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This study investigates predictability, chaos analysis, wavelet decomposition and the performance of neural network models in forecasting the return series of the Tehran Stock Exchange Index (TEDPIX). For this purpose, the daily data from April 24, 2009 to May 3, 2012 is used. Results show that TEDPIX series is chaotic and predictable with nonlinear effect. Also, according to obtained inverse of the largest lyapunov exponent, we are able to predict the future values of the series up to 31 days. Besides, our findings suggest that multi-layer feed forward neural network model and fuzzy model based on decomposed data, are of superior performances in predicting the return series. It is worth mentioning that, among these models, MFNN reveals the best performance.

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Generally,some booms in housing prices are followed by busts. One common phenomenon relating these changes is that the house price cycle is generally believed to the product of the short-run deviations from the long-run upward trends. The long-term cyclical fluctuation in Iran’s housing market was periodically occurred about every 6 years.
 Furthermore, Movements in house prices have significant impact on household welfare, financial stability and business cycles. Being able to forecast housing price booms is therefore of central importance for central banks, financial supervision authorities as well as for other economic agents. However, forecasting house prices using only a single or a few selected variables at a time intuitively appears efficient because only a single variable almost contain all of the pertinent investigative information about the past behavior of the variable. In this study, wavelet decomposition has been used to extract the cyclical components of house price, and then using the cyclical components and neural network methodwe start to forecast the booms in housing prices in 2013.