Financial prediction using non linear classification techniques
Albanis, G.T. (2001). Financial prediction using non linear classification techniques. (Unpublished Doctoral thesis, City University London)
Abstract
In this thesis, we explore the ability of statistical classification methods to predict financial events in the bond and stock markets. Our classification methods include conventional Linear Dicriminant Analysis (LDA), and a number of less familiar non-linear techniques such as Probabilistic Neural Network (PNN), Learning Vector Quanization (LVQ), Oblique Classifer (OCI), and Ripper Rule Induction (RRI).
| Publication Type: | Thesis (Doctoral) |
|---|---|
| Subjects: | H Social Sciences > HG Finance |
| Departments: | Bayes Business School Doctoral Theses Bayes Business School > Bayes Business School Doctoral Theses |
![[thumbnail of Financial_prediction_using_non_linear_classification_techniques.pdf]](https://openaccess.city.ac.uk/style/images/fileicons/application_pdf.png)
Preview
Download (40MB) | Preview
Export
Downloads
Downloads per month over past year
Metadata
Metadata
MetadataAltmetric
CORE (COnnecting REpositories)
Actions (login required)
Actions (login required)
Admin Login