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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Main Author: Ye, Shuhong
Other Authors: Wang Lipo
Format: Final Year Project
Language:English
Published: 2017
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Online Access:http://hdl.handle.net/10356/72032
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.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
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Electrical and electronic engineering
spellingShingle DRNTU::Engineering::Electrical and electronic engineering
Ye, Shuhong
Feature selection methods for financial engineering
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.
author2 Wang Lipo
author_facet Wang Lipo
Ye, Shuhong
format Final Year Project
author Ye, Shuhong
author_sort Ye, Shuhong
title Feature selection methods for financial engineering
title_short 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_sort feature selection methods for financial engineering
publishDate 2017
url http://hdl.handle.net/10356/72032
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