Dynamic Learning, Herding and Guru Effects in Networks

Physics of Particles and Nuclei Letters, 2008

The agent-based multileader model of the stock price dynamics on the directed evolving complex network is studied by direct simulation. The resulting stationary regime follows from the balance of extremal dynamics, adaptivity of the strategic variables, and reconnection rules. For given parametric combination, the network displays small-world phenomenon with high clustering coefficients and power-law node degree distribution. The fitness exploration by the mechanism of repeated random walk is used to violate dominance of centralized leadership. The simulation suggests that ultra slow dynamics of fitness implies explanation of the long-time volatility of the log-price returns.

Journal of Economic Dynamics and Control, 2009

JEL classification: CO2 D84 D85 G19

The clustering of agents in the market is a typical problem discussed by the new approaches to macroeconomic modelling, that describe macroscopic variables in terms of the behavior of a large collection of microeconomic entities. Clustering is often described by Ewens Sampling Formula (ESF), that admits a very nice interpretation in terms of rational vs herding behavior. Focusing on the evergreen problem of the size of …rms, we discuss the incompatibility between empirical data and ESF. An alternative model is suggested, inspired to Simon's approaches to the …rm size problem. It di¤ers from the Ewens model both in destruction and in creation. In particular the probability of herding is independent on the size of the herd. This very simple assumption destroys the exchangeability of the random partitions, and forbids an analytical solution. Simple computational simulations look to con…rm that actually the mean number of clusters of size i (the equilibrium distribution) follows the corresponding Yule distribution. Finally we introduce a Markov chain, that resembles the marginal dynamics of a cluster, which drives the cluster to the right-censored Yule distribution.

EPL (Europhysics Letters), 2013

We propose a Markov jump process with the three-state herding interaction. We see our approach as an agent-based model for the financial markets. Under certain assumptions this agent-based model can be related to the stochastic description exhibiting sophisticated statistical features. Along with power-law probability density function of the absolute returns we are able to reproduce the fractured power spectral density, which is observed in the high-frequency financial market data. Given example of consistent agentbased and stochastic modeling will provide background for the further developments in the research of complex social systems.

B E Journal of Theoretical Economics, 2008

We study a sequential trading financial market where there are gains from trade, that is, where informed traders have heterogeneous private values. We show that an informational cascade (i.e., a complete blockage of information) arises and prices fail to aggregate information dispersed among traders. During an informational cascade, all traders with the same preferences choose the same action, following the market (herding) or going against it (contrarianism). We also study financial contagion by extending our model to a two-asset economy. We show that informational cascades in one market can be generated by informational spillovers from the other. Such spillovers have pathological consequences, generating long-lasting misalignments between prices and fundamentals. . We are indebted to our advisor, Douglas Gale, for his invaluable guidance. We also owe a special debt to Marco LiCalzi for a very careful reading of the paper. We thank show that, when there there is multidimensional uncertainty (i.e., uncertainty not only on the direction of a shock to the asset fundamental, but also on the existence of the shock itself), herd behavior can arise even in their framework. Their definition of herding, however, is not the standard one in the literature (see footnote 24). Even with multidimensional uncertainty, informational cascades cannot arise in their study (see their Proposition 2 and their comments at page 733). See also the considerations of

2004

The behavioral origins of the stylized facts of financial returns have been addressed in a growing body of agent-based models of financial markets. While the traditional efficient market viewpoint explains all statistical properties of returns by similar features of the news arrival process, the more recent behavioral finance models explain them as imprints of universal patterns of interaction in these markets. In this paper we contribute to this literature by introducing a very simple agent-based model in which the ubiquitous stylized facts (fat tails, volatility clustering) are emergent properties of the interaction among traders. The simplicity of the model allows us to estimate the underlying parameters, since it is possible to derive a closed form solution for the distribution of returns. We show that the tail shape characterizing the fatness of the unconditional distribution of returns can be directly derived from some structural variables that govern the traders' interactions, namely the herding propensity and the autonomous switching tendency.

2021

2007

We derive microscopic foundations for a well-known probabilistic herding model in the agent-based finance literature. Lo and behold, the model is quite robust with respect to behavioral heterogeneity, yet structural heterogeneity, in the sense of an underlying network structure that describes the very feasibility of agent interaction, has a crucial and non-trivial impact on the macroscopic properties of the model.

Advances in Complex Systems, 2009

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 were 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.

arXiv: General Finance, 2019

Global supply networks in agriculture, manufacturing, and services are a defining feature of the modern world. The efficiency and the distribution of surpluses across different parts of these networks depend on choices of intermediaries. This paper conducts price formation experiments with human subjects located in large complex networks to develop a better understanding of the principles governing behavior. Our first finding is that prices are larger and that trade is significantly less efficient in small-world networks as compared to random networks. Our second finding is that location within a network is not an important determinant of pricing. An examination of the price dynamics suggests that traders on cheapest -- and hence active -- paths raise prices while those off these paths lower them. We construct an agent-based model (ABM) that embodies this rule of thumb. Simulations of this ABM yield macroscopic patterns consistent with the experimental findings. Finally, we extrapol...