Stock trading using RBF neural networks
Stock market comprises of complex sample of data in time series. It has unique characteristics like non-linearity, high noise and uncertainties. In order to gain profit, prediction of stock price becomes a hot topic all the time. According to the characteristics of financial time series, BP neura...
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| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
2016
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| Online Access: | http://hdl.handle.net/10356/68014 |
| _version_ | 1826116050878464000 |
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| author | Hu, Donglin |
| author2 | Wang Lipo |
| author_facet | Wang Lipo Hu, Donglin |
| author_sort | Hu, Donglin |
| collection | NTU |
| description | Stock market comprises of complex sample of data in time series. It has unique
characteristics like non-linearity, high noise and uncertainties. In order to gain profit,
prediction of stock price becomes a hot topic all the time. According to the
characteristics of financial time series, BP neural network prediction model with the
minimum standard of empirical risk has poor generalization ability, which easy to
fall into the optimal and disadvantages of local presence, we come up with RBF
neural network. |
| first_indexed | 2024-10-01T04:05:06Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/68014 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:05:06Z |
| publishDate | 2016 |
| record_format | dspace |
| spelling | ntu-10356/680142023-07-07T16:32:27Z Stock trading using RBF neural networks Hu, Donglin Wang Lipo School of Electrical and Electronic Engineering DRNTU::Engineering Stock market comprises of complex sample of data in time series. It has unique characteristics like non-linearity, high noise and uncertainties. In order to gain profit, prediction of stock price becomes a hot topic all the time. According to the characteristics of financial time series, BP neural network prediction model with the minimum standard of empirical risk has poor generalization ability, which easy to fall into the optimal and disadvantages of local presence, we come up with RBF neural network. Bachelor of Engineering 2016-05-24T03:07:15Z 2016-05-24T03:07:15Z 2016 Final Year Project (FYP) http://hdl.handle.net/10356/68014 en Nanyang Technological University 63 p. application/pdf |
| spellingShingle | DRNTU::Engineering Hu, Donglin Stock trading using RBF neural networks |
| title | Stock trading using RBF neural networks |
| title_full | Stock trading using RBF neural networks |
| title_fullStr | Stock trading using RBF neural networks |
| title_full_unstemmed | Stock trading using RBF neural networks |
| title_short | Stock trading using RBF neural networks |
| title_sort | stock trading using rbf neural networks |
| topic | DRNTU::Engineering |
| url | http://hdl.handle.net/10356/68014 |
| work_keys_str_mv | AT hudonglin stocktradingusingrbfneuralnetworks |