The Impact of ESG Ratings on the Systemic Risk of European Blue-Chip Firms

Eratalay, Mustafa Hakan; Ángel, Ariana Paola Cortés

doi:10.2139/ssrn.4033739

Cited by 8 publications

(2 citation statements)

References 20 publications

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“…Besides, Hoepner et al [5] presented significant adverse effects of a high environment pillar score on the downside risk. Lastly, Eratalay and Angel's [6] study concluded with an insignificant effect of the environment pillar score on the volatility of index return on the sample used.…”

Section: 1mentioning

confidence: 93%

Developing Machine Learning Techniques to Investigate the Impact of Air Quality Indices on Tadawul Exchange Index

et al. 2022

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The air quality index (AQI) can be described using different pollutant indices. Many investigators study the effect of stock prices and air quality in the field to show if there is a relationship between changing the stock market and the concentration of various pollutants. This study aims to find a relationship between Saudi Tadawul All Share Index (TASI) and multiple pollutants, including particulate matter (PM10), ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), and AQI. Based on tree models, the relationship is created using linear regression and two prediction models, Chi-square Automatic Interaction Detection (CHAID), and CR-Tree. In order to achieve the target of this research, the TASI dataset relates to six pollutants using time; afterward, the new dataset is divided into three parts—test, validate, and train—after eliminating the outlier data. In order to test the performance of two prediction models, R2 and various error functions are used. The linear regression model results found that PM10, NO2, CO, month, day, and year are significant, whereas O3, SO2, and AQI indices are insignificant. The test dataset showed that R2 scores are 0.995 and 0.986 for CR-Tree and CHAID, respectively, with RMSE values of 387 and 227 for CR-Tree and CHAID, respectively. The prediction models showed that the CHAID model performed better than CR-Tree because it used only three indices, namely, PM10, AQI, and O3, with year and month. The results indicated an effect between changing TASI and the three pollutants, PM10, AQI, and O3.

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Section: 1mentioning

confidence: 93%

Developing Machine Learning Techniques to Investigate the Impact of Air Quality Indices on Tadawul Exchange Index

et al. 2022

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“…Thus, many studies have indicated that companies should consider risks in a holistic perspective and separately for each business unit in companies (Teng et al, 2021;Gangi et al, 2020). This may eventually lead to adopting a portfolio approach of risk management in companies (Verheyden et al, 2016;Eratalay & Ángel, 2022;Samyukth, 2021). Moreover, the need for adopting a risk-based approach in companies during the past decade has led to an increased interest in defining and evaluating risks and taking them as guidance in making decisions related to the company (Khorin, & Krikunov, 2021).…”

Section: Second: the Reputational Risksmentioning

confidence: 99%

impact of ESG strategies integration on the operational and reputational risks

Al-Qaruty¹,

Alhorani²,

Dabbaghia³

et al. 2022

ijhs

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This study aimed to demonstrate the impact of strategies integration of environmental, social and corporate governance strategies (ESG) on operational and reputational risks: a literature review for insurance companies listed on the Amman Stock Exchange. where the study population consisted of 22 Jordanian insurance companies, while the sample consisted of 8 companies, and one was excluded due to the lack of data, and based on the descriptive analytical approach, the study reviewed many previous studies related to its subject, and using the method of content analysis for annual reports and disclosures issued by Major insurance companies during the period 2020-2021, where a survey list was designed to measure the extent of the research companies' commitment to environmental, social and corporate governance strategies. The study concluded with many results, perhaps the most prominent of which is the presence of disclosure about social responsibility activities in the annual reports, and that all companies focused in their activities on providing donations and support to the local community and supporting education and health issues during the Covid pandemic period,,,

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

Assous

2022

Economies

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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.

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The Impact of ESG Ratings on the Systemic Risk of European Blue-Chip Firms

Cited by 8 publications

References 20 publications

Developing Machine Learning Techniques to Investigate the Impact of Air Quality Indices on Tadawul Exchange Index

Developing Machine Learning Techniques to Investigate the Impact of Air Quality Indices on Tadawul Exchange Index

impact of ESG strategies integration on the operational and reputational risks

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

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