A Hybrid Stock Selection Model Based on Forecasting, Classification and Feature Selection
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- 발행기관
- 보안공학연구지원센터(IJUNESST) 바로가기
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- 간행물
- International Journal of u- and e- Service, Science and Technology 바로가기
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- 통권
- Vol.9 No.6 (2016.06)바로가기
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- 페이지
- pp.153-168
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- 저자
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- 언어
- 영어(ENG)
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- URL
- https://www.earticle.net/Article/A278479
※ 원문제공기관과의 협약기간이 종료되어 열람이 제한될 수 있습니다.
원문정보
초록
- 영어
- The basic aim of this paper is to provide a model to explain stock performance paramount level. To reach this purpose, this research proposes that rough set theory (RS), allied with the use of Grey Prediction, Semi-Supervised Graph Regularized Non-negative Matrix Factorization (SGNMF), K-means and Grey Relation, can out-perform the more standard approaches that are employed in economics. This study focuses on stock to select the optimal stock portfolio out applying the financial statement datum from the New Taiwan Economy database (TEJ). Firstly, this study collects relative financial ratio datum as the conditional attributes selection and then uses GM(1,1) for forecasting, SGNMF for choosing the more important conditional attributes, and rough set for figuring the best portfolio out. Finally, the Grey relational analysis is used to reduce the investment risk for fund allocation. This study will demonstrate that rough sets model is applicable to stock portfolio. The empirical result in Taiwan: During five years (2009-2013), the average annual rate of return was 20.41%, the accumulated rate of return for 9 quarter was 61.22%. The portfolio determined by the model is a promising alternative to the conventional methods for economic and financial prediction..
목차
Abstract
1. Introduction
2. Methodologies Review
2.1. Rough Sets
2.2. Grey Prediction Modeling(GM(1,1))
2.3. Semi-supervised Graph Regularized Non-negative Matrix Factorization(SGNMF)
2.4. Grey Relational Analysis
3. Research Model Development
3.1. Establishes Data Set for Rough Set By Grey Prediction Model
3.2. Decision-Making Attributes Selecting: Combining the Taiwan Industry Features with Buffet’s Rules
3.3. Modeling Flow of the Stock Portfolio Model
4. The Empirical Results of Stock Portfolio Model in Taiwan Stock Market
4.1. Material Reasoning
4.2. Difference of Fund Allocation and Their Investment Results
5. Conclusions and Discussions
Acknowledgements
References
키워드
Grey Forecasting SGNMF K-means Rough-Set Grey Relational Analysis Stock Portfolio저자
간행물 정보
발행기관
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- 발행기관명
- 보안공학연구지원센터(IJUNESST) [Science & Engineering Research Support Center, Republic of Korea(IJUNESST)]
- 설립연도
- 2006
- 분야
- 공학>컴퓨터학
- 소개
- 1. 보안공학에 대한 각종 조사 및 연구 2. 보안공학에 대한 응용기술 연구 및 발표 3. 보안공학에 관한 각종 학술 발표회 및 전시회 개최 4. 보안공학 기술의 상호 협조 및 정보교환 5. 보안공학에 관한 표준화 사업 및 규격의 제정 6. 보안공학에 관한 산학연 협동의 증진 7. 국제적 학술 교류 및 기술 협력 8. 보안공학에 관한 논문지 발간 9. 기타 본 회 목적 달성에 필요한 사업
간행물
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- 간행물명
- International Journal of u- and e- Service, Science and Technology
- 간기
- 격월간
- pISSN
- 2005-4246
- 수록기간
- 2008~2016
- 십진분류
- KDC 505 DDC 605
이 권호 내 다른 논문 / International Journal of u- and e- Service, Science and Technology Vol.9 No.6
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