study introduces a statistic that can identify whether a strategy is a net liquidity consumer or provider, drawing parallels to the Kyle (1985) informed-trader/market-maker dichotomy. This statistic also serves as a proxy for illiquidity, with potential applications in understanding welfare loss and fire-sale externalities, and has been calibrated using CRSP equity data from 2016-2025, including periods like the COVID-19 pandemic. AI
IMPACT This research offers a novel framework for analyzing algorithmic trading, potentially improving market stability and understanding of financial dynamics.
RANK_REASON The cluster contains a research paper published on arXiv detailing a new methodology for auditing algorithmic trading strategies. [lever_c_demoted from research: ic=2 ai=0.4]
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