Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
387759 | Expert Systems with Applications | 2008 | 9 Pages |
Abstract
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 multi-layer 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.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Tong-Seng Quah,