ADAPTIVE PATTERN RECOGNITION IN FINANCIAL DATA AND TIME SERIES
ADAPTIVE PATTERN RECOGNITION IN FINANCIAL DATA AND TIME SERIES
Abstract
Financial data and time series are available nowadays in different forms
and frequencies. Their analysis has long been a subject to extensive study and myriad of
different experiments, yet the main issue how to distinguish valuation information from
noise (or random changes) still remains. In this paper we discuss and implement an
approach for financial data analysis that is adaptive and can be applied to different
domains of financial analysis and management. As a main tool we have used self
organizing neural networks that are implemented in C++ and used for studying patterns in
corporate financial transactions. Major advantages and possible pitfalls of applied methods
are investigated with regard to their theoretical limitations, as well as with regard to their
currently presented application.
References
Issues for Field Settings. Chicago: Rand McNally.
Costeiros, E. T. (2014). SOMCode. Retrieved from SOMCode - Self-Organizing Map Code
Project: http://somcode.cpatc.embrapa.br/
Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From Data Mining to Knowledge
Discovery in Databases. KDNuggets, 5-8.
Han, J., & Kamber, M. (2006). Data Mining: Concepts and Techniques. Morgan Kaufmann
Publishers.
Kohonen, T. (1982). Self-Organized Formation of Topologically Correct Feature Maps.
Biological Cybernetics, 59-69.
Miller, M., & Orr, D. (1966). A model of the demand for money by firms. he Quarterly
Journal of Economics, 81, 413-435.
Sathya, R., & Abraham, A. (2013). Comparison of Supervised and Unsupervised Learning
Algorithms for Pattern Classification. International Journal of Advanced Research in
Artificial Intelligence, 34-38.
Trochim, W. (1985). Pattern matching, Validity and Conceptualization in Program
Evaluation. Evaluation Review, 575-604.
UKAEA. (2014, 08 02). UKAEA financial transactions. Retrieved from UKAEA:
https://www.gov.uk/government/publications/ukaea-financial-transactions-april-2013
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
By submitting a paper for publishing the authors hereby comply with the following provisions: 1. The authors retain the copyrights and only give the journal the right for first publication while licensing the work under Creative Commons Attribution License, which grants permissions to others to share the contribution citing this journal as first publication of the text. 2. The authors may enter separate, additional contractual relations for non-exclusive distribution of the published version of the work in this journal (e.g. to upload it in an institutional depository, or to be published in a book), given that they cite the first publication in this journal. 3. The authors are allowed and are encouraged to publish their works online (e.g. to upload it in an institutional depository, personal websites, social networks, etc.) before, during, and after the submission of the paper here, because this may lead to productive exchange, as well as earlier and larger referencing of the published works (see The Effect of Open Access).
