Title: Portfolio selection with support vector regression: multiple kernels comparison

Authors: Pedro Alexandre Moura Barros Henrique; Pedro Henrique Melo Albuquerque; Sarah Sabino De Freitas Marcelino; Yaohao Peng

Addresses: University of Brasilia, Campus Darcy Ribeiro, Brasília, Distrito Federal, 70910-900, Brazil ' University of Brasilia, Campus Darcy Ribeiro, Brasília, Distrito Federal, 70910-900, Brazil ' University of Brasilia, Campus Darcy Ribeiro, Brasília, Distrito Federal, 70910-900, Brazil ' University of Brasilia, Campus Darcy Ribeiro, Brasília, Distrito Federal, 70910-900, Brazil

Abstract: This study aimed to verify whether the use of support vector regression (SVR) makes the portfolio's return exceed the market. For such proposal, SVR was applied for 15 different kernel functions to select the best stocks for each quarter, calculating the quarterly portfolio return and cumulative return along the period. Subsequently, the returns of these portfolios were compared with the returns of a market benchmark. White's (2000) test was applied to avoid the data-snooping effect in assessing the statistical significance of the portfolios developed by the training strategies. The portfolio selected by SVR with inverse multiquadric kernel presented the highest cumulative return of 374.40% and a value at risk (VaR) of −6.87%. The results of this study corroborate the superiority hypothesis of the innovative method of SVR in the formation of portfolios, thus constituting a robust predictive method capable to cope with high dimensionality interactions.

Keywords: statistical learning theory; optimisation theory; financial econometrics; support vector machine; SVM; kernel methods.

DOI: 10.1504/IJBIDM.2021.115476

International Journal of Business Intelligence and Data Mining, 2021 Vol.18 No.4, pp.395 - 410

Received: 31 Jul 2018
Accepted: 30 Sep 2018
Published online: 07 Jun 2021 *

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