- author = "Fiacc Larkin",
- title = "Artificial Evolution Approaches to Address the Data Challenges Encountered During Financial Forecasting",
- school = "Computer Science and Information Systems, Faculty of Science and Engineering, University of Limerick",
- year = "2010",
- address = "Ireland",
- month = jun # " 2",
- keywords = "genetic algorithms, genetic programming, financial forecasting, evolutionary computation",
-
URL = "
https://hdl.handle.net/10344/428",
-
URL = "https://researchrepository.ul.ie/articles/thesis/Artificial_evolution_approaches_to_address_the_data_challenges/19811524?file=35260693",
-
URL = "https://researchrepository.ul.ie/ndownloader/files/35260693/1",
- size = "214 pages",
-
abstract = "This thesis is concerned with leveraging the power of
evolutionary computation to find structure or models
within complex datasets. We opt for a data-centric
approach to this issue, paying particular (but not
exclusive) attention to real world datasets from the
financial forecasting domain. Financial forecasting is
an alluring endeavour that does not enjoy the sort of
widely acknowledged success as do other popular areas
of EC application such as circuit design, scheduling or
industrial process control.
Data believed to be pertinent to financial forecasting tends to be large and multi-dimensional, it is extremely non-linear and abundant with such a degree of noise that it has led to the popular school of thought that forecasting financial datasets is fundamentally impossible. Furthermore, there exists no widely accepted consensus, theoretical or otherwise, on the dynamics of financial markets. These and other troubling attributes make financial forecasting and the interpretation of financial data a fruitful testbed for evolutionary algorithms.
By addressing the unique data challenges inspired by this domain we advance the understanding and capabilities of evolutionary computation. In particular our investigations result in the creation and proof of concept of two brand new evolutionary algorithms; the Hybrid Forecasting System, used to evolve practical objectives to a problem, and, Evolutionary Multidimensional Scaling, a new Multidimensional Scaling algorithm more appropriate to financial applications than existing MDS algorithms. In addition to these new algorithms this thesis demonstrates the application of EC to a new source of data (quantitative news sentiment), highlights a phenomenon detrimental to EAs in noisy environments and demonstrates three new styles of data visualisation.",
-
notes = "Title sometimes given as 'Artificial evolution
approaches to address the data challenges'
Boe Boeing 2005-08-01 to 2005-10-04, Cat Caterpillar 2007-08-07 to 2007-10-10, Coke Coca Cola 2006-03-31 to 2006-06-06, GE General Electric 2007-08-27 to 2007-10-26, Intel Intel 2004-08-18 to 2004-10-08, J&J Johnson and Johnson 2004-01-11 to 2005-01-05, Msft Microsoft 2005-08-11 to 2005-10-06, Nc Nacco Industries 2006-05-11 to 2006-07-19, Pfi Pfizer 2006-10-30 to 2007-01-03, Wmt Walmart 2007-04-03 to 2007-06-07,
Supervisor: Conor Ryan",
