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

•Developing two innovative and structurally different hybrid forecasting models.•Practicing stock market timing with the new models in comparison to the base study.•Inventive application of the Support Vector Machine and Heuristic Algorithms.•Application of Adaptive Japanese Candlestick concept for...

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Bibliographic Details
Published in:Expert systems with applications 2018-03, Vol.94, p.21-31
Main Authors: Ahmadi, Elham, Jasemi, Milad, Monplaisir, Leslie, Nabavi, Mohammad Amin, Mahmoodi, Armin, Amini Jam, Pegah
Format: Article
Language:English
Subjects:
Algorithms
Candlestick technical analysis
Competition
Finance
Forecasting
Genetic Algorithm
Genetic algorithms
Heuristic
Imperialist Competition Algorithm
Markets
Mathematical models
Neural networks
Parameter estimation
Securities markets
Stock market forecasting
Support Vector Machine
Support vector machines
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Summary:•Developing two innovative and structurally different hybrid forecasting models.•Practicing stock market timing with the new models in comparison to the base study.•Inventive application of the Support Vector Machine and Heuristic Algorithms.•Application of Adaptive Japanese Candlestick concept for attribute generation. In this paper, two hybrid models are used for timing of the stock markets on the basis of the technical analysis of Japanese Candlestick by Support Vector Machine (SVM) and Heuristic Algorithms of Imperialist Competition and Genetic. In the first model, SVM and Imperialist Competition Algorithm (ICA) are developed for stock market timing in which ICA is used to optimize the SVM parameters. In the second model, SVM is used with Genetic Algorithm (GA) where GA is used for feature selection in addition to SVM parameters optimization. Here the two approaches, Raw-based and Signal-based are devised on the basis of the literature to generate the input data of the model. For a comparison, the Hit Rate is considered as the percentage of correct predictions for periods of 1–6 day. The results show that SVM-ICA performance is better than SVM-GA and most importantly the feed-forward static neural network of the literature as the standard one.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2017.10.023