ℓp-Norm Multikernel Learning Approach for Stock Market Price Forecasting

Joint Authors

Wu, Kun
Liao, Bifeng
Shao, Xigao

Source

Computational Intelligence and Neuroscience

Issue

Vol. 2012, Issue 2012 (31 Dec. 2012), pp.1-10, 10 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2012-12-29

Country of Publication

Egypt

No. of Pages

10

Main Subjects

Biology

Abstract EN

Linear multiple kernel learning model has been used for predicting financial time series.

However, ℓ1-norm multiple support vector regression is rarely observed to outperform trivial baselines in practical applications.

To allow for robust kernel mixtures that generalize well, we adopt ℓp-norm multiple kernel support vector regression (1≤p<∞) as a stock price prediction model.

The optimization problem is decomposed into smaller subproblems, and the interleaved optimization strategy is employed to solve the regression model.

The model is evaluated on forecasting the daily stock closing prices of Shanghai Stock Index in China.

Experimental results show that our proposed model performs better than ℓ1-norm multiple support vector regression model.

American Psychological Association (APA)

Shao, Xigao& Wu, Kun& Liao, Bifeng. 2012. ℓp-Norm Multikernel Learning Approach for Stock Market Price Forecasting. Computational Intelligence and Neuroscience،Vol. 2012, no. 2012, pp.1-10.
https://search.emarefa.net/detail/BIM-484081

Modern Language Association (MLA)

Shao, Xigao…[et al.]. ℓp-Norm Multikernel Learning Approach for Stock Market Price Forecasting. Computational Intelligence and Neuroscience No. 2012 (2012), pp.1-10.
https://search.emarefa.net/detail/BIM-484081

American Medical Association (AMA)

Shao, Xigao& Wu, Kun& Liao, Bifeng. ℓp-Norm Multikernel Learning Approach for Stock Market Price Forecasting. Computational Intelligence and Neuroscience. 2012. Vol. 2012, no. 2012, pp.1-10.
https://search.emarefa.net/detail/BIM-484081

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-484081