Comparison of several combined methods for forecasting Tehran stock exchange index
Ali Raoofi (),
Amir Hossein Montazer-Hojjat and
Pouyan Kiani ()
International Journal of Business Forecasting and Marketing Intelligence, 2016, vol. 2, issue 4, 315-333
Forecasting economic and financial variables is of high interest to economic policy-makers in all countries. In this paper, the Tehran Stock Exchange Price Index (TEPIX) is estimated and forecasted using daily data for the period 22 May 2011 to 11 August 2011. To achieve that goal, various forecasting methods will be applied, including ARIMA, FARIMA, ANN and ANFIS models. Comparing the forecast accuracy of the models mentioned above, using forecast accuracy measures such as RMSE, MAE, MAPE and U-Thiel implied that the combined models of ANFIS and FARIMA have outperformed other models of forecasting daily stock indices. However, statistical comparison of forecast accuracy of different models using statistics such as Harvey, Leybourne and Newbold shows no significant difference between the forecast accuracy of these models.
Keywords: stock markets; stock index forecasting; ANNs; artificial neural networks; ANFIS; adaptive neuro-fuzzy inference systems; fuzzy logic; FARIMA; Iran; daily stock indices; forecasting accuracy. (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:ids:ijbfmi:v:2:y:2016:i:4:p:315-333
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