Forecasting stock returns on the Amman Stock Exchange: Do neural networks outperform linear regressions?
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Author(s)Abdel Razzaq Al Rababa’a ,Zaid SaidatLink to ORCID Index: https://orcid.org/0000-0003-4866-4765
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Raed HendawiLink to ORCID Index: https://orcid.org/0000-0002-2166-2287
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DOIhttp://dx.doi.org/10.21511/imfi.18(4).2021.24
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Article InfoVolume 18 2021, Issue #4, pp. 280-296
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170 Downloads
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Creative Commons Attribution 4.0 International License
Different models have been used in the finance literature to predict the stock market returns. However, it remains an open question whether non-linear models can outperform linear models while providing accurate predictions for future returns. This study examines the prediction of the non-linear artificial neural network (ANN) models against the baseline linear regression models. This study aims specifically to compare the prediction performance of regression models with different specifications and static and dynamic ANN models. Thus, the analysis was conducted on a growing market, namely the Amman Stock Exchange. The results show that the trading volume and interest rates on loans tend to explain the monthly returns the most, compared to other predictors in the regressions. Moreover, incorporating more variables is not found to help in explaining the fluctuations in the stock market returns. More importantly, using the root mean square error (RMSE), as well as the mean absolute error statistical measures, the static ANN becomes the most preferred model for forecasting. The associated forecasting errors from these metrics become equal to 0.0021 and 0.0005, respectively. Lastly, the analysis conducted with the dynamic ANN model produced the highest RMSE value of 0.0067 since November 2018 following the amendment to the Jordanian income tax law. The same observation is also seen since the emerging of the COVID-19 outbreak (RMSE = 0.0042).
- Keywords
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JEL Classification (Paper profile tab)C45, C53, D53, G12
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References55
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Tables5
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Figures5
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- Figure 1. Basic design of the artificial neural network
- Figure 2. Simple LSTM design
- Figure 3. Dynamic forecasts from LSTM vs. actual returns
- Figure 4. RMSE from the dynamic forecasts of LSTM
- Figure A1. Rolling regression estimates from Model 6
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- Table 1. List of predictors in the regressions and static NN
- Table 2. Descriptive statistics
- Table 3. Pairwise correlation
- Table 4. Estimates from the baseline models
- Table 5. Out-of-sample results (RMSE and MAE estimates)
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