A New Smart Chaotic and Noise Based Approach to Predict Stock Prices

Stock is the mirror of economics country. To forecast the market in different countries there are different Techniques, but the computers make a revolution on Trend Forecast and the Methods of autoregressive and mathematics is applied on the forecast. By the cause of nonlinear of series didn't give a good result and nonlinear type is replaced .Neural network is one of the trend forecast finance time series is one of the important tools .There are many researches for Prediction of stock index. In IRAN researchers for Prediction of stock index is offered differently regression linear method and mathematics modeling or even nonlinear methods Including neural networks , fuzzy, Genetic , But these methods are unusual Circumstances Exchange and chaotic are not affected in their forecasting .On the researches out of IRAN Many of the world's leading stock including Netherlands, China, Taiwan, India, Turkey. Mostly neural network method is forecasted. Research of foreign researchers in this base has continued until recently and it forecasts with High accuracy .There is still space for growth and improvement and using of analysis and Good algorithm helps to improve this research .This research seeks to offer a smart model of Expected prices in the Tehran stock market and comparing with other methods and Detecting of chaos in data and presentation finding a way to eliminate it negative effects on forecasting and helping to improve accuracy. With giving attention to the Chaos are ignored in data to identifying nonlinear stocks and designing an occasion method for forecasting .Since that establishing a solution for eliminating these factors to polishing new method on modeling. At the beginning ofmaking this model is forecasting the series of time forecast is considered and then non linearity of it and the chaos on this series are proved. The next step is forecasting on different models and it starts with the linear models and we go to normal neural network and training different structures of layers with repetition on data will be done that shows behavior of stock index is learned but still Chaos not resolved correctly. Following our discussion increasing in forecast accuracy, Imperialistic competition algorithm and Radial Basis functions is Used and tested. At each stage of improvements in MSE forecast error is Observed But at the end of algorithm RBF Standard error MSE Is improved 7.75%. The result of a combination of different methods In other related research is much more acceptable and Higher accuracy in predicting on forecasting

Keywords:

Chaos , Stock Price , Imperialistic Competition Algorithm