本研究欲從行為財務學之角度，結合文字探勘及自然語言處理的技術探討過度自信經理人會如何影響公司發放股利之決策。本文建構三種過度自信指標:其一，參考 Malmendier and Tate (2008) 中衡量過度自信經理人的方法，以中庸且享譽的財經媒體對公司經理人之報導來判斷公司經理人是否存在過度自信。其二，採用 Lin et al. (2005) 衡量過度自信的方式並稍作修改，運用文字探勘及自然語言處理的技術從公司法人說明會會議記錄文本中提取經理人的財務預測數字，分別建立離散型及連續型過度自信指標，並與前述的媒體過度自信指標相比較。本研究不同於傳統建構媒體過度自信指標的方式，其不僅需要花費大量時間人工蒐集描寫公司經理人的文章，且需要主觀判斷該文句是否針對特定經理人。本文使用法人說明會會議記錄文本除了含有增額資訊價值且唾手可得之外，也利用 BERT 分類模型，大幅減少取得文本資料中所需資訊之時間及增加其精準度，更建構出相較前者更為客觀之過度自信指標。

In this study we examine the relation between managerial overconfidence and dividend policy by combining the technique of text mining and natural language processing. We construct three proxies to measure managerial overconfidence. One of them is the measure used in Malmendier and Tate (2008), we hand-collect data on CEO press coverage from neutral media and identify whether CEOs were characterized as confident or non-confident. The other two proxies we develop are based on the measure used in Lin et al. (2005), we collect conference call transcripts, using the technique of text mining and natural language processing to capture the manager's forecasting number, we use it to construct a binary variable and a continuous variable to measure managerial overconfidence. The contribution of this research is that it is different from the traditional way of constructing overconfidence indicators, it not only requires a lot of time to hand-collect articles describing managers, but also needs to subjectively judge whether the terms are used to describe the CEO. The transcripts of conference call we used are readily available, and we also use BERT for sentiment classification to greatly reduce the time to obtain required information, construct a more objective overconfidence indicator.

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