Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

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

A novel approach for candlestick technical analysis using a combination of the support vector... 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/approachIt 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.FindingsBased 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/valueIn 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Asian Journal of Economics and Banking Emerald Publishing

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

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

Asian Journal of Economics and Banking , Volume 7 (1): 23 – Mar 24, 2023

Abstract

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/approachIt 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.FindingsBased 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/valueIn 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.

Loading next page...
 
/lp/emerald-publishing/a-novel-approach-for-candlestick-technical-analysis-using-a-gOAUE0UkDS

References (76)

Publisher
Emerald Publishing
Copyright
© Armin Mahmoodi, Leila Hashemi, Milad Jasemi, Jeremy Laliberté, Richard C. Millar and Hamed Noshadi
ISSN
2615-9821
eISSN
2633-7991
DOI
10.1108/ajeb-11-2021-0131
Publisher site
See Article on Publisher Site

Abstract

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/approachIt 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.FindingsBased 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/valueIn 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.

Journal

Asian Journal of Economics and BankingEmerald Publishing

Published: Mar 24, 2023

Keywords: Stock market predicting; Candlestick technical analysis; Neural network; Support vector machine; Particle swarm optimization

There are no references for this article.