董事会专长背景与公司业绩

Chiou-Yann Lee, Chun-Ru Wen, Binh Thi-Thanh-Nguyen
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引用次数: 0

摘要

本研究提出了一种结合阈值技术和人工神经网络(ANN)的新型财务业绩预测方法。它应用阈值回归法来识别影响台湾传统产业财务绩效的董事会内部因素。研究结果表明,利用相关董事会结构数据,ANN 方法可有效预测财务绩效。此外,实证结果表明,成员越多的董事会盈利能力越强。此外,具有会计专业知识的董事会成员人数越多,利润越稳定。相比之下,具有财务专业知识的成员越多,对盈利能力的影响越明显。
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Board Expertise Background and Firm Performance
This study presents a novel financial performance forecasting method that combines the threshold technique with Artificial Neural Networks (ANN). It applies the threshold regression method to identify the factors within the board of directors that influence the financial performance of traditional industries in Taiwan. The findings indicate that the ANN method effectively predicts financial performance by using relevant board structure data. Furthermore, the empirical results suggest that boards with more members demonstrate increased profitability. Additionally, a more significant presence of board members with accounting expertise contributes to more consistent profits. In contrast, an increased presence of members with financial expertise has a more pronounced impact on profitability.
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