Financial Computational Intelligence
AbstractArtificial intelligence decision support system is always a popular topic in providing the human with an optimized decision recommendation when operating under uncertainty in complex environments. The particular focus of our discussion is to compare different methods of artificial intelligence decision support systems in the investment domain â€“ the goal of investment decision-making is to select an optimal portfolio that satisfies the investorâ€™s objective, or, in other words, to maximize the investment returns under the constraints given by investors. In this study we apply several artificial intelligence systems like Influence Diagram (a special type of Bayesian network), Decision Tree and Neural Network to get experimental comparison analysis to help users to intelligently select the best portfoli
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Bibliographic InfoPaper provided by Society for Computational Economics in its series Computing in Economics and Finance 2005 with number 42.
Date of creation: 11 Nov 2005
Date of revision:
Artificial intelligence; neural network; decision tree; bayesian network;
Find related papers by JEL classification:
- C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
This paper has been announced in the following NEP Reports:
- NEP-ALL-2005-11-19 (All new papers)
- NEP-CMP-2005-11-19 (Computational Economics)
- NEP-ICT-2005-11-19 (Information & Communication Technologies)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Chiu-Che Tseng, Piotr J. Gmytrasiewicz, Chris Ching, 2001. "Refining Influence Diagram For Stock Portfolio Selection," Computing in Economics and Finance 2001 241, Society for Computational Economics.
- Chiu-Che Tseng, 2003. "Comparing Artificial Intelligence Systems for Stock Portfolio Selection," Computing in Economics and Finance 2003 236, Society for Computational Economics.
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