Elliot Waves Predicting for Stock Marketing Using Euclidean Based Normalization Method Merged with Artificial Neural Network

Financial marketing is very common in the world to make money or to control the company strategy. Nearly all events trigger to each other and moreover countries. Some predicting methods, on guessing the marketing depends on natural behavior of the events. When, have a scrutinize to backwards, it can be evaluated that some upfront events occur periodically and trigger to each others and may lead to next known moving. Elliot is one of the famous estimating methods on stock marketing. Elliot waves let to time to think and analyze the next moving not in hurry. The proposed method consist of three stages (i) arranging the real time data (ii) data normalization according to Euclidean distance named Euclidean based normalization method and (iii) performing artificial neural network to predict the next swing of the stock or financial marketing. The results compared raw data results and minimum maximum normalization methods to EBNM. Mean squared error and average deviation and R² statistical value were used as performance criteria. According to MSE, the obtained results were 0.000484, 0.205069 and 0.003178 minimum maximum normalization, raw data set and EBNM method respectively. The performance of the proposed method has more accurate than the other two methods.