Social Networks and Asset Price Dynamics

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@Article{Yeh:2015:ieeeTEC,
  author =       "Chia-Hsuan Yeh and Chun-Yi Yang",
  title =        "Social Networks and Asset Price Dynamics",
  journal =      "IEEE Transactions on Evolutionary Computation",
  year =         "2015",
  volume =       "19",
  number =       "3",
  pages =        "387--399",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming, Social
                 network, artificial stock market, agentbased
                 modelling",
  ISSN =         "1089-778X",
  DOI =          "doi:10.1109/TEVC.2014.2322121",
  size =         "15 pages",
  abstract =     "In this paper, we investigate how behavioural
                 contagion in terms of mimetic strategy learning within
                 a social network would affect the asset price dynamics.
                 The characteristics of this paper are as follows.
                 First, traders are characterised by bounded rationality
                 and their adaptive learning behaviour is represented by
                 the genetic programming algorithm. The use of the
                 genetic programming algorithm allows traders to freely
                 form forecasting strategies with great potential of
                 variety in functional forms, which are not
                 pre-determined but may be fundamental-like or
                 technical-like or any mix of these two broad
                 categories, as they need to adapt to the time-varying
                 market environment. The evolutionary nature of the
                 genetic programming algorithm has its merit for
                 modeling mimetic behavior in the context of information
                 transmission in that, other than making duplicates of
                 an entire trading rule as if a mind reading technique
                 exists, strategy imitation could take place down to the
                 level of building blocks that genetic operators work
                 out or pieces of information that constitute a strategy
                 and are more ready to be transmitted via word-of-mouth
                 communication, which is more intuitive compared to the
                 existing literature. Second, the traders are spatially
                 heterogeneous based on their positions in social
                 networks. Mimetic learning thus takes part in local
                 interactions among traders that are directly tied with
                 each other when they evolve their trading strategies
                 according to the relative performance of their own and
                 their neighbours'. Therefore, specifically, we aim to
                 analyse the effect of network topologies, i.e. a
                 regular lattice, a small world, a random network, a
                 fully connected network, and a preferential attachment
                 network, on market dynamics regarding price distortion,
                 volatility, and trading volume, as information diffuses
                 across these different social network structures.",
  notes =        "C.-H. Yeh is with the Department of Information
                 Management, Yuan Ze University, Chungli, Taoyuan 320,
                 Taiwan.

                 Also known as \cite{6826570}",
}

Genetic Programming entries for Chia Hsuan Yeh Chun-Yi Yang

Citations