Zaid Saidat

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).

This study investigates the relationship between big data and knowledge sharing and how they can be linked to banking innovation, including process and product innovation. In this study, questionnaires are tailored to fit the hypotheses formulated from data collected from 279 participants (including managers from several departments of the bank such as administration, research & development, accounting, operations, marketing, and sales) at the Housing Bank for Trade operating in the city of Irbid, Jordan. To get a correct result, this study used the structural equation model ‘SEM’ and found a positive relationship between big data and innovation in products and processes, which is confirmed by the data. BID positively affects product innovation (β = 0.302; p < 0.001) and process innovation (β = 0.286; p < 0.001). Also, the study confirms that mediating “knowledge sharing” plays a significant role in innovation and big data in the bank. This study brought evidence that big data is the major dimension that leads to knowledge sharing and innovation performance. The findings also show that companies must employ counterintuitive strategies when developing innovative products or services that differ significantly, quite a bit, from the established expectations of consumersи?. Ultimately, to reap the benefits of technology such as big data and market-driven investing, organizations must invest in both. To orchestrate dynamic capabilities required by innovation, organizational models, roles, and management methods need to be revised.