Behavior of Trading Automata in a Computerized Double Auction Market
John Rust, John H. Miller, and Richard Palmer
Key Words:
Double Auction Tournament, Trading Strategy, Learning
This paper reports the results of a series of tournaments held at the Santa Fe Institute beginning in March 1990 in which computer programs played the roles of buyers and sellers in a synchronized double auction market. We show that despite the decentralized nature of the trading process and traders’ incomplete information about supply and demand, transaction-price trajectories for a heterogeneous collection of computer programs typically converged to the competitive equilibrium, resulting in allocations that were nearly 100% efficient. We also show that a very simple trading strategy is a highly effective and robust performer in these markets. A simple rule-of-thumb was able to outperform more complex algorithms that used statistically based predictions of future transaction prices, explicit optimizing principles, and sophisticated “learning algorithms.”