Saudi Green Banks and Stock Return Volatility: GLE Algorithm and Neural Network Models

Assous, Hamzeh F. (2022) Saudi Green Banks and Stock Return Volatility: GLE Algorithm and Neural Network Models. Economies, 10 (10). p. 242. ISSN 2227-7099

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Abstract

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.

Item Type: Article
Subjects: STM Academic > Multidisciplinary
Depositing User: Unnamed user with email support@stmacademic.com
Date Deposited: 21 Jun 2023 10:28
Last Modified: 30 Oct 2023 05:20
URI: http://article.researchpromo.com/id/eprint/1126

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