- 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 = "
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",
