Applying Least Squares Support Vector Machines to Mean-Variance Portfolio Analysis
Joint Authors
Source
Mathematical Problems in Engineering
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-06-27
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
Portfolio selection problem introduced by Markowitz has been one of the most important research fields in modern finance.
In this paper, we propose a model (least squares support vector machines (LSSVM)-mean-variance) for the portfolio management based on LSSVM.
To verify the reliability of LSSVM-mean-variance model, we conduct an empirical research and design an algorithm to illustrate the performance of the model by using the historical data from Shanghai stock exchange.
The numerical results show that the proposed model is useful when compared with the traditional Markowitz model.
Comparing the efficient frontier and total wealth of both models, our model can provide a more measurable standard of judgment when investors do their investment.
American Psychological Association (APA)
Wang, Jian& Kim, Junseok. 2019. Applying Least Squares Support Vector Machines to Mean-Variance Portfolio Analysis. Mathematical Problems in Engineering،Vol. 2019, no. 2019, pp.1-10.
https://search.emarefa.net/detail/BIM-1195515
Modern Language Association (MLA)
Wang, Jian& Kim, Junseok. Applying Least Squares Support Vector Machines to Mean-Variance Portfolio Analysis. Mathematical Problems in Engineering No. 2019 (2019), pp.1-10.
https://search.emarefa.net/detail/BIM-1195515
American Medical Association (AMA)
Wang, Jian& Kim, Junseok. Applying Least Squares Support Vector Machines to Mean-Variance Portfolio Analysis. Mathematical Problems in Engineering. 2019. Vol. 2019, no. 2019, pp.1-10.
https://search.emarefa.net/detail/BIM-1195515
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1195515