Real stock trading using soft computing models
- Authors
- Doeksen, B.; Abraham, A.; Thomas, J.; Paprzycki, M.
- Issue Date
- Apr-2005
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Citation
- International Conference on Information Technology: Coding and Computing, ITCC, v.2, pp 162 - 167
- Pages
- 6
- Journal Title
- International Conference on Information Technology: Coding and Computing, ITCC
- Volume
- 2
- Start Page
- 162
- End Page
- 167
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/65521
- DOI
- 10.1109/itcc.2005.238
- ISSN
- 0000-0000
- Abstract
- The main focus of this study is to compare different performances of soft computing paradigms for predicting the direction of individuals stocks. Three different artificial intelligence techniques were used to predict the direction of both Microsoft and Intel stock prices over a period of thirteen years. We explored the performance of artificial neural networks trained using backpropagation and conjugate gradient algorithm and a Mamdani and Takagi Sugeno Fuzzy inference system learned using neural learning and genetic algorithm. Once all the different models were built the last part of the experiment was to determine how much profit can be made using these methods versus a simple buy and hold technique. © 2005 IEEE.
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Collections - College of Software > School of Computer Science and Engineering > 1. Journal Articles
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