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The effect of ESG divergence on the financial performance of Hong Kong-listed firms: An artificial neural network approach

Author

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  • Cheng, Louis T.W.
  • Cheong, Tsun Se
  • Wojewodzki, Michal
  • Chui, David

Abstract

This paper applies an advanced machine learning algorithm, the Artificial Neural Network (ANN), to examine both linear and nonlinear effects between firm-level characteristics and ESG performance of all firms listed on the Hong Kong Stock Exchange (HKEX) with ESG scores during 2019–2021. To mitigate the problem of data-specific findings due to rating bias from a single rating agency, we employ novel iScore (divergence-adjusted ESG measure). The documented findings indicate the unsuitability of traditional linear regression models to capture the nonlinear effects and to detect some linear relationships. Furthermore, the results show the superiority of the self-organising map (SOM) ANN framework in explaining the impact of firm-level factors on ESG performance.

Suggested Citation

  • Cheng, Louis T.W. & Cheong, Tsun Se & Wojewodzki, Michal & Chui, David, 2025. "The effect of ESG divergence on the financial performance of Hong Kong-listed firms: An artificial neural network approach," Research in International Business and Finance, Elsevier, vol. 73(PA).
    • Handle: RePEc:eee:riibaf:v:73:y:2025:i:pa:s0275531924004094
    DOI: 10.1016/j.ribaf.2024.102616
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