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

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

Research in International Business and Finance, 2025, vol. 73, issue PA

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.

Keywords: Corporate ESG Performance; Rating divergence; Machine Learning; Hong Kong (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:riibaf:v:73:y:2025:i:pa:s0275531924004094

DOI: 10.1016/j.ribaf.2024.102616

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