Volume 2, Issue 4 (11-2020)                   sjamao 2020, 2(4): 1-11 | Back to browse issues page


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Sabbaghi Lalimi A H, Damavandi H. Using Machine Learning Methods in the Financial Market for Technical Analysis Based on Hybrid Models. sjamao. 2020; 2 (4) :1-11
URL: http://sjamao.srpub.org/article-7-87-en.html
Faculty of Industry Engineering, Department of Socio-Economic Systems, Sharif University of Technology, Iran.
Abstract:   (361 Views)
In this study, it is presented a new hybrid model based on deep neural networks to predict the direction and magnitude of the Forex market movement in the short term. The overall model presented is based on the scalping strategy and is provided for high frequency transactions. The proposed hybrid model is based on a combination of three models based on deep neural networks. The first model is a deep neural network with a multi-input structure consisting of a combination of Long Short Term Memory layers. The second model is a deep neural network with a multi-input structure made of a combination of one-dimensional Convolutional Neural network layers. The third model has a simpler structure and is a multi-input model of the Multi-Layer Perceptron layers. The overall model was also a model based on the majority vote of three top models. This study showed that models based on Long Short-Term Memory layers provided better results than the other models and even hybrid models with more than 70% accurate.
Full-Text [PDF 455 kb]   (741 Downloads)    
Type of Study: Research | Subject: Mathematical and Quantitative Methods
Received: 2020/09/15 | Accepted: 2020/10/30 | Published: 2020/11/25

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