Which Factors Will Affect the ESG Index in the USA and Europe: Stock, Crude Oil, or Gold?

Liu, Tiantian; Nakajima, Tadahiro; Hamori, Shigeyuki

doi:10.1007/978-981-16-2990-7_4

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…In the COVID-19 pandemic, people have comprehended the concept of ESG through direct experience, and this experience also affected the view of the investors [6,7]. The ESG index is now vital for both companies and investors since investors consider it as an important factor in investment decisions [9,10], which influences the future performance of the companies. Figure 1 compares the return on investment of ESG-related funds and non-ESG funds.…”

Section: Introduction 1backgroundmentioning

confidence: 99%

Proposing an Integrated Approach to Analyzing ESG Data via Machine Learning and Deep Learning Algorithms

et al. 2022

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In the COVID-19 era, people face situations that they have never experienced before, which alerted the importance of the ESG. Investors also consider ESG indexes as an essential factor for their investments, and some research yielded that the return on sustainable funds is more significant than on non-sustainable ones. Nevertheless, a deficiency in research exists about analyzing ESG through artificial intelligence algorithms due to adversity in collecting ESG-related datasets. Therefore, this paper suggests integrated AI approaches to the ESG datasets with the five different experiments. We also focus on analyzing the governance and social datasets through NLP algorithms and propose a straightforward method for predicting a specific firm’s ESG rankings. Results were evaluated through accuracy score, RMSE, and MAE, and every experiment conducted relevant scores that achieved our aim. From the results, it could be concluded that this paper successfully analyzes ESG data with various algorithms. Unlike previous related research, this paper also emphasizes the importance of the adversarial attacks on the ESG datasets and suggests methods to detect them effectively. Furthermore, this paper proposes a simple way to predict ESG rankings, which would be helpful for small businesses. Even though it is our limitation that we only use restricted datasets, our research proposes the possibility of applying the AI algorithms to the ESG datasets in an integrated approach.

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Section: Introduction 1backgroundmentioning

confidence: 99%

Proposing an Integrated Approach to Analyzing ESG Data via Machine Learning and Deep Learning Algorithms

et al. 2022

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“…The literature is replete with instances indicating that shock or crude oil fluctuations impact ESG worldwide. Liu et al (2021) find that US and European ESG indices are affected by the volatility of stocks, gold, and crude oil. The shares and gold volatilities see the maximum impact.…”

Section: Crude Oil Prices and Esgmentioning

confidence: 86%

Fossil Fuel-Based versus Electric Vehicles: A Volatility Spillover Perspective Regarding the Environment

Rastogi,

Kanoujiya,

Singh

et al. 2023

JRFM

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Due to environmental concerns, electric vehicles (EVs) are gaining traction over fossil fuel-based vehicles. For electronic devices, including vehicles, copper is the key material used for building. This situation draws attention to the impact of copper prices, crude oil prices, and exchange rates on the economic viability of using EVs over fossil fuels. We use the volatility spillover effect (VSE) to determine the financial viability of these two types of vehicles in the context of environmental issues. Daily data on copper prices, crude oil, exchange rate, and the BSE100 ESG (“Bombay Stock Exchange 100 Environmental, Social and Governance”) index are taken from 1 November 2017 to 20 September 2022. Two popular multivariate GARCH (“Multivariate Generalized Autoregressive Conditional Heteroscedasticity”) family models, i.e., the BEKK (“Baba–Engle–Kraft–Kroner”)-GARCH (BG) and DCC (“Dynamic Conditional Correlation”)-GARCH (DG) models, are utilized to find volatility connections between these variables. These are appropriate GARCH models to observe the volatility dependence of one market on another market. It is found that there exist volatility effects of copper and exchange rate on the S&P BSE100 ESG Equity Index Price, which we will refer to here as ESG. However, crude oil is found to be insignificant for ESG. The novelty of this study is in the use of volatility spillover to determine economic viability. The volatility effects of copper prices are positive for ESG in the short run and negative for long-term volatility. The exchange rate has a positive volatility effect on ESG in the long run. Surprisingly, we find that EVs are technologically better than fossil fuel-based vehicles as a possible sustainable energy source. We observe studies that have raised similar concerns about EVs’ lack of business sense compared to fossil fuels. However, using VSE to explore financial viability offers a fresh perspective. Based on the findings of the current study, it is recommended that policymakers and researchers revisit their support for EVs as an alternate and sustainable source of energy.

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Connectedness and Portfolios in Emerging Markets: ESG Leaders vs. Conventional Indexes

de Boyrie,

Pavlova

2023

Preprint

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Which Factors Will Affect the ESG Index in the USA and Europe: Stock, Crude Oil, or Gold?

Cited by 3 publications

References 19 publications

Proposing an Integrated Approach to Analyzing ESG Data via Machine Learning and Deep Learning Algorithms

Proposing an Integrated Approach to Analyzing ESG Data via Machine Learning and Deep Learning Algorithms

Fossil Fuel-Based versus Electric Vehicles: A Volatility Spillover Perspective Regarding the Environment

Connectedness and Portfolios in Emerging Markets: ESG Leaders vs. Conventional Indexes

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