Forecasting financial variables using artificial neural networks - dynamic factor model
Journal of Economic and Financial Sciences
| Field | Value | |
| Title | Forecasting financial variables using artificial neural networks - dynamic factor model | |
| Creator | Babikir, Ali Mwambi, Henry | |
| Description | In this paper we introduce a new model that uses the dynamic factor model (DFM) framework combined with artificial neural network (ANN) analysis, which accommodates a large cross-section of financial and macroeconomic time series for forecasting. In our new ANN-DF model we use the factor model to extract factors from ANNs in sample forecasts for each single series of the dataset, which contains 228 monthly series. These factors are then used as explanatory variables in order to produce more accurate forecasts. We apply this new model to forecast three South African variables, namely, Rate on three-month trade financing, Lending rate and Short-term interest rate in the period 1992:1 to 2011:12. The model comparison results, based on the root mean square errors of three, six and twelve months ahead out-of-sample forecasts over the period 2007:1 to 2011:12 indicate that, in all of the cases, the ANN-DFM and the DFM statistically outperform the autoregressive (AR) models. In the majority of cases the ANN-DFM outperforms the DFM. The results indicate the usefulness of the factors in forecasting performance. The RMSE results are confirmed by the test of equality of forecast accuracy proposed by Diebold-Mariano. | |
| Publisher | AOSIS | |
| Date | 2017-06-06 | |
| Identifier | 10.4102/jef.v10i1.7 | |
| Source | Journal of Economic and Financial Sciences; Vol 10, No 1 (2017); 94-106 2312-2803 1995-7076 | |
| Language | eng | |
| Relation |
The following web links (URLs) may trigger a file download or direct you to an alternative webpage to gain access to a publication file format of the published article:
https://jefjournal.org.za/index.php/jef/article/view/7/7
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