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Title: Financial prediction using non linear classification techniques
Author: Albanis, George T.
ISNI:       0000 0001 3409 9078
Awarding Body: City University London
Current Institution: City, University of London
Date of Award: 2001
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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).
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: HG Finance Finance Taxation Applied mathematics Mathematical statistics Operations research