CHAOS ANALYSIS, WAVELET DECOMPOSITION AND THE PERFORMANCE OF NEURAL NETWORK MODELS IN FORECASTING TEHRAN STOCK EXCHANGE INDEX

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

ABBASINEJAD HOSSEIN; NADERI ESMAEIL

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: JOURNAL OF ECONOMIC MODELING RESEARCH Year:2012 | Volume:2 | Issue:8 Page(s): 119-140

Download Full-Text

Persian Verion

View:

2,299

Download:

Cites:

Information Journal Paper

Title

CHAOS ANALYSIS, WAVELET DECOMPOSITION AND THE PERFORMANCE OF NEURAL NETWORK MODELS IN FORECASTING TEHRAN STOCK EXCHANGE INDEX

Author(s)

ABBASINEJAD HOSSEIN | NADERI ESMAEIL | Issue Writer Certificate

Keywords

FORECASTINGQ2

EXCHANGE MARKETQ3

CHAOS ANALYSISQ2

WAVELET DECOMPOSITIONQ2

NEURAL NETWORK MODELSQ2

Abstract

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.

Cites