Computational Intelligence in Financial Forecasting and Agent-Based Modeling: Applications of Genetic Programming and Self-Organizing Maps

Created by W.Langdon from gp-bibliography.bib Revision:1.8081

@PhdThesis{Kampouridis:thesis,

• author = "Michael Kampouridis",
• title = "Computational Intelligence in Financial Forecasting and Agent-Based Modeling: Applications of Genetic Programming and Self-Organizing Maps",
• school = "School of Computer Science and Electronic Engineering, University of Essex",
• year = "2011",
• address = "UK",
• month = nov,
• keywords = "genetic algorithms, genetic programming",
• URL = "http://www.kampouridis.net/papers/thesis.pdf",
• URL = "http://ethos.bl.uk/OrderDetails.do?did=57&uin=uk.bl.ethos.548594",
• size = "180 pages",
• abstract = "This thesis focuses on applications of Computational Intelligence techniques to Finance and Economics. First of all, we build upon a Genetic Programming (GP)-based financial forecasting tool called Evolutionary Dynamic Data Investment Evaluator (EDDIE), which was developed, and reported on in the past, by researchers at the University of Essex. The novelty of the new version we present, which we call EDDIE 8, is its extended grammar, which allows the GP to search in the space of the technical indicators in order to form its trees. In this way, EDDIE 8 is not constrained into using pre-specified indicators, but it is left up to the GP to choose the optimal ones. Results show that, thanks to the new grammar, new and improved solutions can be found by EDDIE 8. Furthermore, we present work on the Market Fraction Hypothesis (MFH). This hypothesis is based on observations in the literature about the fraction dynamics of the trading strategy types that exist in financial markets. However, these observations have never been formalised before, nor have they been tested under real data. We therefore first formalize the hypothesis, and then propose a model, which uses a two-step approach, for testing the hypothesis. This approach consists of a rule-inference step and a rule-clustering step. We employ GP as the rule inference engine, and apply Self-Organising Maps (SOMs) to cluster the inferred rules. After running experiments on real datasets, we are able to obtain valuable information about the fraction dynamics of trading strategy types, and their long and short term behaviour. Finally, we present work on the Dinosaur Hypothesis (DH), which states that the behavior of financial markets constantly changes and that the population of trading strategies continually co-evolves with their respective market. To the best of our knowledge, this observation has only been made and tested under artificial datasets, but not with real data. We formalise this hypothesis by presenting its main constituents. We also test it with empirical datasets, where we again use a GP system to infer rules and SOM for clustering purposes. Results show that for the majority of the datasets tested, the DH is supported. Thus this indicates that markets have non-stationary behaviour and that strategies cannot remain effective unless they continually adapt to the changes happening in the market.",
• notes = "http://www.essex.ac.uk/csee/department/news/newsletter/31_10_11.aspx uk.bl.ethos.548594",

}

Genetic Programming entries for Michael Kampouridis

Citations