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Information Journal Paper

Title

CAPABILITY COMPARISON OF THE MODELS BASED ON LONG MEMORY AND DYNAMIC NEURAL NETWORK MODELS IN FORECASTING THE STOCK RETURN INDEX IN TEHRAN STOCK EXCHANGE

Pages

  115-130

Abstract

 The aim of this study is to introduce an efficient nonlinear model for predicting the return of Tehran Stock Exchange (TSE) Price index. For this purpose, the daily time series of price index from Farvardin 1388 to Aban 1390 is used. This study includes 616 observations, 90% of which used for estimating coefficients and the remaining 60 observation are deduced for out of sample FORECASTING. By comparing the results of a nonlinear dynamic artificial neural network (NNAR) and a nonlinear regression model (autoregressive fractional integration moving average «ARFIMA»), we found that NNAR models have better performance in out of sample FORECASTING based on mean square error criteria (MSE) and root mean square error criteria (RMSE) than the nonlinear regression models (ARFIMA).

Cites

References

Cite

APA: Copy

KOMIJANI, A., & NADERI, E.. (2012). CAPABILITY COMPARISON OF THE MODELS BASED ON LONG MEMORY AND DYNAMIC NEURAL NETWORK MODELS IN FORECASTING THE STOCK RETURN INDEX IN TEHRAN STOCK EXCHANGE. FINANCIAL KNOWLEDGE OF SECURITY ANALYSIS (FINANCIAL STUDIES), 5(15), 115-130. SID. https://sid.ir/paper/200338/en

Vancouver: Copy

KOMIJANI A., NADERI E.. CAPABILITY COMPARISON OF THE MODELS BASED ON LONG MEMORY AND DYNAMIC NEURAL NETWORK MODELS IN FORECASTING THE STOCK RETURN INDEX IN TEHRAN STOCK EXCHANGE. FINANCIAL KNOWLEDGE OF SECURITY ANALYSIS (FINANCIAL STUDIES)[Internet]. 2012;5(15):115-130. Available from: https://sid.ir/paper/200338/en

IEEE: Copy

A. KOMIJANI, and E. NADERI, “CAPABILITY COMPARISON OF THE MODELS BASED ON LONG MEMORY AND DYNAMIC NEURAL NETWORK MODELS IN FORECASTING THE STOCK RETURN INDEX IN TEHRAN STOCK EXCHANGE,” FINANCIAL KNOWLEDGE OF SECURITY ANALYSIS (FINANCIAL STUDIES), vol. 5, no. 15, pp. 115–130, 2012, [Online]. Available: https://sid.ir/paper/200338/en

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