Applying Least Squares Support Vector Machines to Mean-Variance Portfolio Analysis

Jian Wang, Junseok Kim

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish
Article number4189683
JournalMathematical Problems in Engineering
Volume2019
DOIs
Publication statusPublished - 2019 Jan 1

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Least Squares Support Vector Machine
Support vector machines
Model
Portfolio Management
Efficient Frontier
Empirical Research
Portfolio Selection
Historical Data
Finance
Verify
Numerical Results

ASJC Scopus subject areas

  • Mathematics(all)
  • Engineering(all)

Cite this

Applying Least Squares Support Vector Machines to Mean-Variance Portfolio Analysis. / Wang, Jian; Kim, Junseok.

In: Mathematical Problems in Engineering, Vol. 2019, 4189683, 01.01.2019.

Research output: Contribution to journalArticle

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