Asset Price Dynamics with Heterogeneous Beliefs and Local Network Interactions

Abstract

In this paper we investigate the effects of network topologies on asset price dynamics. We introduce network communications into a simple asset pricing model with heterogeneous beliefs. The agents may switch between several belief types according to their performance. The performance information is available to the agents only locally through their own experience and the experience of other agents directly connected to them. We model the communications with four commonly considered network topologies: a fully connected network, a regular lattice, a small world, and a random graph. The results show that the network topologies influences asset price dynamics in terms of the regions of stability, amplitudes of fluctuations and statistical properties.

Understanding How Short‐Termism and a Dynamic Investor Network Affects Investor Returns: An Agent‐Based Perspective

“…Of most significance is that the investors with the highest connectedness will earn the largest profits from trading more aggressively, and the market's price volatility will be highest in an environment where the investor network exhibits an intermediate level of connectedness and lowest in markets with higher or lower levels of connectedness. The latter suggests that a scale-free topology will exhibit the highest volatility, a theory confirmed in various agent-based models (ABMs) (see [22,27,28]).…”

“…This paper combined research from a variety of fields including agent-based artificial stock markets ( [13,22,37]), behavioral finance ( [11,49,50]), evolving social networks ( [31,51]), and empirical research into investor networks ( [25,26,52]) and financial markets ( [17,53]). By successfully combining these fields the paper delivers meaningful insights into the dynamics responsible for enhanced volatility in financial markets, divergent wealth accumulation by investors, and the detrimental effects of short-term behavior.…”

“…Allowing investors to interact with and receive information from other investors enables them to form an information network. The critical consequences of this process are that investor networks and financial networks in general have been found to be capable of affecting the behavior of the market ( [22][23][24]), and they explain trading volumes and the performance of investors ( [25,26]). Critically, this analysis has not been, nor can it be, considered in the models underwritten by a representative agent approach.…”

“…Notably, the original models explained a great deal without the use of networks [34]. Despite this, the inclusion of networks within this framework is becoming an established field of research because by introducing structural heterogeneity-which manifests itself as a network between investors [34]-new dynamics regarding the stylized behavior of financial markets have been uncovered (recent examples include [22,28,[35][36][37]). The common theme of these network models is that they utilize exogenously imposed, static, stylized networks.…”

Divergent Behavior in Markets with Idiosyncratic Private Information

“…The resulting competitive or even superior market-based information, achieved without imposition of cost on the private fundamental information, is, to my knowledge, unique to the developed model. Market-based trading strategies, particularly low cost trend-following rules, introduce instability in the dynamic financial market system in Brock and LeBaron (1996), Brock and Hommes (1998), De Grauwe and Grimaldi (2005), Giardina and Bouchaud (2003), Goldbaum (2003), Lux (1998), and Panchenko, Gerasymchuk and Pavlov (2013). In contrast, I present a model in which market-based information is capable of generating profits while improving market efficiency.…”