Portfolio selection with support vector regression: multiple kernels comparison
by Pedro Alexandre Moura Barros Henrique; Pedro Henrique Melo Albuquerque; Sarah Sabino De Freitas Marcelino; Yaohao Peng
International Journal of Business Intelligence and Data Mining (IJBIDM), Vol. 18, No. 4, 2021

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.

Online publication date: Mon, 07-Jun-2021

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