It is shown that ensemble classifiers composed of neural networks trained using particle swarm optimisation can uncover a substantial degree of predictability in stock price movements. As in a previous work by the authors use is made here of a training metric, the Matthews correlation coefficient, that has been shown to better handle numerically unbalanced data sets. The work provides a solid basis for the future construction of a trading model. © Springer-Verlag 2013.
CITATION STYLE
Khoury, P., & Gorse, D. (2013). Investigation of the predictability of steel manufacturer stock price movements using particle swarm optimisation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8227 LNCS, pp. 673–680). https://doi.org/10.1007/978-3-642-42042-9_83
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