Utilizing Computational Intelligence for DJIA Stock Selection

This paper presents methodologies for equities selection based on soft-computing models which focus on applying fundamental analysis for equities screening. It 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), and studies their computational time complexity. Benchmark metrics are applied 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.