| Description |
Algorithmic participation in financial markets has been a growing phenomenon since the early 1990s. This paper uses experimental data and simulations to analyze the interaction between humans and trading algorithms. Interactions between humans and algorithms are observed through laboratory sessions and simulations which can demonstrate the impact of algorithms in financial markets. The study introduces an opportunistic algorithm in laboratory marketplaces to analyze the impact of profit seeking agents. Through simulations, the effects of an opportunistic algorithm, in markets where humans can trade manually or with algorithmic assistance, using a mean-variance optimizer-trading agent, are analyzed. We find that the trend-seeking algorithm can trade profitably in markets where mean-variance agents drive prices towards equilibrium. The momentum agent can generate profits independently as well as when deployed concurrently with a mean-variance agent. Experimental results demonstrate that mean-variance agents, when employed by laboratory participants, drive prices towards equilibrium, and through simulations we find that a momentum agent can be employed to increase the performance of the participants. |
