Evolving Software Traders and Detecting Community Structure in Financial Markets

Created by W.Langdon from gp-bibliography.bib Revision:1.4202

  author =       "Todd D. Kaplan",
  title =        "Evolving Software Traders and Detecting Community
                 Structure in Financial Markets",
  school =       "Computer Science, The University of New Mexico",
  year =         "2011",
  address =      "Albuquerque, New Mexico, USA",
  month =        may,
  keywords =     "genetic algorithms, genetic programming, Staq, Applied
                 science, Biological science, Community structure,
                 Computer Science, Ecology, Finance, Financial ecology,
                 Simulation of financial market, Social Science,
                 Software traders, Study, Trophic species",
  URL =          "http://phdtree.org/pdf/25230493-evolving-software-traders-and-detecting-community-structure-in-financial-markets/",
  URL =          "https://www.cs.unm.edu/~forrest/dissertations/kaplan-unm-diss-final.pdf",
  size =         "210 pages",
  abstract =     "A trophic network, commonly referred to as a food web,
                 describes the feeding relationships between different
                 groups of species in an ecosystem. Ecologists construct
                 trophic networks to aid their understanding of
                 ecosystems. In the realm of financial markets, trophic
                 networks can serve an analogous role. Their use could
                 potentially illuminate underlying dynamics responsible
                 for commonly observed macro-level phenomena. For
                 example, one might hypothesize that periods of market
                 volatility occur after a keystone trader species
                 becomes inactive and the trophic network subsequently
                 restructures itself. The primary topic in this research
                 investigates whether it is possible to detect trophic
                 structure within real-world financial markets.
                 Secondarily, the efficacy of using genetic programming
                 to evolve software traders in a simulated stock market
                 (continuous double auction) is examined.

                 The research to follow is split into three parts. In
                 Part I, new tools for detecting community structure in
                 complex networks are developed. First, a two-phase
                 macro-strategy for community detection is introduced.
                 The approach is unique in that it can be used in
                 combination with any existing community detection
                 algorithm to provide high-yield, robust results.
                 Second,the resolution limit inherent to the community
                 structure measurement known as modularity is
                 illustrated experimentally. To overcome this
                 limitation, a fine-granularity community structure
                 measure called divisionality is developed. Third, a
                 dual-assortative measure (DAMM) of community structure
                 is established. DAMM extends the domain of networks
                 that can be analysed for community structure to include
                 those with negatively weighted edges.

                 Part II focuses on the evolution of software agents
                 that compete in an artificial financial market. The
                 evolutionary framework is based on a stack-based
                 language (Staq) that was developed for genetic
                 programming (GP). The genetic programs of two evolved
                 agents, each based on a different fitness function, are
                 examined. One of these evolved traders, known as clear
                 and hoist (CH), reveals a limitation of the simulated
                 market: a lack of fundamentalism. Two value-based
                 strategies are developed to address this shortcoming.
                 The effect of each strategy on the CH trader is
                 independently examined.

                 In Part III, the community structure tools developed in
                 Part I are used to detect trophic species in financial
                 market data. After introducing the trophic detection
                 algorithm, a methodology for assessing the significance
                 of detected structure is described. The efficacy of the
                 approach is demonstrated using simulated data. Finally,
                 real-world data from the London Stock Exchange (LSE) is
                 examined using the trophic detection framework.
                 Although significant structure is detected in subsets
                 of the real-world data, the results are inconsistent.
                 However, given limitations of the LSE data, the lack of
                 consistent detection is not surprising. Most notably,
                 each trader in this data represents an entity acting on
                 the behalf of many individuals and institutions having
                 different strategies. Due to this aggregation, the
                 trading actions of individuals are obfuscated and thus
                 the trophic structure is as well. Examination of
                 real-world data with greater specificity - detailing
                 trades at the level of individuals - is warranted.",
  notes =        "Supervisor Stephanie Forrest",

Genetic Programming entries for Todd D Kaplan