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K-means and agglomerative hierarchical clustering analysis of esg scores, yearly variations, and stock returns: insights from the energy sector in Europe and the United States

Author

Listed:
  • Ștefan Rusu

    (University of Oradea, Oradea, Romania)

  • Marcel Ioan Boloș

    (University of Oradea, Oradea, Romania)

  • Marius Leordeanu

    (Polytechnic University of Bucharest, Bucharest, Romania; The Institute of Mathematics of the Romanian Academy, Bucharest, Romania)

Abstract

This study employs k-means clustering and agglomerative hierarchical clustering techniques to visually examine the potential relationship between Environmental Social and Governance (ESG) scores, their year-over-year variations, and annual stock returns for a sample of 34 energy sector companies operating in Europe and the United States. While the agglomerative hierarchical clustering dendrogram suggests two clusters, the elbow method of the k-means algorithm suggests 2-4 clusters. The results indicate that neither ESG scores nor their year-on-year variations had an impact on the annual returns of the stocks. The conclusion is further confirmed by the Pearson correlation coefficient. However, the ESG scores of European energy companies show a tighter dispersion and smaller year-over-year change, making them more predictable ESG score-wise and thus, potentially, more attractive to ESG-driven investors.

Suggested Citation

  • Ștefan Rusu & Marcel Ioan Boloș & Marius Leordeanu, 2023. "K-means and agglomerative hierarchical clustering analysis of esg scores, yearly variations, and stock returns: insights from the energy sector in Europe and the United States," Journal of Financial Studies, Institute of Financial Studies, vol. 8(Special-J), pages 166-180, June.
    • Handle: RePEc:fst:rfsisf:v:8:y:2023:i:special-june_2023:p:166-180
    DOI: 10.55654/JFS.2023.SP.11
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    References listed on IDEAS

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    More about this item

    Keywords

    Stock market; clustering; ESG; machine learning; k-means clustering; agglomerative hierarchical clustering.;
    All these keywords.

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • Q50 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - General

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