论文标题
独立做市商之间的合作
Cooperation between Independent Market Makers
论文作者
论文摘要
随着金融市场的数字化，经销商越来越多地通过算法处理营销活动。最近的反托拉斯文献引起了人们对人工智能造成的勾结的关注。本文通过独立的Q学习研究了做市商之间合作的可能性。具有库存风险的市场制造被制定为重复的通用游戏。在Stag-Hunt类型的回报下，我们发现做市商可以在没有沟通的情况下学习合作策略。通常，即使最低差异是唯一的NASH平衡，高点也可能具有最大的概率。此外，将更多的代理商引入游戏并不一定会消除竞争性差异的存在。
With the digitalization of the financial market, dealers are increasingly handling market-making activities by algorithms. Recent antitrust literature raises concerns on collusion caused by artificial intelligence. This paper studies the possibility of cooperation between market makers via independent Q-learning. Market making with inventory risk is formulated as a repeated general-sum game. Under a stag-hunt type payoff, we find that market makers can learn cooperative strategies without communication. In general, high spreads can have the largest probability even when the lowest spread is the unique Nash equilibrium. Moreover, introducing more agents into the game does not necessarily eliminate the presence of supra-competitive spreads.
