Novel comparative methodology of hybrid support vector machine with meta-heuristic algorithms to develop an integrated candlestick technical analysis model

Author:

Mahmoodi ArminORCID,Hashemi Leila,Mahmoodi Amin,Mahmoodi Benyamin,Jasemi Milad

Abstract

PurposeThe proposed model has been aimed to predict stock market signals by designing an accurate model. In this sense, the stock market is analysed by the technical analysis of Japanese Candlestick, which is combined by the following meta heuristic algorithms: support vector machine (SVM), meta-heuristic algorithms, particle swarm optimization (PSO), imperialist competition algorithm (ICA) and genetic algorithm (GA).Design/methodology/approachIn addition, among the developed algorithms, the most effective one is chosen to determine probable sell and buy signals. Moreover, the authors have proposed comparative results to validate the designed model in this study with the same basic models of three articles in the past. Hence, PSO is used as a classification method to search the solution space absolutelyand with the high speed of running. In terms of the second model, SVM and ICA are examined by the time. Where the ICA is an improver for the SVM parameters. Finally, in the third model, SVM and GA are studied, where GA acts as optimizer and feature selection agent.FindingsResults have been indicated that, the prediction accuracy of all new models are high for only six days, however, with respect to the confusion matrixes results, it is understood that the SVM-GA and SVM-ICA models have correctly predicted more sell signals, and the SCM-PSO model has correctly predicted more buy signals. However, SVM-ICA has shown better performance than other models considering executing the implemented models.Research limitations/implicationsIn this study, the authors to analyze the data the long length of time between the years 2013–2021, makes the input data analysis challenging. They must be changed with respect to the conditions.Originality/valueIn this study, two methods have been developed in a candlestick model, they are raw based and signal-based approaches which the hit rate is determined by the percentage of correct evaluations of the stock market for a 16-day period.

Publisher

Emerald

Subject

General Materials Science

Reference58 articles.

1. A nonlinear autoregressive model with exogenous variables neural network for stock market timing: the candlestick technical analysis;International Journal of Engineering,2016

2. New efficient hybrid candlestick technical analysis model for stock market timing on the basis of the Support Vector Machine and Heuristic Algorithms of Imperialist Competition and Genetic;Expert Systems with Applications,2018

3. Classification and prediction of stock market index based on fuzzy metagraph;Procedia Computer Science,2015

4. Surveying stock market forecasting techniques–Part II: soft computing methods;Expert Systems with Applications,2009

5. Wrapper ANFIS-ICA method to do stock market timing and feature selection on the basis of Japanese Candlestick;Expert Systems with Applications,2015

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