Enhancing risk management in inclusive ESG investment portfolios in financial markets through big data analysis

Abstract

Corporate ethics, sustainability, and social responsibility are essential to the future of finance. Businesses are releasing more information on their environmental, social, and governance (ESG) performance to stay competitive. In general, there is a negative correlation between volatility and higher ESG ratings as well as value and profitability. Investors are irritated by the lack of standardized ESG data, albeit as a result of the quick expansion of ESG investments, which has increased the amount of ESG data and rating providers. There are several measures and parameters offered by ESG rating providers, which can cause issuers to become exhausted from reporting on sustainability concerns. Conversely, the complexity of various ESG measures makes it difficult to make direct comparisons between businesses, especially when comparing them in different sectors. Emerging artificial intelligence capabilities can close the ESG disclosure gap and offer new perspectives on the ESG data that is currently available, which could help address these issues. The objective of this project was to automatically extract and analyze company ESG initiatives and keywords from ESG reports, as well as news and articles from other parties, using the Tunicate Swarm Optimized Dynamic Support Vector Machine (TSO-DSVM). With this method, an internal ESG score that is more accurate and understandable could be generated. The relationship between this internal ESG score and market risk in inclusive ESG investing portfolios will be evaluated by integrating it into the study. The proposed method has performed well than the existing methods with an F1-score of 89.44%, recall of 88.65%, precision of 90.75%, and an accuracy of 92.54%. The findings suggest that decreased value at risk is favorably associated with an increased internal ESG score, which improves risk management tactics for investors in the ESG sector.

Keywords

risk management ESG investment portfolios financial markets Tunicate Swarm Optimized Dynamic Support Vector Machine (TSO-DSVM)

Get full access to this article

View all access options for this article.

References

Young-Ferris

Roberts

. Looking for something that isn’t there’: a case study of an early attempt at ESG integration in investment decision making. Eur Account Rev 2023; 32(3): 717–744. DOI: 10.1080/09638180.2021.2000458.

Chen

Song

Gao

. Environmental, social, and governance (ESG) performance and financial outcomes: analyzing the impact of ESG on financial performance. J Environ Manag 2023; 345: 118829. DOI: 10.1016/j.jenvman.2023.118829.

Landi

Iandolo

Renzi

, et al. Embedding sustainability in risk management: the impact of environmental, social, and governance ratings on corporate financial risk. Corp Soc Responsib Environ Manag 2022; 29(4): 1096–1107. DOI: 10.1002/csr.2256.

Raghunandan

Rajgopal

. Do ESG funds make stakeholder-friendly investments? Rev Account Stud 2022; 27(3): 822–863. DOI: 10.1007/s11142-022-09693-1.

Billio

Costola

Hristova

, et al. Inside the ESG ratings:(Dis) agreement and performance. Corp Soc Responsib Environ Manag 2021; 28(5): 1426–1445. DOI: 10.1002/csr.2177.

Krueger

Sautner

Tang

, et al. The effects of mandatory ESG disclosure around the world. J Account Res 2021; 62: 1795–1847. DOI: 10.1111/1475-679X.12548.

Hughes

Urban

Wójcik

. Alternative ESG ratings: how technological innovation is reshaping sustainable investment. Sustainability 2021; 13(6): 3551. DOI: 10.3390/su13063551.

Nguyen

. Do the ESG factors truly enhance real estate companies’ valuation and performance in uncertain times. J Property Invest Finance 2024. DOI: 10.1108/JPIF-03-2024-0036.

Mandas

Lahmar

Piras

, et al.

ESG in the financial industry: what matters for rating analysts?

Res Int Bus Finance 2023; 66: 102045. DOI: 10.1016/j.ribaf.2023.102045.

10.

Zhang

. Renovation in environmental, social and governance (ESG) research: the application of machine learning. Asian Rev Account 2023; 32: 554–572. DOI: 10.1108/ARA-07-2023-0201.

11.

MacNeil

Esser

. From a financial to an entity model of ESG. Eur Bus Org Law Rev 2022; 23(1): 9–45. DOI: 10.1007/s40804-021-00234-y.

12.

Lim

. Environmental, social, and governance (ESG) and artificial intelligence in finance: state-of-the-art and research takeaways. Artif Intell Rev 2024; 57(4): 76. DOI: 10.1007/s10462-024-10708-3.

13.

D’Amato

D’Ecclesia

Levantesi

. Fundamental ratios as predictors of ESG scores: a machine learning approach. Decisions Econ Finan 2021; 44(2): 1087–1110. DOI: 10.1007/s10203-021-00364-5.

14.

D’Amato

D’Ecclesia

Levantesi

. ESG score prediction through random forest algorithm. Comput Manag Sci 2022; 19(2): 347–373. DOI: 10.1007/s10287-021-00419-3.

15.

Saxena

Singh

Gehlot

, et al. Technologies empowered environmental, social, and governance (ESG): an industry 4.0 landscape. Sustainability 2022; 15(1): 309. DOI: 10.3390/su15010309.

16.

Mansouri

Momtaz

. Financing sustainable entrepreneurship: ESG measurement, valuation, and performance. J Bus Ventur 2022; 37(6): 106258. DOI: 10.1016/j.jbusvent.2022.106258.

17.

Fang

Nie

Shen

. Can enterprise digitization improve ESG performance? Econ Modell 2023; 118: 106101. DOI: 10.1016/j.econmod.2022.106101.

18.

Jonsdottir

Sigurjonsson

Johannsdottir

, et al. Barriers to using ESG data for investment decisions. Sustainability 2022; 14(9): 5157. DOI: 10.3390/su14095157.

19.

Yang

Fei

, et al. Regularization methods for sparse ESG-valued multi-period portfolio optimization with return prediction using machine learning. Expert Syst Appl 2023; 232: 120850. DOI: 10.1016/j.eswa.2023.120850.

20.

Zhang

Chen

. Socially responsible investment portfolio construction with a double‐screening mechanism considering machine learning prediction. Discrete Dynam Nat Soc 2021; 2021(1): 7390887–7390915. DOI: 10.1155/2021/7390887.

21.

Lee

Joo

Choi

, et al. Proposing an integrated approach to analyzing ESG data via machine learning and deep learning algorithms. Sustainability 2022; 14(14): 8745. DOI: 10.3390/su14148745.

22.

D'Amato

D'Ecclesia

Levantesi

. Firms’ profitability and ESG score: a machine learning approach. Appl Stoch Model Bus Ind 2024; 40(2): 243–261. DOI: 10.1002/asmb.2758.

23.

Del Vitto

Marazzina

Stocco

. ESG ratings explainability through machine learning techniques. Ann Oper Res 2023; 19: 1–30. DOI: 10.1007/s10479-023-05514-z.

24.

Momparler

Carmona

Climent

. Catalyzing sustainable investment: revealing ESG power in predicting fund performance with machine learning. Comput Econ 2024; 1–26. DOI: 10.1007/s10614-024-10618-0.

25.

Citterio

King

. The role of Environmental, Social, and Governance (ESG) in predicting bank financial distress. Finance Res Lett 2023; 51: 103411. DOI: 10.1016/j.frl.2022.103411.

26.

. Using machine learning approach to evaluate the excessive financialization risks of trading enterprises. Comput Econ 2022; 59(4): 1607–1625. DOI: 10.1007/s10614-020-10090-6.