Saudi Green Banks and Stock Return Volatility: GLE Algorithm and Neural Network Models
This study investigates the effects of ESG factors on stock return volatility from 2012 to 2020 using linear regression, GLE algorithm, and neural network models. This paper used the ESG factors and main control variables (ROA, EPS, and year) as independent variables. The regression model results sh...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Economies |
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| Online Access: | https://www.mdpi.com/2227-7099/10/10/242 |
| _version_ | 1797473655812259840 |
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| author | Hamzeh F. Assous |
| author_facet | Hamzeh F. Assous |
| author_sort | Hamzeh F. Assous |
| collection | DOAJ |
| description | This study investigates the effects of ESG factors on stock return volatility from 2012 to 2020 using linear regression, GLE algorithm, and neural network models. This paper used the ESG factors and main control variables (ROA, EPS, and year) as independent variables. The regression model results showed that both year and E scores significantly positively affected Saudi banks’ stock return volatility. However, the S score and ROA significantly negatively impacted the volatility. The results indicated that the prediction models were more efficient in analysing the volatility and building an accurate prediction model using all independent variables. The results of the GLE algorithm model showed that the level of importance of the variables was sorted from highest to least significant as follows: S score, ROA, E score, and then G score. While the result of the neural network was sorted as ROA, ROE, and EPS, then the E score, S score, and G score factors all had the same minor importance in predicting the stock return volatility. Linear regression and prediction models indicated that the S score was the most crucial variable in predicting stock return volatility. Both policymakers and investors can benefit from our findings. |
| first_indexed | 2024-03-09T20:20:26Z |
| format | Article |
| id | doaj.art-71957ff5fc16400a99d3d4631ee6a627 |
| institution | Directory Open Access Journal |
| issn | 2227-7099 |
| language | English |
| last_indexed | 2024-03-09T20:20:26Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Economies |
| spelling | doaj.art-71957ff5fc16400a99d3d4631ee6a6272023-11-23T23:50:26ZengMDPI AGEconomies2227-70992022-10-01101024210.3390/economies10100242Saudi Green Banks and Stock Return Volatility: GLE Algorithm and Neural Network ModelsHamzeh F. Assous0Finance Department, School of Business, King Faisal University, Al Ahasa 31982, Saudi ArabiaThis study investigates the effects of ESG factors on stock return volatility from 2012 to 2020 using linear regression, GLE algorithm, and neural network models. This paper used the ESG factors and main control variables (ROA, EPS, and year) as independent variables. The regression model results showed that both year and E scores significantly positively affected Saudi banks’ stock return volatility. However, the S score and ROA significantly negatively impacted the volatility. The results indicated that the prediction models were more efficient in analysing the volatility and building an accurate prediction model using all independent variables. The results of the GLE algorithm model showed that the level of importance of the variables was sorted from highest to least significant as follows: S score, ROA, E score, and then G score. While the result of the neural network was sorted as ROA, ROE, and EPS, then the E score, S score, and G score factors all had the same minor importance in predicting the stock return volatility. Linear regression and prediction models indicated that the S score was the most crucial variable in predicting stock return volatility. Both policymakers and investors can benefit from our findings.https://www.mdpi.com/2227-7099/10/10/242ESGSaudi banking sectorenvironmental scoresocial scoregovernance scorevolatility |
| spellingShingle | Hamzeh F. Assous Saudi Green Banks and Stock Return Volatility: GLE Algorithm and Neural Network Models Economies ESG Saudi banking sector environmental score social score governance score volatility |
| title | Saudi Green Banks and Stock Return Volatility: GLE Algorithm and Neural Network Models |
| title_full | Saudi Green Banks and Stock Return Volatility: GLE Algorithm and Neural Network Models |
| title_fullStr | Saudi Green Banks and Stock Return Volatility: GLE Algorithm and Neural Network Models |
| title_full_unstemmed | Saudi Green Banks and Stock Return Volatility: GLE Algorithm and Neural Network Models |
| title_short | Saudi Green Banks and Stock Return Volatility: GLE Algorithm and Neural Network Models |
| title_sort | saudi green banks and stock return volatility gle algorithm and neural network models |
| topic | ESG Saudi banking sector environmental score social score governance score volatility |
| url | https://www.mdpi.com/2227-7099/10/10/242 |
| work_keys_str_mv | AT hamzehfassous saudigreenbanksandstockreturnvolatilityglealgorithmandneuralnetworkmodels |