This research is to improve the efficiency of present prediction factors. It has been tested that many factors have prediction power toward stocks returns. Although the prediction power is not stable, the factors are still valuable. This research analyzes preceding factors by neural network in order to make better use of these factors. Besides, we examine 15 companies respectively and compare the results between neural network and liner regression of those companies. Data are divided into training period and prediction period. We use data of training period to build up our model and test the model by the data from prediction period to verify the prediction powers of the models. The results show neural network has better solution compared to liner regression in both training and prediction period. Neural network is more precision and has less prediction error.
| Identifer | oai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0621106-095254 |
| Date | 21 June 2006 |
| Creators | Huang, Chuan-feng |
| Contributors | none, none, none |
| Publisher | NSYSU |
| Source Sets | NSYSU Electronic Thesis and Dissertation Archive |
| Language | Cholon |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0621106-095254 |
| Rights | not_available, Copyright information available at source archive |
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