Equity trend prediction with neural networks
| dc.contributor.author | Halliday, R. | |
| dc.date.accessioned | 2013-05-14T02:16:47Z | |
| dc.date.available | 2013-05-14T02:16:47Z | |
| dc.date.issued | 2004 | |
| dc.description.abstract | This paper presents results of neural network based trend prediction for equity markets. Raw equity exchange data is pre-processed before being fed into a series of neural networks. The use of Self Organising Maps (SOM) is investigated as a data classification method to limit neural network inputs and training data requirements. The resulting primary simulation is a neural network that can prediction whether the next trading period will be, on average, higher or lower than the current. Combinations of pre-processing and feature extracting SOM’s are investigated to determine the more optimal system configuration. | en |
| dc.identifier.citation | Halliday, R. (2004), Equity trend prediction with neural networks, Research Letters in the Information and Mathematical Sciences, 6, 15-29 | en |
| dc.identifier.issn | 1175-2777 | |
| dc.identifier.uri | http://hdl.handle.net/10179/4427 | |
| dc.language.iso | en | en |
| dc.publisher | Massey University | en |
| dc.subject | Neural networks | en |
| dc.subject | Data classification | en |
| dc.subject | Self Organising Maps | en |
| dc.title | Equity trend prediction with neural networks | en |
| dc.type | Article | en |