Emergence of Scale-Free Networks in Markets

Abstract

Financial markets are complex systems, all the information scattered around the market is fairly and dynamically reflected in the current prices. However, it is difficult to understand the dynamics of markets merely by traditional analyzing methods. We here propose a new concept inspired by complex networks to study the trading behavior and the dynamics of markets. A web-based platform for prediction market which trades the political futures contracts is built to monitor the trading behavior among the human players. Two experiments are conducted on this platform in parallel for 30 days. From the accumulated transaction data, we reconstruct the so-called cash-flow networks. By examining the degree distributions of these networks, we observe that the network structure is scale-free with a power-law exponent of 1.15±0.07, which means that there must be a non-trivial mechanism governing the network growth in our markets. Through carrying out a post-simulation modelling by a continuous double auction market with "zero-intelligence" traders, we demonstrate that such a simple model is capable of generating scale-free networks. We thus suggest that the scale-free nature of the cash-flow networks should rely on the institutional design and the structure of markets rather than on the traders' strategies.

Experimental evidence for the interplay between individual wealth and transaction network

“…In previous studies [22,26], we demonstrated that the transaction networks resulting from two parallel market experiments on our platform possess scale-free, hierarchical and disassortative structure. However, due to the low statistics in earlier experiments, we can not make solid conclusions for neither the dynamics of network growth nor the relation between individual wealth and network structure.…”

Herding or wisdom of the crowd? Controlling efficiency in a partially rational financial market

“…We consider an artificial financial market composed of N = 100 agents, whose pattern of interactions is captured by a Barabasi-Albert scale-free network [36] with average degree 4. A scalefree network topology has been selected as it is typically observed both in financial networks and in other social or biological contexts [37][38][39][40]. In agreement with past works, the fundamental price p f (t) is taken as a realization of a geometric Wiener process [30,31].…”

Trading networks, abnormal motifs and stock manipulation

“…Wang et al (2008) study the network topology of a prediction market which is an experimental futures exchange. Tseng et al (2009) propose an agent-based model with "zero-intelligence" traders under the continuous double auction market mechanism to explain the power-law degree distribution of the trading network. Tseng et al (2010a) investigate the trading network of a prediction market to study the dynamics of wealth accumulation.…”