Bayesian network theory is used to construct a novel probability-based measure for CEO overconfidence. This measure is estimated by studying the probabilistic correlation between CEO overconfidence and several CEO- and firm-specific determinants of overconfidence, that have been documented in the literature. Using S&P 500 firms over the period 2007–2017, we show that the established Bayesian network model has a high fitting and prediction accuracy of CEO overconfidence. This novel measure of CEO overconfidence can be used to conduct empirical studies in corporate and behavioral finance. It also provides a tool to improve decision-making in firms and corporate governance.

贝叶斯网络理论用于构建一种新的基于概率的 CEO 过度自信度量。通过研究 CEO 过度自信与几个 CEO 和公司特定的过度自信决定因素之间的概率相关性来估计该衡量标准，这些因素已在文献中记录。使用 2007-2017 年期间的标准普尔 500 公司，我们表明所建立的贝叶斯网络模型对 CEO 过度自信具有较高的拟合和预测准确性。这种衡量 CEO 过度自信的新方法可用于对企业和行为金融进行实证研究。它还提供了一种工具来改进公司和公司治理的决策。