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A stock selection algorithm hybridizing grey wolf optimizer and support vector regression
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DOI：10.1016/j.eswa.2021.115078.png)
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Abstract
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Artificial intelligence remarkably facilitates quantitative investment. A latest intelligent search algorithm, grey wolf optimizer, is well integrated with support vector regression machine to obtain the optimal portfolio. The performance of the hybrid algorithm is empirically investigated through transactional and financial data from stock markets of America and China. The experimental results indicate that (i) the proposed algorithm is able to stably achieve excess returns; (ii) compared with genetic algorithm, particle swarm optimization, gravitational search algorithm and harmony search, the enhanced grey wolf optimizer significantly boots the predictive performance of support vector regression machine; (iii) the proposed algorithm can achieve the better profitability and the higher reliability in Chinese A-share market.
Keywords:
Quantitative investment
Portfolio optimization
Stock selection
Grey wolf optimizer
Support vector regression
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