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

Yıl 2024, Cilt: 10 Sayı: 2, 459 - 517, 31.12.2024

Ömür Saltık

https://doi.org/10.29132/ijpas.1540178

Öz

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.

Anahtar Kelimeler

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

Öz

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.

Anahtar Kelimeler

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

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