Zaid Saidat
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Forecasting stock returns on the Amman Stock Exchange: Do neural networks outperform linear regressions?
Abdel Razzaq Al Rababa’a , Zaid Saidat , Raed Hendawi doi: http://dx.doi.org/10.21511/imfi.18(4).2021.24Investment Management and Financial Innovations Volume 18, 2021 Issue #4 pp. 280-296
Views: 637 Downloads: 170 TO CITE АНОТАЦІЯDifferent 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).