Do ESG practices affect the financial performance of banks? A meta-analysis perspective

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Type of the article: Research Article

Abstract
This study aims to investigate the pooled effects of environmental, social, and governance (ESG) practices on banks’ financial performance (FP) using a random effects model of meta-analysis. In line with the PRISMA guidelines, 52 studies were identified as eligible out of 387 studies for this analysis. After applying the inclusion criteria, i.e., studies that have quantitatively reported the required measures like the correlation coefficient between ESG and FP, 16 studies were considered for meta-analysis with a combined total sample of 4,084 participants. The combined effect size was r = 0.10 (SE = 0.10, 95% CI: –0.11 to 0.31), reflecting a weak and statistically insignificant correlation, displaying no impact of ESG practices on the financial performance of banks from 2018 to 2025. Furthermore, the predicted interval was –1.38 to 1.59, which means that future research would provide very heterogeneous effect sizes. A heterogeneity analysis shows that there is wide variation among the studies (Q = 1213.82, p < 0.001, I² = 98.76%), indicating that differences in study characteristics may lead to differences in effect sizes. The trim and fill method provides no evidence for the existence of missing studies; however, publication bias is considered a possibility. The findings should be interpreted cautiously, given their high heterogeneity and the suspected source of bias. Despite their small effect size, inconsistencies across studies highlight the need for future research to investigate possible moderating factors. Practical implications emphasize that even if the generalizability of the findings is established, it cannot be without considering study-specific variables.

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    • Figure 1. Forest plot
    • Figure 2. Funnel plot
    • Figure 3. Standardized residual histogram
    • Table 1. Studies included for analysis
    • Table 2. Meta-analysis model
    • Table 3. Publication bias analysis (funnel plot)
    • Table 4. Bin table (residual histogram)
    • Conceptualization
      Amiya Kumar Mohapatra, Subhasish Das, Aditya Prasad Sahoo
    • Formal Analysis
      Amiya Kumar Mohapatra, Subhasish Das, Yayati Nayak, Aditya Prasad Sahoo, Rahul Matta
    • Investigation
      Amiya Kumar Mohapatra, Subhasish Das, Aditya Prasad Sahoo, Rahul Matta
    • Methodology
      Amiya Kumar Mohapatra, Subhasish Das
    • Project administration
      Amiya Kumar Mohapatra, Yayati Nayak, Aditya Prasad Sahoo, Rahul Matta
    • Software
      Amiya Kumar Mohapatra, Subhasish Das, Rahul Matta
    • Supervision
      Amiya Kumar Mohapatra, Aditya Prasad Sahoo, Rahul Matta
    • Validation
      Amiya Kumar Mohapatra, Subhasish Das, Yayati Nayak, Aditya Prasad Sahoo
    • Writing – original draft
      Amiya Kumar Mohapatra, Subhasish Das, Aditya Prasad Sahoo
    • Writing – review & editing
      Amiya Kumar Mohapatra, Subhasish Das, Yayati Nayak, Rahul Matta
    • Data curation
      Subhasish Das, Yayati Nayak, Aditya Prasad Sahoo, Rahul Matta
    • Resources
      Yayati Nayak, Rahul Matta
    • Visualization
      Yayati Nayak, Aditya Prasad Sahoo, Rahul Matta