Financial prediction using non linear classification techniques

Albanis, G.T. (2001). Financial prediction using non linear classification techniques. (Unpublished Doctoral thesis, City University London)

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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).

Item Type: Thesis (Doctoral)
Subjects: H Social Sciences > HG Finance
Divisions: Cass Business School

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