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Volume 7 - Number 1 | March 2023

A novel approach for candlestick technical analysis using a combination of the support vector machine and particle swarm optimization

Armin Mahmoodi, Leila Hashemi, Milad Jasemi, Jeremy Laliberté, Richard C. Millar, and Hamed Noshadi

28/03/2023 155 0 0
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Stock market predicting Candlestick technical analysis Neural network Support vector machine Particle swarm optimization

Abstract:

Purpose
In this research, the main purpose is to use a suitable structure to predict the trading signals of the stock market with high accuracy. For this purpose, two models for the analysis of technical adaptation were used in this study.
Design/methodology/approach
It can be seen that support vector machine (SVM) is used with particle swarm optimization (PSO) where PSO is used as a fast and accurate classification to search the problem-solving space and finally the results are compared with the neural network performance.
Findings
Based on the result, the authors can say that both new models are trustworthy in 6 days, however, SVM-PSO is better than basic research. The hit rate of SVM-PSO is 77.5%, but the hit rate of neural networks (basic research) is 74.2.
Originality/value
In this research, two approaches (raw-based and signal-based) have been developed to generate input data for the model: raw-based and signal-based. For comparison, the hit rate is considered the percentage of correct predictions for 16 days.

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Further reading

  1. Cristianini, N. and Shawe-Taylor, J. (2000), An Introduction to Support Vector Machines, Cambridge University Press, London.
  2. Demidova, L., Nikulchev, E. and Sokolova, Y. (2016), “The SVM classifier based on the modified particle swarm optimization”, (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 7 No. 2, pp. 18-32.
  3. Indra, G., Jemi gold, P., Pavithra, P. and Akila, K. (2021), “Applicability of svm & narx for prediction alayis of flood in humid and semi-humid regions”, Annals of the Romanian Society for Cell Biology, Vol. 25 No. 6, pp. 6282-6293.
  4. Majhi, B., Rout, M. and Baghel, V. (2014), “On the development and performance evaluation of a multiobjective GA-based RBF adaptive model for the prediction of stock indices”, Journal of King Saud University-Computer and Information Sciences, Vol. 26 No. 3, pp. 319-331.
  5. Nunno, F., de Marinis, G., Gargano, R. and Granata, F. (2021), “Tide prediction in the venice lagoon using nonlinear autoregressive exogenous (NARX) neural network”, Water, Vol. 13, p. 1173.
  6. Sahin, U. and Ozbayoglu, A.M. (2014), “TN-RSI: trend-normalized RSI indicator for stock trading systems with evolutionary computation”, Procedia Computer Science, Vol. 36, pp. 240-245.
  7. Xia, Y., Zhao, J., Ding, Q. and Jiang, A. (2021), “Incipient chiller fault diagnosis using an optimized Least squares support vector machine with gravitational search algorithm”, Frontiers in Energy Research, Vol. 9, 755649, doi: 10.3389/fenrg.2021.755649.

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