Zero-Intelligence vs. Human Agents: An Experimental Analysis of the Efficiency of Double Auctions and Over-the-Counter Markets of Varying Sizes

We study two well-known electronic markets: an over-the-counter (OTC) market, in which each agent looks for the best counterpart through bilateral negotiations, and a double auction (DA) market, in which traders post their quotes publicly. We focus on the DA-OTC efficiency gap and show how it varies with different market sizes (10, 20, 40, and 80 traders). We compare experimental results from a sample of 6,400 undergraduate students in Economics and Management with zero-intelligent (ZI) agent-based simulations. Simulations with ZI traders show that the traded quantity (with respect to the e cient one) increases with market size under both DA and OTC. Experimental results with human traders confrm the same tendency under DA, while the share of periods in which the traded quantity is higher (lower) than the efficient one decreases (increases) with market size under OTC, ultimately leading to a DA-OTC efficiency gap increasing with market size. We rationalize these results by putting forward a novel game-theoretical model of OTC market as a repeated bargaining procedure under incomplete information on buyers' valuations and sellers' costs, showing how efficiency decreases slightly with size due to two counteracting e ects: acceptance rates in earlier periods decrease with size, and earlier offers increase, but not always enough to compensate for the decreasein acceptance rates.