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Machine learning in the boardroom: Gender diversity prediction using boosting and undersampling methods

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  • Khan, Haroon ur Rashid
  • Bin Khidmat, Waqas
  • Hammouda, Amira
  • Muhammad, Tufail

Abstract

This paper addresses the crucial issue of boardroom diversity and proposes a novel approach utilizing machine learning to predict gender diversity on the boards of Chinese publicly-traded companies from 2008 to 2017. The study employs tree-based boosting with under-sampling as the machine learning technique. Various tree-based boosting techniques are utilized, and the evaluation is based on accuracy, precision, recall, F1 scores, and ROC scores. The findings reveal that extreme Gradient Boosting (XGBoost) with undersampling outperforms other models in terms of predictive performance. Moreover, the paper extracts interpretable principles in the form of if-else statements from the model to enhance its interpretability. This approach contributes to achieving corporate governance goals by promoting board gender diversity using machine learning techniques.

Suggested Citation

  • Khan, Haroon ur Rashid & Bin Khidmat, Waqas & Hammouda, Amira & Muhammad, Tufail, 2023. "Machine learning in the boardroom: Gender diversity prediction using boosting and undersampling methods," Research in International Business and Finance, Elsevier, vol. 66(C).
    • Handle: RePEc:eee:riibaf:v:66:y:2023:i:c:s0275531923001794
    DOI: 10.1016/j.ribaf.2023.102053
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    References listed on IDEAS

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    1. Nazim Hussain & Ugo Rigoni & René P. Orij, 2018. "Corporate Governance and Sustainability Performance: Analysis of Triple Bottom Line Performance," Journal of Business Ethics, Springer, vol. 149(2), pages 411-432, May.
    2. Jeremy Galbreath, 2016. "When do Board and Management Resources Complement Each Other? A Study of Effects on Corporate Social Responsibility," Journal of Business Ethics, Springer, vol. 136(2), pages 281-292, June.
    3. Nina Smith & Pierpaolo Parrotta, 2018. "Why so Few Women on Boards of Directors? Empirical Evidence from Danish Companies in 1998–2010," Journal of Business Ethics, Springer, vol. 147(2), pages 445-467, January.
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    1. Venturelli, Valeria & Pedrazzoli, Alessia & Pennetta, Daniela & Gualandri, Elisabetta, 2024. "Pinkwashing in the banking industry: The relevance of board characteristics," Research in International Business and Finance, Elsevier, vol. 67(PB).

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