Asset price dynamics with heterogeneous beliefs and local network interactions

Panchenko, Valentyn; Gerasymchuk, Sergiy; Pavlov, Oleg V.

doi:10.1016/j.jedc.2013.06.015

Cited by 37 publications

(28 citation statements)

References 61 publications

(60 reference statements)

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“…Also, researchers addressed the role of network theory and agents direct interactions of model such as the models of Panchenko et al [22], Yeh and Yang [23], Delellis et al [24,25], and Khashanah and Alsulaiman [1,26]. For more details of the ABM in market microstructure, readers may refer to [27][28][29][30].…”

Section: Complexitymentioning

confidence: 99%

Connectivity, Information Jumps, and Market Stability: An Agent-Based Approach

Khashanah

Alsulaiman

2017

Complexity

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We propose a metamodel to assess simulated market stability by introducing information connectivity in an agent-based network. The market is occupied by heterogeneous agents with different behaviors, strategies, and information connectivity. A jump-diffusion process simulating events that may occur in the market is introduced. Agents information awareness varies along with agents propensity to respond to the information jump and jump size. A jump reshuffles market positions based on agents risk preferences determined by behavior and strategy. We examine the effect of information awareness on the volatility index of the simulated market in a scale-free market network. The analysis is performed by developing five experiments wherein the first one corresponds to systemic information ignorance state. Three experiments examine the role of hubs, normal agents, and hermits in the network when intermediate combinations of agent types have information awareness. The fifth experiment corresponds to the systemic information awareness with all agents being informed. The results show that the simulated market is driven to instability in a similar manner to patterns observed in a crisis where all agents become homogeneous in information awareness of events. Hubs contribute to increased connectivity and act as amplifiers of good, bad, or inaccurate information or sentiment.

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Section: Complexitymentioning

confidence: 99%

Connectivity, Information Jumps, and Market Stability: An Agent-Based Approach

Khashanah

Alsulaiman

2017

Complexity

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“…Information linkages may also be external to the market. Panchenko et al (2013) examine this issue explicitly. They extend the model of Brock and Hommes (1998) to allow traders to adopt one of two strategies based on the choices and performance of a trader's social network.…”

Section: Related Literaturementioning

confidence: 99%

“…As described above, and in a similar manner to Panchenko et al (2013), we ground our work in the literature of market dynamics under heterogeneous beliefs. The dynamics of centralized exchanges with chartist and fundamentalist traders have been considered in detail.…”

Section: Related Literaturementioning

confidence: 99%

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Heterogeneous beliefs in over-the-counter markets

Kamps¹,

Ladley²,

Simaitis³

2014

Journal of Economic Dynamics and Control

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The behavior and stability of over-the-counter markets is of central concern to regulators. Little is known, however, about how the structure of these markets determine their properties. In this paper we consider an over-the-counter market populated by boundedly rational heterogeneous traders in which the structure is represented by a network. Stability is found to decrease as the market becomes less well connected, however, the configuration of connections has a significant effect. The presence of hubs, such as those found in scale free networks increases stability and decreases volatility whilst small-world short-cut links have the opposite effect. Volatility in the fundamental value increases market volatility, however, volatility in the riskless asset returns has an ambiguous effect.

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“…To the best of my knowledge, Panchenko et al (2013) (henceforth PGP13) are the only authors who conduct a full-fledged theoretical study of the effect of the information network on expectation formation in an asset pricing model. The authors use a HSM model to show that price oscillations are not tamed by the presence of the network.…”

Section: Introductionmentioning

confidence: 99%

Contrarian Behavior, Information Networks and Heterogeneous Expectations in an Asset Pricing Model

Makarewicz

2016

Comput Econ

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This paper studies the emergence of contrarian behavior in information networks in an asset pricing model. Financial traders coordinate on similar behavior, but have heterogeneous price expectations and are influenced by friends. According to a popular belief, they are prone to herding. However, in laboratory experiments subjects use contrarian strategies. Theoretical literature on learning in networks is scarce and cannot explain this conundrum (Panchenko et al. in J Econ Dyn Control 37(12):2623-2642, 2013). The paper follows Anufriev et al. (CeNDEF Working paper 15-07, 2015) and investigates an agent-based model, in which agents forecast price with a simple general heuristic: adaptive and trend extrapolation expectations, with an additional term of (dis-)trust towards their friends' mood. Agents independently use Genetic Algorithms to optimize the parameters of the heuristic. The paper considers friendship networks of symmetric (regular lattice, fully connected) and asymmetric architecture (random, rewired, star). The main finding is that the agents learn contrarian strategies, which amplifies market turn-overs and hence price oscillations. Nevertheless, agents learn similar behavior and their forecasts remain well coordinated. The model therefore offers a natural interpretation for the difference between the experimental stylized facts and market surveys.

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Asset price dynamics with heterogeneous beliefs and local network interactions

Cited by 37 publications

References 61 publications

Connectivity, Information Jumps, and Market Stability: An Agent-Based Approach

Connectivity, Information Jumps, and Market Stability: An Agent-Based Approach

Heterogeneous beliefs in over-the-counter markets

Contrarian Behavior, Information Networks and Heterogeneous Expectations in an Asset Pricing Model

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