高階經理人薪酬與企業績效：類神經網路之應用

A Neural Network Model for Executive Compensation and Firm Performance

游冠倫(Kuan-Lun Yu)

指導教授 ： 王大維

國立臺灣大學/管理學院/會計學研究所/碩士(2010年)

https://doi.org/10.6342/NTU.2010.03184

摘要

基於代理理論所衍生出的代理問題，高階經理人的薪酬被認為是能夠將經理人與股東間的利益一致，使得經理人能夠付諸股東所企盼的行動。但可惜過去的實證研究，皆只在薪酬與績效間找到微弱的關聯性。而本研究主要目的在於透過不同以往的研究方法，以類神經網路預測模型驗證高階經理人薪酬與企業經營績效的關聯性。結果顯示在高達七成以上預測率的模型中，薪酬變數對於預測模型的重要性都相當高，故可推論薪酬與企業績效具有一定程度的關聯性。且我們亦發現在不同績效指標上，各薪酬的重要性亦各有不同，諸如紅利與會計績效，選擇權與市場績效間的連結。除此之外，時間區段的更迭，也發現各薪酬重要性有一致的變化。最後，此研究結果亦能提供企業未來訂定高階經理人薪酬的方針。

關鍵字

高階經理人薪酬 ； 企業績效 ； 類神經網路

並列摘要

The conflict of interest between the shareholders and the executives is known as the principal-agent problem. If the shareholders have complete information, they can easily design a contract (or incentive plan) that encourages the actions they want. However, the literature suggests weak or statistically insignificant relation between executive compensation and firm performance. In order to overcome the limitation in prior empirical or analytical studies, this paper investigates the association between executive incentive plans and firm performance by using an artificial neural network. Our results show that, overall, we can accurately associate the executives' incentive plan with the firm's performance 63% of the time. For the best and the worst performing firms, the accuracy rate is about 70%. Our findings also suggest that (1) the importance of the component of the incentive plan changes over time, (2) accounting-based performance measure is associated with EPS while market-based performance measure is associated with the market-to-book ratio, and (3) when firms with higher uncertainty, they rely less on stock/option incentives. Finally, the simplicity of the model can help firms better design or change the compensation scheme of the executives.

並列關鍵字

firm performance ； artificial neural network

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