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).

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The aim of this study was to investigate the effect of the retention per share compared to the dividend per share by modeling the firm’s market value as a function of the retention per share and the dividend per share for all firms in the Jordanian context using unbalanced panel data analysis for a sample of 2281 firm years covering the period from 2010 to 2021. The results of the pooled sample indicated a strong positive significant effect for dividends per share. However, the retention per share indicated a negative significant effect on the firm’s market value. The other robustness analysis for the two sub-samples and the financial and non-financial sub-samples indicated the same results, consistent with the pooled sample for the two main explanatory variables.