ℓ(p)-Norm multikernel learning approach for stock market price forecasting.
Linear multiple kernel learning model has been used for predicting financial time series. However, ℓ(1)-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 ℓ(p)-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 ℓ(1)-norm multiple support vector regression model.
School of Mathematics and Statistics, Central South University, Changsha, Hunan 410075, China.
Xigao
X
Shao
Kun
K
Wu
Bifeng
B
Liao
D003198:Computer Simulation; D006801:Humans; D007449:Investments; D007858:Learning; D008962:Models, Theoretical; D011237:Predictive Value of Tests; D060388:Support Vector Machine