Soft Computing

, Volume 21, Issue 18, pp 5387–5398 | Cite as

Stock market trend prediction using AHP and weighted kernel LS-SVM

  • Ivana Marković
  • Miloš Stojanović
  • Jelena Stanković
  • Milena Stanković
Methodologies and Application

Abstract

Nowadays, stock market trend prediction represents a challenging subject both in terms of the choice of the prediction model and in terms of constructing the set of features that model will use for prediction. To address both of these aspects, we propose a feature ranking and feature selection approach in combination with weighted kernel least squares support vector machines (LS-SVMs). We introduce the analytic hierarchy process (AHP) into the stock market and propose evaluation criteria which provide the prediction model with relevant knowledge of the underlying processes of the studied stock market. The feature weights obtained by the AHP method are used for feature ranking and selection, and used with the LS-SVMs through a weighted kernel. The test results indicate that the proposed model outperforms the benchmark models. In addition, the set of feature weights obtained by the proposed approach can also independently be incorporated into other kernel-based learners.

Keywords

Analytic hierarchy process Stock market trend prediction Least squares support vector machines Weighted kernel 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Ivana Marković
    • 1
  • Miloš Stojanović
    • 2
  • Jelena Stanković
    • 1
  • Milena Stanković
    • 3
  1. 1.Faculty of EconomicsUniversity of NišNišSerbia
  2. 2.College of Applied Technical SciencesNišSerbia
  3. 3.Faculty of Electronic EngineeringUniversity of NišNišSerbia

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