Efficiency of continuous double auctions under individual evolutionary learning with full or limited information

Abstract

In this paper we explore how specific aspects of market transparency and agents' behavior affect the efficiency of the market outcome. In particular, we are interested whether learning behavior with and without information about actions of other participants improves market efficiency. We consider a simple market for a homogeneous good populated by buyers and sellers. The valuations of the buyers and the costs of the sellers are given exogenously. Agents are involved in consecutive trading sessions, which are organized as a continuous double auction with order book. Using Individual Evolutionary Learning agents submit price bids and offers, trying to learn the most profitable strategy by looking at their realized and counterfactual or "foregone" payoffs. We find that learning outcomes heavily depend on information treatments. Under full information about actions of others, agents' orders tend to be similar, while under limited information agents tend to submit their valuations/costs. This behavioral outcome results in higher price volatility for the latter treatment. We also find that learning improves allocative efficiency when compared to outcomes Zero-Intelligent traders.

Price Dynamics and Market Volatility: Behavioral Heterogeneity under Switching Trading Strategies on Artificial Financial Market

“…An ARCH effect exists on the simulated price returns that are close to the ARCH effects in real returns. These stylized facts are influenced by the nature of the agents' behavior, which reinforces the preliminary results obtained by Anufriev and Panchenko (2013). These findings corroborate similar results of prior research conducted by other authors on global markets (Cont, 2001;LeBaron, 1999;Lux, 2009;Harrison et al, 2010, etc).…”

Learning and Information Dissemination in Limit Order Markets

“…They show that, when the information is long-lived, the artificial neural network learning traders can learn most of the private information from market data. See also the order driven behavior model of LiCalzi and Pellizzari (2003), models on the interaction of heterogeneous behavioral and market structure in Anufriev and Panchenko (2009), and on market efficiency under evolutionary learning with limited or full information in Anufriev, Arifovich, Ledyard and Panchenko (2013). These heterogeneous agent models show a great potential to examine the impact of the decision-making of uninformed traders on limit order markets.…”

Information Effect in Taxi Service Double Auction with Opportunity Cost: An Experimental Analysis

“…Without such information, however, bidders tend to submit orders at prices that are closer to their own valuations/costings, causing higher price volatility. In addition, Anufriev et al (2011) [23] found that market efficiency was similar with or without the information. Cao, Hansch and Wang (2008) [24] studied the effect of information on bidding strategies using data from the Australian Stock Exchange.…”

“…Anufriev et al (2011) [23] investigated the effect of information on market efficiency. With full information about the action of others, bidders tend to submit orders at a similar price to the previously observed transaction price.…”