Creating short-term stockmarket trading strategies using artificial neural networks: A case study
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
Electronic versions
Developing short-term stockmarket trading systems is a complex process, as there is a great deal of random noise present in the time series data of individual securities. The primary difficulty in training neural networks to identify return expectations is to find variables to help identify the signal present in the data. In this paper, the authors follow the previously published Vanstone and Finnie methodology. They develop a successful neural network, and demonstrate its effectiveness as the core element of a financially viable trading system.
| Original language | English |
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| Title of host publication | WORLD CONGRESS ON ENGINEERING 2008, VOLS I-II |
| Editors | SI Ao, L Gelman, DWL Hukins, A Hunter, AM Korsunsky |
| Publisher | INT ASSOC ENGINEERS-IAENG |
| Pages | 80-84 |
| Number of pages | 5 |
| ISBN (print) | 978-988-98671-9-5 |
| Publication status | Published - 2008 |
| Externally published | Yes |
Publication series
| Name | Lecture Notes in Engineering and Computer Science |
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| Publisher | INT ASSOC ENGINEERS-IAENG |