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

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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.

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Authors and Affiliations

  1. Faculty of Economics, University of Niš, Trg kralja Aleksandra Ujedinitelja 11, 18000, Niš, Serbia

    Ivana Marković & Jelena Stanković

  2. College of Applied Technical Sciences, Aleksandra Medvedeva 20, 18000, Niš, Serbia

    Miloš Stojanović

  3. Faculty of Electronic Engineering, University of Niš, Aleksandra Medvedeva 14, 18000, Niš, Serbia

    Milena Stanković

Authors
  1. Ivana Marković
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  2. Miloš Stojanović
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  3. Jelena Stanković
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  4. Milena Stanković
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Correspondence to Ivana Marković.

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Communicated by V. Loia.

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Marković, I., Stojanović, M., Stanković, J. et al. Stock market trend prediction using AHP and weighted kernel LS-SVM. Soft Comput 21, 5387–5398 (2017). https://doi.org/10.1007/s00500-016-2123-0

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