Abstract
One of the popular but complicated methods in technical analysis is the Elliott wave method. In this method, the most important part is to recognize the main trend patterns of the market, which is a difficult task due to the fractal structure of the market. Bu More
Abstract
One of the popular but complicated methods in technical analysis is the Elliott wave method. In this method, the most important part is to recognize the main trend patterns of the market, which is a difficult task due to the fractal structure of the market. But like other fields, the use of artificial intelligence in the field of financial forecasts has also become widespread. Therefore, it seems that the use of artificial intelligence in Elliott wave analysis is attractive. Therefore, in the current research, by introducing a deep learning model to predict the market through the detection of Elliott wave patterns, it has been investigated and compared the power of the model in two modes of binary and multiple classification. In this research, for 15 considered patterns, 1002 examples of stock price charts of companies present in the Iranian stock market in the 11-year period from 1390 to 1400 were collected and labeled, and finally for recognition as input to the deep learning algorithm with Recurrent neural network model was used in binary and multiple classification modes. In this research, RapidMiner 9.9 software was used to design and implement the model, and accuracy criteria were used to determine the power of the model. The results show 18% accuracy in pattern recognition in multiple classification mode and 61% accuracy in binary classification mode. Therefore, the power of the deep learning model in detecting the fractal patterns of Elliott waves and as a result predicting the market trend is significantly higher in the binary classification mode than in the multiple classification mode. Therefore, the present study recommends the use of deep learning model with binary classification to detect fractal patterns of Elliott waves.
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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 More
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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The main goal of this research is to studying an identifying the main influencing variables on the TEPIX (Tehran Stock Price Index) and modelling them using artificial neural networks and comparing results with technical analysis and Elliot waves. Independent variables More
The main goal of this research is to studying an identifying the main influencing variables on the TEPIX (Tehran Stock Price Index) and modelling them using artificial neural networks and comparing results with technical analysis and Elliot waves. Independent variables used are dollar exchange rate, inflation, GDP, unemployment and liquidity and dependent variable is TEPIX. In this study, artificial neural networks (NLP and GMDH), technical analysis tools (Elliot waves and regression channel) are used that they show between independent variables in GMDH, unemployment is unneeded variable and have low influence, but others have high influence in the model. Further the study shows that technical analysis and artificial neural networks may have same results, but ANN have more power to predict the TEPIX
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