Feature selection methods for financial engineering
This paper is mainly focused on the academic goal. By reproducing the method in the existing published paper using the same data sets and recommend improvements on the procedure. Two main feature selection methods are used in this paper. One is the Classic Support Vector Machine regression and...
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| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
2017
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| Subjects: | |
| Online Access: | http://hdl.handle.net/10356/72032 |
| _version_ | 1811693202377277440 |
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| author | Ye, Shuhong |
| author2 | Wang Lipo |
| author_facet | Wang Lipo Ye, Shuhong |
| author_sort | Ye, Shuhong |
| collection | NTU |
| description | This paper is mainly focused on the academic goal. By reproducing the method in the existing
published paper using the same data sets and recommend improvements on the procedure.
Two main feature selection methods are used in this paper. One is the Classic Support Vector
Machine regression and another one is an improved method Feature-weighted Support Vector
Machine regression which was proposed by James N. K. Liu and Yanxing Hu. The improved
method combines the Classic Support Vector Machine with the Grey correlation degree. Given
different weight values to different features, the closer the relation between the feature to the
target problem, the higher the weight value will be given. The processed data then goes into
the Support Vector Machine regression training and the output model will be used for
forecasting the stock daily close price.
In this paper, the historical data and technical indicators of 7 stocks are downloaded from China
Shenzhen A-share market. 5 stocks are used same as the reference data in the same period.
Another 2 Growth Enterprise Market stocks are added to test generalization of the method.
The result for this paper shows the Feature-weighted Support Vector Machine regression has
better performance in forecasting the stock price. |
| first_indexed | 2024-10-01T06:47:56Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/72032 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:47:56Z |
| publishDate | 2017 |
| record_format | dspace |
| spelling | ntu-10356/720322023-07-07T17:13:26Z Feature selection methods for financial engineering Ye, Shuhong Wang Lipo School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering This paper is mainly focused on the academic goal. By reproducing the method in the existing published paper using the same data sets and recommend improvements on the procedure. Two main feature selection methods are used in this paper. One is the Classic Support Vector Machine regression and another one is an improved method Feature-weighted Support Vector Machine regression which was proposed by James N. K. Liu and Yanxing Hu. The improved method combines the Classic Support Vector Machine with the Grey correlation degree. Given different weight values to different features, the closer the relation between the feature to the target problem, the higher the weight value will be given. The processed data then goes into the Support Vector Machine regression training and the output model will be used for forecasting the stock daily close price. In this paper, the historical data and technical indicators of 7 stocks are downloaded from China Shenzhen A-share market. 5 stocks are used same as the reference data in the same period. Another 2 Growth Enterprise Market stocks are added to test generalization of the method. The result for this paper shows the Feature-weighted Support Vector Machine regression has better performance in forecasting the stock price. Bachelor of Engineering 2017-05-23T08:10:11Z 2017-05-23T08:10:11Z 2017 Final Year Project (FYP) http://hdl.handle.net/10356/72032 en Nanyang Technological University 43 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering Ye, Shuhong Feature selection methods for financial engineering |
| title | Feature selection methods for financial engineering |
| title_full | Feature selection methods for financial engineering |
| title_fullStr | Feature selection methods for financial engineering |
| title_full_unstemmed | Feature selection methods for financial engineering |
| title_short | Feature selection methods for financial engineering |
| title_sort | feature selection methods for financial engineering |
| topic | DRNTU::Engineering::Electrical and electronic engineering |
| url | http://hdl.handle.net/10356/72032 |
| work_keys_str_mv | AT yeshuhong featureselectionmethodsforfinancialengineering |