---
title: Gender Diversity Prediction in Chinese Boardrooms with Machine Learning
url: https://www.ml-quant.com/papers/repec/eee-riibaf-v-66-y-2023-i-c-s0275531923001794/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: RePEc:eee:riibaf:v:66:y:2023:i:c:s0275531923001794
source_url: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0275531923001794%3Bh%3Drepec%3Aeee%3Ariibaf%3Av%3A66%3Ay%3A2023%3Ai%3Ac%3As0275531923001794
featured: 2023-10-12
citations: unknown
topic: ML & AI Methods
---


# Gender Diversity Prediction in Chinese Boardrooms with Machine Learning

A study successfully used machine learning, specifically the XGBoost model, to predict gender diversity on the boards of Chinese publicly-traded companies.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0275531923001794%3Bh%3Drepec%3Aeee%3Ariibaf%3Av%3A66%3Ay%3A2023%3Ai%3Ac%3As0275531923001794
- Identifier: RePEc:eee:riibaf:v:66:y:2023:i:c:s0275531923001794
- Released: 2023-10-12
- First featured: Quant Letter No. 20 (2023-10-12): https://www.ml-quant.com/issues/2023-10-12/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: ML & AI Methods

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