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Develop a Fuzzy System Based on Evolutionary Algorithms To Predict Stock Market

Kazemi, Mohammad Reza | 2009

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 40010 (01)
  4. University: Sharif University of Technology
  5. Department: Industrial Engineering
  6. Advisor(s): Kianfar, Farhad
  7. Abstract:
  8. today's financial markets such as stock market are more attractive and important position and wealth are considered income and therefore attracts many people have. But the other hand, activity in these markets requires a high risk of admission. The point that is important is that the risk of investing in these markets can be predicted to some extent with the trend of stocks and securities can be controlled. Time series trend of stock prices and non-static characters is excited. But analysis of such behavior is impossible, i.e., reliance on sophisticated tools and of course accept the possibility of an error can be predicted price to pay. Synthetic models of artificial intelligence today, due to high flexibility and ability to estimate nonlinear relationships without detailed assumptions and limitations of traditional methods such as time series and ... The dominant approach to the problem of forecasting have become. In this thesis to provide a combination of artificial intelligence model to predict stock price is considered in two stages of evolution will. Firstly, adopting a technical approach in forecasting stock price variables known to influence stock prices, using stepwise regression model to Login Mnadartryn variables are selected. In the second stage using a confirmatory genetic algorithm to create a TSK fuzzy expert system will be considered. Above algorithm by using a special type of evolutionary algorithms to symbiotic evolutionary algorithm, fuzzy database system, including knowledge base and rules database, the loses. Being provided the data the company stock price, and because of IBM America articles published in this company stock price forecasting using artificial intelligence hybrid models are provided, synthetic model presented in this thesis for IBM company stock price forecasts were used. Comparison of model results presented in this thesis and other articles presented in the models, significant improvement of previous models based on all criteria MAPE model presented in this thesis is a model and therefore can be presented as a powerful to predict stock price was used.

  9. Keywords:
  10. Forecasting ; Stock Market ; Stock Price ; Fuzzy-Genetic System ; Regression Analysis

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