Profitability, efficiency, and inequality in double auction markets with snipers

This research builds upon two early efforts to explore robot trading strategies within the double auction market: (1) Gode and Sunder's Zero Intelligence robots, a simple, loss-avoiding, random, persistent, liquidity-providing strategy that produced, in double auction markets, high efficiency of allocation and market price convergence (within-period) towards the predictions of competitive theory; and (2) an entirely parasitic version of Todd Kaplan's Snipers, a simple, loss-avoiding, deterministic, liquidity-removing strategy. The original version of Kaplan's Snipers achieved the highest profit in an early tournament (the Santa Fe Double Auction Tournament) in part by accepting others’ orders when there was an excellent price, a low bid-ask spread, or time was running out. As we increase the proportion of snipers in a market, we find that sniping is not generally superior to the ZI strategy and that the snipers’ parasitic and end-of-period behaviors eventually cause extreme price variance and divergence from competitive equilibrium, lower market efficiencies, and rising Gini coefficients of inequality. Our results contrast with earlier claims by Gode and Sunder and others that double auction market efficiency and convergence to competitive equilibria are market properties relatively insensitive to agent strategies. Instead, we find a need to consider agent strategy in explaining how our market outcomes differ from those previously obtained either by Gode and Sunder or by Kaplan's successful tournament entry. Specifically, the interaction of parasitic sniper strategies creates a trading constraint: snipers will never trade with each other. These strategy-induced trading constraints force the standard market efficiency metric lower as the sniper population rises.