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The determinants of voluntary disclosure: Integration of eXtreme gradient boost (XGBoost) and explainable artificial intelligence (XAI) techniques

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  • Lu, Yu-Hsin
  • Lin, Yu-Cheng

Abstract

Financial information transparency is vital for the various users of financial statements. This study employs the Explainable Artificial Intelligence (XAI) approach, utilizing eXtreme Gradient Boost (XGBoost) to explore management's motivations for voluntary disclosure. By transforming financial data into various plots, we introduce a voluntary disclosure model that enhances interpretability through Shapley Additive exPlanations (SHAP) techniques. These XAI methods aim to clarify different results in the voluntary disclosure literature, addressing the ongoing debate within the financial research community regarding voluntary disclosure. This research marks a significant advancement in voluntary disclosure by merging the transparency of XAI with effective voluntary disclosure prediction, offering a more comprehensive understanding of the determinants of voluntary disclosure.

Suggested Citation

  • Lu, Yu-Hsin & Lin, Yu-Cheng, 2024. "The determinants of voluntary disclosure: Integration of eXtreme gradient boost (XGBoost) and explainable artificial intelligence (XAI) techniques," International Review of Financial Analysis, Elsevier, vol. 96(PA).
    • Handle: RePEc:eee:finana:v:96:y:2024:i:pa:s105752192400509x
    DOI: 10.1016/j.irfa.2024.103577
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    References listed on IDEAS

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