1Professor Faculty Member Department of Accounting, Science and Research Branch, Islamic Azad University Tehran, Iran
2Associate Professor and Faculty Member Department of Industrial Management, Central Tehran Branch, Islamic Azad University Tehran, Iran
3Assistant Professor and Faculty Member Department of Accounting, Science and Research Branch, Islamic Azad University Tehran, Iran
4Assistant Professor and Faculty Member Department of Economy, Science and Research Branch, Islamic Azad University Tehran, Iran
5PhD Candidate Science and Research Branch, Islamic Azad University Tehran, Iran Correspondent Author
In this study, 3 models of Time-Varying Parameters (TVP), Dynamic Model Selection (DMS) and Dynamic Model Averaging (DMA) and a comparison with the Ordinary Least Squares (OLS) method in MATLAB in the time period 2003-2013 (with data on a monthly basis) are discussed. In the present study, the variables of unofficial exchange rate changes, interest rate changes and inflation in oil price forecast returns for stocks in Tehran Stock Exchange are used. The study concludes that dynamic models with time-varying parameters are more accurate in predicting returns in the Stock Exchange, in a way that the MAFE and MSFE models, DMA, DMS which have complete dynamics are more efficient than other models. As a consequence, it can be said that the variability of the coefficients of the variables in the TVP model cannot lead to higher accuracy in predicting returns in the Stock Exchange, and it is required that the dynamics of time-varying variables of the model used to predict stock returns be taken into consideration
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