Network structure and N-dependence in agent-based herding models

Alfarano, Simone; Milaković, Mishael

doi:10.1016/j.jedc.2008.05.003

Cited by 85 publications

(108 citation statements)

References 40 publications

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“…It should be noted that fixing the other parameters we find the pairwise (L = 2) case to show similar behavior as the general L case (we have only studied L < 20). A similar conclusion was found using different distributions of a k , which seems to indicate a robustness of contagion effects on those two parameters somewhat similar to what was reported in [11].…”

Section: Resultssupporting

confidence: 89%

“…A different view of interaction between market participants was presented in an "ant recruitment" model of herding/epidemics in [10]. In similar vein models of social opinion dynamics and agent based models of investor sentiments was presented in [11][12][13][14][15][16]. It should be noted however that only the work of [12,14] keep the price and sentiments as two distinguished variables which we find important since sentiment (as well as price) can be obtained empirically.…”

Section: Introductionmentioning

confidence: 97%

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Communication impacting financial markets

Andersen¹,

Vrontos²,

Δελλαπόρτας³

2014

EPL

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Since the attribution of the Nobel prize in 2002 to Kahneman for prospect theory, behavioral finance has become an increasingly important subfield of finance. However the main parts of behavioral finance, prospect theory included, understand financial markets through individual investment behavior. Behavioral finance thereby ignores any interaction between participants.We introduce a socio-financial[1] model that studies the impact of communication on the pricing in financial markets. Considering the simplest possible case where each market participant has either a positive (bullish) or negative (bearish) sentiment with respect to the market, we model the evolution of the sentiment in the population due to communication in subgroups of different sizes. Nonlinear feedback effects between the market performance and changes in sentiments are taking into account by assuming that the market performance is dependent on changes in sentiments (e.g. a large sudden positive change in bullishness would lead to more buying). The market performance in turn has an impact on the sentiment through the transition probabilities to change an opinion in a group of a given size. The idea is that if for example the market has observed a recent downturn, it will be easier for even a bearish minority to convince a bullish majority to change opinion compared to the case where the meeting takes place in a bullish upturn of the market.Within the framework of our proposed model, financial markets stylized facts such as volatility clustering and extreme events may be perceived as arising due to abrupt sentiment changes via ongoing communication of the market participants. The model introduces a new volatility measure which is apt of capturing volatility clustering and from maximum likelihood analysis we are able to apply the model to real data and give additional long term insight into where a market is heading.

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Section: Resultssupporting

confidence: 89%

Section: Introductionmentioning

confidence: 97%

Communication impacting financial markets

Andersen¹,

Vrontos²,

Δελλαπόρτας³

2014

EPL

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“…The formula was tested for relatively small networks of the types random, 2-D regular lattice, Barabasi-Albert scale-free and small world. Similar results were obtained in the context of herding behavior of economic agents [24,42]. The evolution equation for the macroscopic states can be obtained from equations (6) if we define r 1 (m, t) and r 0 (m, t) as the probabilities of having m peripheral nodes in state 1 at time t with the central node in state 1 and 0 respectively.…”

Section: Fully Connected Networksupporting

confidence: 58%

Binary dynamics on star networks under external perturbations

2015

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We study a binary dynamical process that is a representation of the voter model with two candidates and opinion makers. The voters are represented by nodes of a network of social contacts with internal states labeled 0 or 1 and nodes that are connected can influence each other. The network is also perturbed by opinion makers, a set of external nodes whose states are frozen in 0 or 1 and that can influence all nodes of the network. The quantity of interest is the probability of finding m nodes in state 1 at time t. Here we study this process on star networks, which are simple representations of hubs found in complex systems, and compare the results with those obtained for networks that are fully connected. In both cases a transition from disordered to ordered equilibrium states is observed as the number of external nodes becomes small. For fully connected networks the probability distribution becomes uniform at the critical point. For star networks, on the other hand, we show that the equilibrium distribution splits in two peaks, reflecting the two possible states of the central node. We obtain approximate analytical solutions for the equilibrium distribution that clarify the role of the central node in the process. We show that the network topology also affects the time scale of oscillations in single realizations of the dynamics, which are much faster for the star network. Finally, extending the analysis to two stars we compare our results with simulations in simple scale-free networks.

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“…Therefore it seems reasonable to assume that these directors, in spite of their small number relative to the overall network size, exert a disproportionate degree of influence in the network. The hierarchical nature of the director network is also important from the viewpoint of probabilistic herding models, such as the one studied by Alfarano and Milaković (2008), because the hierarchical structure of the network can generate system-wide conformity in agents' opinions irrespective of the size of the network, including the possibility that the social interaction of core agents leads to the propagation of "animal spirits" across an entire system that is several orders of magnitude larger than the size of the core.…”

Section: Resultsmentioning

confidence: 99%

“…Mintz and Schwartz (1981) have argued that the degree of interest-group formation can be assessed by some core of board interlocks. Moreover, Alfarano and Milaković (2008) have argued theoretically that a network core is crucial for the propagation of opinion dynamics in a generic probabilistic herding model with a large number of agents, and several authors have suggested procedures to classify or identify a core of key players in complex networks (for a more recent take on the classic concept of coreperiphery structures in networks see, e.g., Borgatti, 2006;Borgatti and Everett, 1999;Holme, 2005).…”

Section: Introductionmentioning

confidence: 99%

The small core of the German corporate board network

Milaković

Alfarano

Lux

2010

Comput Math Organ Theory

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Abstract:We consider the current bipartite graph of German corporate boards and identify a small core of directors who are highly central in the entire network while being densely connected among themselves. To identify the core, we compare the actual number of board memberships to a random benchmark, focusing on deviations from the benchmark that span several orders of magnitude. It seems that the board appointment decisions of largely capitalized companies are the driving force behind the existence of a core in Germany's board and director network. Conditional on being a board member, it is very improbable to obtain a second membership, but multiple board membership becomes increasingly likely once this initial barrier is overcome. We also present a simple model that describes board appointment decisions as a trade-off between social capital and monitoring ability.Keywords: Board and director interlocks, network core, network formation, market capitalization JEL classification: D85, L20, M14, M51 AbstractWe consider the current bipartite graph of German corporate boards and identify a small core of directors who are highly central in the entire network while being densely connected among themselves. To identify the core, we compare the actual number of board memberships to a random benchmark, focusing on deviations from the benchmark that span several orders of magnitude. It seems that the board appointment decisions of largely capitalized companies are the driving force behind the existence of a core in Germany's board and director network. Conditional on being a board member, it is very improbable to obtain a second membership, but multiple board membership becomes increasingly likely once this initial barrier is overcome. We also present a simple model that describes board appointment decisions as a trade-off between social capital and monitoring ability.

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Network structure and N-dependence in agent-based herding models

Cited by 85 publications

References 40 publications

Communication impacting financial markets

Communication impacting financial markets

Binary dynamics on star networks under external perturbations

The small core of the German corporate board network

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