Binary gravity search algorithm and support vector machine for forecasting and trading stock indices

Haijun Kang, Xiangyu Zong, Jianyong Wang and Haonan Chen

International Review of Economics & Finance, 2023, vol. 84, issue C, 507-526

Abstract: A hybrid Support Vector Machine (SVM) model is proposed and applied to the task of forecasting the daily returns of five popular stock indices in the world, including the S&P500, NKY, CAC, FTSE100 and DAX. The originality of this work is that the Binary Gravity Search Algorithm (BGSA) is utilized, in order to optimize the parameters and inputs of SVM. The results show that the forecasts made by this model are significantly better than the Random Walk (RW), SVM, best predictors and Buy-and-Hold. The average accuracy of BGSA-SVM for five stock indices is 52.87%. In general, this study proves that a profitable trading strategy based on BGSA-SVM prediction can be realized in a real stock market.

Keywords: Finance; Support vector machine; Binary gravity search algorithm; Stock indices; Forecasting (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reveco:v:84:y:2023:i:c:p:507-526

DOI: 10.1016/j.iref.2022.11.009

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