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New algorithms leverage order book data for online market making

Researchers have developed new algorithms for online market making that leverage action-dependent feedback from order books. These algorithms achieve improved regret bounds compared to standard bandit feedback models, even with limited information. The work quantifies the value of observing order book dynamics in financial trading scenarios. AI

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IMPACT Introduces novel algorithmic approaches for financial trading that could enhance AI-driven market-making strategies.

RANK_REASON Academic paper detailing new algorithms and theoretical results. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv stat.ML →

COVERAGE [1]

  1. arXiv stat.ML TIER_1 · Davide Maran, Marcello Restelli ·

    Online Market Making and the Value of Observing the Order Book

    arXiv:2605.19584v1 Announce Type: cross Abstract: We study an online market-making problem in which a learner sequentially posts bid and ask prices for a single asset while interacting with traders holding private valuations. Unlike existing online learning formulations that assu…