Presenting a Model for Predicting Stock Market Trends by Detecting Fractal Patterns Based on Elliott Wave Theory Using Deep Learning Method

Subject Areas : Financial engineering

Masoud Nadem ¹ , Yahya Kamyabi ² , esfandiar malekian ³

1 - Department of Accounting, Faculty of Economics and Administrative Sciences, University of Mazandaran, Babolsar, Iran.
2 - Department of Accounting, Faculty of Economics and Administrative Sciences, University of Mazandaran, Babolsar, Iran
3 - Department of Accounting, Faculty of Economics and Administrative Sciences, University of Mazandaran, Babolsar, Iran

Received: 2023-01-13 Accepted : 2023-05-14 Published : 2023-12-22

Keywords: Fractal, deep learning, recurrent neural networks, Wave Patterns, Elliott Waves,

Abstract :

Today, artificial intelligence has made a big change in the recognition of chart patterns in technical analysis. Although, the emergence of new and complex analytical methods in technical analysis has provided a new challenge for artificial intelligence methods. One of the popular and complex technical analysis methods is Elliott Wave Theory. On the other hand, the speed of progress of artificial intelligence methods is such that a more powerful method is introduced every time. One of the new and powerful artificial intelligence methods is the deep learning method. Therefore, in this research, a model has been presented to predict the trend of the stock market through the detection of fractal patterns based on Elliott wave theory using deep learning method. In this research, 15 Elliott wave patterns were considered, and then 1002 samples of stock price charts of companies listed on Tehran Stock Exchange were collected and labeled for patterns, and finally entered as input into deep learning algorithm using recurrent neural network model for recognition. In this research, RapidMiner 9.9 software was used and accuracy criteria were used to determine the power of the model. Based on the results, the accuracy of developed model in recognizing patterns is 61%.

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