Bandit Market Makers
Nicolas Della Penna, Mark D. Reid

TL;DR
This paper presents a modular market making framework that integrates cost-function based automated market makers with bandit algorithms, providing distribution-free profit guarantees and practical simulation insights.
Contribution
It introduces a novel combination of market making and bandit algorithms, offering worst-case profit guarantees and computational efficiency.
Findings
Distribution-free regret guarantees for profits.
Bounded worst-case losses maintained.
Simulation results demonstrate practical effectiveness.
Abstract
We introduce a modular framework for market making. It combines cost-function based automated market makers with bandit algorithms. We obtain worst-case profits guarantee's relative to the best in hindsight within a class of natural "overround" cost functions . This combination allow us to have distribution-free guarantees on the regret of profits while preserving the bounded worst-case losses and computational tractability over combinatorial spaces of the cost function based approach. We present simulation results to better understand the practical behaviour of market makers from the framework.
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Taxonomy
TopicsAdvanced Bandit Algorithms Research · Auction Theory and Applications · Sports Analytics and Performance
