Bandit Market Makers
arXiv:1112.0076
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.
A previous version of this work appeared in the NIPS 2011 Workshop on Computational Social Science and the Wisdom of the Crowds