ℓp-Norm Multikernel Learning Approach for Stock Market Price Forecasting

Xigao Shao,Kun Wu,Bifeng Liao

doi:10.1155/2012/601296

Abstract

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.

Highlights

Forecasting the future values of financial time series is an appealing yet difficult activity in the modern business world
Two experiments on a real financial time series have been carried out to assess the performance of pnorm MK-support vector regression (SVR)
The motivation behind the two experiments are to compare the performance of our proposed method with that of other methods, that is, single kernel support vector regression (SKSVR) [29] and 1-norm MK-SVR [22]

Summary

Introduction

Forecasting the future values of financial time series is an appealing yet difficult activity in the modern business world. Yao and Tan [5] used time series data and technical indicators as the input of neural networks to increase the forecast accuracy of exchange rates; Cao and Tay [6, 7] applied support vector machine (SVM) in financial forecasting and compared it with the multilayer back-propagation (BP) neural network and the regularized radial basis function (RBF) neural network; Qi and Wu [8] proposed a multilayer feed-forward network to forecast exchange rates; Pai and Lin [9] invested a hybrid ARIMA and support vector machines model in stock price forecasting; Pai et al [10] presented a hybrid SVM model to exploit the unique strength of the linear and nonlinear SVM models in forecasting exchange rate; Kwon and Moon [11] proposed a hybrid neurogenetic system for stock trading; Hung and Hong [12] presented an improved ant colony optimization algorithm in a support vector regression (SVR) model, called SVRCACO, for selecting suitable parameters in exchange rate forecasting; Jiang and He [13] introduced local grey SVR (LG-SVR) integrated grey relational grade with local SVR for financial times eries forecasting; and so on

Methods

Results

Conclusion