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; David CHUI

doi:10.1016/j.ribaf.2024.102616

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^*
, David CHUI

^*Corresponding author for this work

FACULTY OF BUSINESS

Research output: Journal Publications › Journal Article (refereed) › peer-review

17 Citations (Scopus)

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.

Original language	English
Article number	102616
Journal	Research in International Business and Finance
Volume	73
Early online date	13 Oct 2024
DOIs	https://doi.org/10.1016/j.ribaf.2024.102616
Publication status	Published - Jan 2025

Bibliographical note

Publisher Copyright:
© 2024 Elsevier B.V.

Funding

Louis T.W. Cheng acknowledges financial support from RGC (grant number: UGC/IDS(R)14/21) of the HKSAR.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 8 Decent Work and Economic Growth
SDG 12 Responsible Consumption and Production
SDG 13 Climate Action

Keywords

Corporate ESG Performance
Hong Kong
Machine Learning
Rating divergence

Access to Document

10.1016/j.ribaf.2024.102616

Cite this

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

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, Hong Kong, Machine Learning, Rating divergence",

author = "CHENG, \{Louis T.W.\} and CHEONG, \{Tsun Se\} and Michal WOJEWODZKI and David CHUI",

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year = "2025",

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doi = "10.1016/j.ribaf.2024.102616",

language = "English",

volume = "73",

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T1 - The effect of ESG divergence on the financial performance of Hong Kong-listed firms : An artificial neural network approach

AU - CHENG, Louis T.W.

AU - CHEONG, Tsun Se

AU - WOJEWODZKI, Michal

AU - CHUI, David

PY - 2025/1

Y1 - 2025/1

N2 - 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.

AB - 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.

KW - Corporate ESG Performance

KW - Hong Kong

KW - Machine Learning

KW - Rating divergence

UR - https://www.scopus.com/pages/publications/85206155421

U2 - 10.1016/j.ribaf.2024.102616

DO - 10.1016/j.ribaf.2024.102616

M3 - Journal Article (refereed)

AN - SCOPUS:85206155421

SN - 0275-5319

VL - 73

JO - Research in International Business and Finance

JF - Research in International Business and Finance

M1 - 102616

ER -

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

Abstract

Bibliographical note

Funding

UN SDGs

Keywords

Access to Document

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