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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Autores principales:	Abdel Razzaq Al Rababa’a, Zaid Saidat, Raed Hendawi
Formato:	article
Lenguaje:	EN
Publicado:	LLC "CPC "Business Perspectives" 2021
Materias:	artificial neural networks COVID-19 linear models predicting stock returns Finance HG1-9999
Acceso en línea:	https://doaj.org/article/ff17b2fe100349c6bf0330a10ad06677
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spelling	oai:doaj.org-article:ff17b2fe100349c6bf0330a10ad066772021-12-01T11:50:05ZForecasting stock returns on the Amman Stock Exchange: Do neural networks outperform linear regressions?10.21511/imfi.18(4).2021.241810-49671812-9358https://doaj.org/article/ff17b2fe100349c6bf0330a10ad066772021-12-01T00:00:00Zhttps://www.businessperspectives.org/images/pdf/applications/publishing/templates/article/assets/15871/IMFI_2021_04_Rababa’a.pdfhttps://doaj.org/toc/1810-4967https://doaj.org/toc/1812-9358Different 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).Abdel Razzaq Al Rababa’aZaid SaidatRaed HendawiLLC "CPC "Business Perspectives"articleartificial neural networksCOVID-19linear modelspredicting stock returnsFinanceHG1-9999ENInvestment Management & Financial Innovations , Vol 18, Iss 4, Pp 280-296 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	artificial neural networks COVID-19 linear models predicting stock returns Finance HG1-9999
spellingShingle	artificial neural networks COVID-19 linear models predicting stock returns Finance HG1-9999 Abdel Razzaq Al Rababa’a Zaid Saidat Raed Hendawi Forecasting stock returns on the Amman Stock Exchange: Do neural networks outperform linear regressions?
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).
format	article
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
title	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_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_sort	forecasting stock returns on the amman stock exchange: do neural networks outperform linear regressions?
publisher	LLC "CPC "Business Perspectives"
publishDate	2021
url	https://doaj.org/article/ff17b2fe100349c6bf0330a10ad06677
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Forecasting stock returns on the Amman Stock Exchange: Do neural networks outperform linear regressions?

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