Algorithmic Approaches to Unraveling ESGC Impact on Financial Performance Based on the Ethical Investing in the Energy Sector

Abstract

This study investigates the connection between Environmental, Social, Governance, and Controversies (ESGC) scores and financial performance in the energy sector across 27 countries and 521 firms from 2000 to 2021 through algorithms and machine learning techniques. The research employs advanced machine learning methods such as clustering analysis (k-means, DBSCAN, Hierarchical Clustering, and Affinity Propagation), violin plots, co-occurrence analysis, and regression techniques to ex-amine the effects of sustainable practices on corporate financial outcomes using a dataset with 640 variables. The study highlights sector-specific and geographical differences in the effectiveness of ESGC practices and demonstrates a positive asso-ciation between high ESGC scores and better financial indicators, such as profitabil-ity, earnings quality, liquidity ratios, and leverage. By presenting a strong case for incorporating ESGC scores into investment strategies, policy formulation, and cor-porate governance, the study suggests that responsible and ethical practices lead to enhanced operational efficiencies and better risk-adjusted returns. The findings, an-alyzed using machine learning techniques, indicate that companies with strong ESG practices have higher earnings quality and profitability, showing that sustainable practices benefit not only the environment and society but also the financial base line.

Keywords

• machine learning
• clustering and regression algorithms
• environmental social governance and controversies (ESGC) scores
• ethical investing
• energy sector

Enerji Sektöründe Etik Yatırıma Dayalı Finansal Performans Üzerindeki ESGC Etkisini Açığa Çıkarmada Algoritmik Yaklaşımlar

Abstract

Bu çalışma, 27 ülkede ve 521 firmada, 2000'den 2021'e kadar enerji sektöründe Çevresel, Sosyal, Yönetişim ve Tartışmalar (ESGC) skorları ile finansal performans arasındaki bağlantıyı algoritmalar ve makine öğrenmesi teknikleri aracılığıyla araştırmaktadır. Çalışmada, kümeleme analizi (k-means, DBSCAN, Hiyerarşik Kümeleme ve Affinity Propagation), keman grafikleri, eşgörü analizi ve regresyon teknikleri gibi gelişmiş makine öğrenmesi yöntemleri kullanılarak, sürdürülebilir uygulamaların kurumsal finansal sonuçlar üzerindeki etkileri 640 değişkenlik bir veri seti üzerinden incelenmektedir. Araştırma, ESGC uygulamalarının etkinliğinde sektöre özgü ve coğrafi farklılıkları vurgulamakta ve yüksek ESGC skorları ile daha iyi finansal göstergeler, örneğin kârlılık, kazanç kalitesi, likidite oranları ve kaldıraç gibi, arasında pozitif bir ilişki olduğunu ortaya koymaktadır. ESGC skorlarının yatırım stratejilerine, politika oluşturmaya ve kurumsal yönetişime entegre edilmesi gerektiği yönünde güçlü bir argüman sunan çalışma, sorumlu ve etik uygulamaların gelişmiş operasyonel verimliliklere ve daha iyi risk uyarlamalı getirilere yol açtığını öne sürmektedir. Bulgular, makine öğrenmesi teknikleriyle analiz edilen veriler ışığında, güçlü ESGC uygulamalarına sahip şirketlerin daha yüksek kazanç kalitesi ve kârlılığa sahip olduğunu, sürdürülebilir uygulamaların sadece çevre ve topluma değil, aynı zamanda finansal temele de fayda sağladığını göstermektedir.

Keywords

• makine öğrenmesi
• kümeleme ve regresyon algortimaları
• çevresel, sosyal, yönetişim ve tartışmalar (ESGC) skorları
• etik yatırım
• enerji sektörü

References

1. Agrawal, A., and Hockerts, K. (2021). Impact investing: Review and research agenda. Journal of Small Business & Entrepreneurship, 33(2), 153-181.
2. Akbulut, M. E. and Uçar, E. (2019). Local Religion and Insider Trading. Accessed June 11, 2023. https://www.researchgate.net/publication/337199728_Local_Religion_and_Insider_Trading. DOI:10.13140/RG.2.2.29206.70721. pp.1-36.
3. Altelbany, S. (2021). Evaluation of ridge, elastic net and lasso regression methods in precedence of multicollinearity problem: A simulation study. Journal of Applied Economics and Business Studies, 5(1), 131-142.
4. Barber, B. M., Morse, A., and Yasuda, A. (2021). Impact investing. Journal of Financial Eco-nomics, 139 (1), 162-185.
5. Berk, J. B. and van Binsbergen, J. H. (2021). The Impact of Impact Investing (August 21,2021). Stanford University Graduate School of Business Research Paper, Law & Economics Center at George Mason University Scalia Law School Research Paper Series No. 22-008, Accessed July 11, 2023. https://ssrn.com/abstract=3909166 or http://dx.doi.org/10.2139/ssrn.3909166
6. Berman, S. L., Wicks, A. C., Kotha, S., and Jones, T. M. (1999). Does stakeholder orienta-tionmatter? The relationship between stakeholder management models and firm financial performanc. Academy Of Management Journal, 42(5), 488-506.
7. Block, J. H., Hirschmann, M., and Fisch, C. (2021). Which criteria matter when impact investors screen social enterprises? Journal of Corporate Finance, 66, 101813.
8. Borker, D. R. (2013). Is there a favorable cultural profile for IFRS?: an examination and exten-sion of Gray's accounting value hypotheses. International Business & Economics Research Journal (IBER), 12(2), 167-178.

9. Chen, Z., and Xie, G. (2022). ESG disclosure and financial performance: Moderating role of ESG investors. International Review of Financial Analysis, 83, 102291.
10. Clarkson, P. M., Li, Y., Richardson, G. D., and Vasvari, F. P. (2008). Revisiting the relation between environmental performance and environmental disclosure: An empirical analysis. Accounting, organizations and society, 33(4-5), 303-327.
11. Consulting, H. (2010). Impact Investing Overview. In Money for Good. San Francisco, CA: Hope Consulting. Accessed July 30, 2023. http://www.hopeconsulting.us/wordpress/wp-content/uploads/2013/04/MFG1-Impact-Investing-Overview.pdf.
12. Correa da Cunha, H., Singh, V., and Farrell, C. (2023). Host country cultural profile and the performance of foreign subsidiaries in Latin America. International Journal of Cross Cultural Man-agement, 23(3), 531-555.
13. Cui, C., and Wang, D. (2016). High dimensional data regression using Lasso model and neural networks with random weights. Information Sciences, 372, 505-517.
14. de Jong, M., and Rocco, S. (2022). ESG and impact investing. Journal of Asset Management, 23(7), 547-549.
15. De Spiegeleer, J., Höcht, S., Jakubowski, D., Reyners, S., and Schoutens, W. (2023). ESG: A new dimension in portfolio allocation. Journal of Sustainable Finance & Investment, 13(2), 827-867.
16. de Souza, R. R., Toebe, M., Mello, A. C., and Bittencourt, K. C. (2023). Sample size and Shapiro-Wilk test: An analysis for soybean grain yield. European Journal of Agronomy, 142, 126666.
17. Díaz-Peña, L. D. C., Castillo Delgadillo, V. M., and Mario Iván, C. V. (2022). Financial firm’s performance: a comparative analysis based on ESG metrics and net zero legislation. Journal of Sus-tainable Finance & Investment, 1-21.
18. Elkington, J. (2018). 25 years ago I coined the phrase “triple bottom line.” Here’s why it’s time to rethink it. Harvard Business Review, 25, 2-5.
19. Erol, I., Unal, U., and Coskun, Y. (2023). ESG investing and the financial performance: A panel data analysis of developed REIT markets. Environmental Science and Pollution Research, 30(36), 85154-85169.
20. Escrig-Olmedo, E., Muñoz-Torres, M. J., and Fernandez-Izquierdo, M. A. (2010). Socially responsible investing: sustainability indices, ESG rating and information provider agencies. Interna-tional journal of sustainable economy, 2(4), 442-461.
21. Ester, M., Kriegel, H. P., Sander, J., & Xu, X. (1996, August). A density-based algorithm for discovering clusters in large spatial databases with noise. In kdd (Vol. 96, No. 34, pp. 226-231).
22. Frey, B. J., & Dueck, D. (2007). Clustering by passing messages between data points. science, 315(5814), 972-976.
23. Freeman, R. E. (1984). Strategic management: A stakeholder approach. Boston, MA: Pitman.
24. Friede, G., Busch, T., and Bassen, A. (2015). ESG and financial performance: aggregated evi-dence from more than 2000 empirical studies. Journal of sustainable finance & investment, 5(4), 210-233.
25. Friedman, M. (1970). The social responsibility of business is to increase its profits. New York Times Magazine, 32-33, 122-124. September 13, 1970.
26. Fulton, M., Kahn, B., and Sharples, C. (2012). Sustainable investing: Establishing long-term value and performance. Available at SSRN 2222740.
27. Gana, R. (2022). Ridge regression and the Lasso: how do they do as finders of significant re-gressors and their multipliers?. Communications in Statistics-Simulation and Computation, 51(10), 5738-5772.
28. González-Estrada, E., Villaseñor, J. A., and Acosta-Pech, R. (2022). Shapiro-Wilk test for mul-tivariate skew-normality. Computational Statistics, 37(4), 1985-2001.
29. Handayani, M. K. Y. (2019). The effect of ESG performance on economic performance in the high profile industry in Indonesia. J Int Bus Econ, 7, 112-121.
30. Hassan, K. M., Nahian Faisal Khan, A., and Ngow, T. (2010). Is faith‐based investing rewarding? The case for Malaysian Islamic unit trust funds. Journal of Islamic Accounting and Business Research, 1(2), 148-171.
31. Hintze, J. L., & Nelson, R. D. (1998). Violin plots: A box plot-density trace synergism. The American Statistician, 52(2), 181-184.
32. Hoerl, A. E., & Kennard, R. W. (1970). Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 12(1), 55-67.
33. Jain, A. K. (2010). Data clustering: 50 years beyond K-means. Pattern Recognition Letters, 31(8), 651-666.
34. Khan, M., Serafeim, G. and Yoon, A. S. (2016). Corporate Sustainability: First Evidence on Materiality. The Accounting Review, Vol. 91, No. 6, 1697-1724. Available at SSRN: https://ssrn.com/abstract=2575912.
35. Kotsantonis, S., Pinney, C. and Serafeim, C. (2016). ESG Integration in Investment Management: Myths and Realities, Journal of Applied Corporate Finance, Vol 28 No. 2.
36. Landi, G., and Sciarelli, M. (2019). Towards a more ethical market: the impact of ESG rating on corporate financial performance. Social Responsibility Journal, 15(1), 11-27.
37. Landier, A., and Lovo, S. (2020). ESG investing: How to optimize impact?. HEC Paris Research, Paper No. FIN-2020-1363.
38. Lesser, K., Rößle, F., and Walkshäusl, C. (2016). Socially responsible, green, and faith-based investment strategies: Screening activity matters!. Finance Research Letters, 16, 171-178.
39. Liu, P., Zhu, B., Yang, M., and Chu, X. (2022). ESG and financial performance: A qualitative comparative analysis in China's new energy companies. Journal of Cleaner Production, 379, 134721.
40. Lydenberg, S., Michael, M., Burckart, W. and Clark, M. (2018). Why and How Investors Can Respond To Income Inequality, UNPRI.
41. Macqueen, J. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of 5-th Berkeley Symposium on Mathematical Statistics and Probability/University of California Press.
42. Maimon, O., & Rokach, L. (2005). Decomposition methodology for knowledge discovery and data mining (pp. 981-1003). Springer US.
43. Murtagh, F., & Contreras, P. (2012). Algorithms for hierarchical clustering: an overview. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2(1), 86-97.
44. Mansour, W., and Jlassi, M. (2014). The effect of religion on financial and investing decisions. Investor behavio. The psychology of financial planning and investing, 135-151. https://doi.org/10.1002/9781118813454.ch8
45. Ogutu, J. O., Schulz-Streeck, T., and Piepho, H. P. (2012). Genomic selection using regularized linear regression models: ridge regression, lasso, elastic net and their extensions. BioMed Central proceedings, Vol. 6, 1-6.
46. Pedersen, L. H., Fitzgibbons, S., and Pomorski, L. (2021). Responsible investing: The ESG- efficient frontier. Journal of Financial Economics, 142(2), 572-597.
47. Porter, M. E., and Van der Linde, C. V. D. (1995). Toward a new conception of the environment competitiveness relationship. Journal Of Economic Perspectives, 9(4), 97-118.
48. Porter, B. E., and Steen, T. P. (2006). Investing in stocks: three models of faith integration. Managerial Finance, 32(10), 812-821.
49. Revelli, C., and Viviani, J. L. (2015). Financial performance of socially responsible investing (SRI): what have we learned? A meta‐analysis. Business Ethics. A European Review, 24(2), 158-185.
50. Rodriguez-Fernandez, M. (2016). Social responsibility and financial performance: The role of good corporate governance. BRQ Business Research Quarterly, 19(2), 137-151.
51. Saleh, A. M. E., Arashi, M., and Kibria, B. G. (2019). Theory of ridge regression estimation with Applications. John Wiley & Sons.
52. Saltuk, Y., Bouri, A., and Leung, G. (2011). Insight into the impact investment market. JP Morgan Social Finance Research. 1-30. Accessed September 30, 2023. https://thegiin.org/assets/documents/Insight%20into%20Impact%20Investment%20Market2.pdf
53. Schwartz, M. S. (2003). The" ethics" of ethical investing. Journal of Business Ethics, 43, 195-213.
54. Schröder, M. (2014). Financial effects of corporate social responsibility: a literature re-view. Journal of Sustainable Finance & Investment, 4(4), 337-350.
55. Schubert, E., Sander, J., Ester, M., Kriegel, H. P., & Xu, X. (2017). DBSCAN revisited, revisited: why and how you should (still) use DBSCAN. ACM Transactions on Database Systems (TODS), 42(3), 1-21.
56. Sparkes, R. (2002). Socially responsible investment: A global revolution. John Wiley & Sons.
57. Stavroyiannis, S., and Babalos, V. (2017). Herding, faith-based investments and the global financial crisis: Empirical evidence from static and dynamic models” Journal of Behavioral Finance, 18(4), 478-489.
58. Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society Series B: Statistical Methodology, 58(1), 267-288.
59. United Nations Principles for Responsible Investment. (2006). Principles for Responsible In-vestment. Retrieved from https://www.unpri.org
60. Verheyden, Tim, Eccles, Robert G. and Feiner, Andreas. (2016). ESG for All? The Impact of ESG Screening on Return, Risk, and Diversification. Journal of Applied Corporate Finance, Vol 28 No. 2.
61. Weston, P., and Nnadi, M. (2023). Evaluation of strategic and financial variables of corporate sustainability and ESG policies on corporate finance performance. Journal of Sustainable Finance & Investment, 13(2), 1058-1074.
62. Yang, X., and Wen, W. (2018). Ridge and lasso regression models for cross-version defect Prediction. IEEE Transactions on Reliability, 67(3), 885-896.
63. Yazici, B., and Yolacan, S. (2007). A comparison of various tests of normality. Journal of Sta-tistical Computation and Simulation, 77(2), 175-183.
64. Zehir, E., and Aybars, A. (2020). Is there any effect of ESG scores on portfolio performance? Evidence from Europe and Turkey. Journal of Capital Markets Studies, 4(2), 129-143.
65. Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society Series B: Statistical Methodology, 67(2), 301-320.