Exploring assignment-adaptive (ASAD) trading agents in financial market experiments

Automated trading systems in the global financial markets are increasingly being deployed to do jobs previously done by skilled human traders: very often a human trader in the markets simply cannot tell whether the counter-party to a trade is another human, or a machine. Clearly, automated trading systems can easily be considered as “intelligent” software agents. In this paper we report on experiments with software trader agents running the well-known “AA” and “ZIP” strategies, often used as reference benchmarks in previously published studies; here we suggest disambiguated standard implementations of these algorithms. Then, using Exchange Portal (ExPo), an open-source financial exchange simulation platform designed for real-time behavioural economic experiments involving human traders and/or trader-agents, we explore the impact of introducing a new method for assignment adaptation in ZIP. Results show that markets containing only assignment adaptive (ASAD) agents equilibrate more quickly after market shocks than markets containing only “standard” ZIP agents. However, perhaps counterintuitively, in mixed heterogeneous populations of ASAD agents and ZIP agents, ZIP agents outperform ASAD agents. Evidence suggests that the behaviour of ASAD agents act as a new signal in the market that ZIP agents then use to beneficially alter their own behaviour, to the detriment of the ASAD agents themselves.