Description
Title: Saudi Green Banks and the Volatility of Stock Returns: GLE and Neural Network Models
Abstract: Using linear regression, GLE algorithm, and neural network models, this study examines the effects of ESG factors on stock return volatility from 2012 to 2020. As independent variables, this study employed ESG factors and key control variables (ROA, EPS, and year). The results of the regression model demonstrated that both year and E scores significantly influenced the stock return volatility of Saudi banks. However, the S score and ROA had a negative influence on volatility. The results indicated that the prediction models were more effective at analyzing volatility and developing an accurate prediction model by incorporating all independent variables. Based on the results of the GLE algorithm model, the significance of the variables was ordered from most significant to least significant as follows: S score, ROA, E score, and then G score. While the neural network’s output was ordered as ROA, ROE, and EPS, the E score, S score, and G score factors had the same minor importance in predicting stock return volatility. Models of linear regression and prediction indicated that the S score was the most important variable for predicting the volatility of stock return. Policymakers and investors can both benefit from our research.
Keywords: ESG; Saudi banking sector; environmental score; social score; governance score; volatility
Paper Quality: SCOPUS / Web of Science Level Research Paper
Subject: Economics
Writer Experience: 20+ Years
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