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New research details market-making strategy with optimal regret bounds

By PulseAugur Editorial · [1 sources] ·

A new research paper published on arXiv details a market-making strategy designed to minimize regret in adversarial trading environments. The proposed policy operates on two markets, a primary for order placement and a secondary for hedging, and aims to achieve logarithmic regret against static comparators and square-root regret against dynamic comparators. The policy is described as simple, efficient, and parameterless, with extensions for simultaneous bids and asks, and natural assumptions on price processes can lead to improved guarantees. AI

IMPACT This research could inform the development of more robust and efficient algorithmic trading systems, potentially impacting high-frequency trading and financial AI applications.

RANK_REASON Academic paper published on arXiv detailing a new theoretical approach. [lever_c_demoted from research: ic=1 ai=0.4]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New research details market-making strategy with optimal regret bounds

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Academic paper published on arXiv detailing a new theoretical approach. [lever_c_demoted from research: ic=1 ai=0.4]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Nicol\`o Cesa-Bianchi, Tommaso Cesari, Roberto Colomboni, Luigi Foscari, Vinayak Pathak ·

    Regret Bounds for Market Making with Persistent Orders

    arXiv:2611.06159v1 Announce Type: cross Abstract: We study adversarial market making with orders that remain active until execution. The maker operates on two markets: a primary market where orders are made and a secondary market where the resulting positions are hedged. In each …

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