Use this URL to cite or link to this record in EThOS: | https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.368036 |
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Title: | Financial prediction using non linear classification techniques | ||||||
Author: | Albanis, George T. |
ISNI:
0000 0001 3409 9078
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Awarding Body: | City University London | ||||||
Current Institution: | City, University of London | ||||||
Date of Award: | 2001 | ||||||
Availability of Full Text: |
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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).
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Supervisor: | Not available | Sponsor: | Not available | ||||
Qualification Name: | Thesis (Ph.D.) | Qualification Level: | Doctoral | ||||
EThOS ID: | uk.bl.ethos.368036 | DOI: | Not available | ||||
Keywords: | HG Finance | ||||||
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