Using Neural Network for DJIA Stock Selection

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Using Neural Network for DJIA Stock Selection
—This paper presents methodologies to select equities based on soft-computing models which focus on applying fundamental analysis for equities screening. This paper compares the performance of three soft-computing models, namely Multilayer Perceptrons (MLP), Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and General Growing and Pruning Radial Basis Function (GGAP-RBF). It studies their computational time complexity; applies several benchmark matrices to compare their performance, such as generalize rate, recall rate, confusion matrices, and correlation to appreciation. This paper also suggests how equities can be picked systematically by using Relative Operating Characteristics (ROC) curve.
Tong-Seng Quah
Added 13 Dec 2010
Updated 13 Dec 2010
Type Journal
Year 2007
Where ENGL
Authors Tong-Seng Quah
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