Forecasting stock returns on the Amman Stock Exchange: Do neural networks outperform linear regressions?
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 art...
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| Format: | Article |
| Language: | English |
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LLC "CPC "Business Perspectives"
2021-12-01
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| Series: | Investment Management & Financial Innovations |
| Subjects: | |
| Online Access: | https://www.businessperspectives.org/images/pdf/applications/publishing/templates/article/assets/15871/IMFI_2021_04_Rababa’a.pdf |
| _version_ | 1818880892207104000 |
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| author | Abdel Razzaq Al Rababa’a Zaid Saidat Raed Hendawi |
| author_facet | Abdel Razzaq Al Rababa’a Zaid Saidat Raed Hendawi |
| author_sort | Abdel Razzaq Al Rababa’a |
| collection | DOAJ |
| description | 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). |
| first_indexed | 2024-12-19T14:53:11Z |
| format | Article |
| id | doaj.art-ff17b2fe100349c6bf0330a10ad06677 |
| institution | Directory Open Access Journal |
| issn | 1810-4967 1812-9358 |
| language | English |
| last_indexed | 2024-12-19T14:53:11Z |
| publishDate | 2021-12-01 |
| publisher | LLC "CPC "Business Perspectives" |
| record_format | Article |
| series | Investment Management & Financial Innovations |
| spelling | doaj.art-ff17b2fe100349c6bf0330a10ad066772022-12-21T20:16:47ZengLLC "CPC "Business Perspectives"Investment Management & Financial Innovations1810-49671812-93582021-12-0118428029610.21511/imfi.18(4).2021.2415871Forecasting stock returns on the Amman Stock Exchange: Do neural networks outperform linear regressions?Abdel Razzaq Al Rababa’a0Zaid Saidat1https://orcid.org/0000-0003-4866-4765Raed Hendawi2https://orcid.org/0000-0002-2166-2287Assistant Professor of Banking and Finance, Faculty of Economics and Administrative Sciences, Yarmouk UniversityAssistant Professor, Faculty of Business, Department of Finance and Banking Sciences, Applied Science Private UniversityAssistant Professor of Finance, Faculty of Economics and Administrative Sciences, Yarmouk UniversityDifferent 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).https://www.businessperspectives.org/images/pdf/applications/publishing/templates/article/assets/15871/IMFI_2021_04_Rababa’a.pdfartificial neural networksCOVID-19linear modelspredicting stock returns |
| spellingShingle | Abdel Razzaq Al Rababa’a Zaid Saidat Raed Hendawi Forecasting stock returns on the Amman Stock Exchange: Do neural networks outperform linear regressions? Investment Management & Financial Innovations artificial neural networks COVID-19 linear models predicting stock returns |
| title | Forecasting stock returns on the Amman Stock Exchange: Do neural networks outperform linear regressions? |
| title_full | Forecasting stock returns on the Amman Stock Exchange: Do neural networks outperform linear regressions? |
| title_fullStr | Forecasting stock returns on the Amman Stock Exchange: Do neural networks outperform linear regressions? |
| title_full_unstemmed | Forecasting stock returns on the Amman Stock Exchange: Do neural networks outperform linear regressions? |
| title_short | Forecasting stock returns on the Amman Stock Exchange: Do neural networks outperform linear regressions? |
| title_sort | forecasting stock returns on the amman stock exchange do neural networks outperform linear regressions |
| topic | artificial neural networks COVID-19 linear models predicting stock returns |
| url | https://www.businessperspectives.org/images/pdf/applications/publishing/templates/article/assets/15871/IMFI_2021_04_Rababa’a.pdf |
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