Hybrid Intelligent Systems for Stock Market Analysis (2001) [9 citations — 4 self]
Abstract:
Abstract: The use of intelligent systems for stock market predictions has been widely established. This paper deals with the application of hybridized soft computing techniques for automated stock market forecasting and trend analysis. We make use of a neural network for one day ahead stock forecasting and a neuro-fuzzy system for analyzing the trend of the predicted stock values. To demonstrate the proposed technique, we considered the popular Nasdaq-100 index of Nasdaq Stock Market SM. We analyzed the 24 months stock data for Nasdaq-100 main index as well as six of the companies listed in the Nasdaq-100 index. Input data were preprocessed using principal component analysis and fed to an artificial neural network for stock forecasting. The predicted stock values are further fed to a neuro-fuzzy system to analyze the trend of the market. The forecasting and trend prediction results using the proposed hybrid system are promising and certainly warrant further research and analysis. 1.
Citations
| 179 | A scaled conjugate gradient algorithm for fast supervised learning – Moller - 1993 |
| 24 | Fuzzy Inference Systems: A Critical Review, Computational Intelligence: Soft Computing and Fuzzy-Neuro Integration with Applications, Kayak O, Zadeh LA et al (Eds – Cherkassky - 1998 |
| 9 | Dynamic Evolving Fuzzy Neural Networks with 'm-out-of-n' Activation Nodes for On-line Adaptive Systems – Kasabov, Song - 1999 |
| 2 | Using Principal Component Transformation – Duszak, Loczkodaj - 1994 |
| 1 | Roles of Soft Computing and Fuzzy – LA |
