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

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

Research in International Business and Finance, 2023, vol. 66, issue C

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.

Keywords: Gender diversity; Corporate governance; Boardroom; Chinese companies; Machine learning; Data imbalance; Treebased boosting; Undersampling (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:riibaf:v:66:y:2023:i:c:s0275531923001794

DOI: 10.1016/j.ribaf.2023.102053

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