Artificial Intelligence Measurement of Disclosure (AIMD)
Artificial Intelligence Measurement of Disclosure (AIMD)
复制标题
人工智能披露衡量标准 (AIMD)
DOI：
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
Michael Grüning
中科院分区：
文献类型：
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作者：
Michael Grüning
关于自愿披露的实证研究缺乏适当的衡量技术来量化公司披露的强度。在本文中，我介绍了人工智能披露测量（AIMD），这是一种利用人工智能测量披露的计算机化技术，它可以从英语年度报告中得出 10 个不同信息维度的披露代理，而无需人工参与。标准有效性测试表明，在控制一组稳健的协变量和多种统计技术的情况下，AIMD 与以价差和 PIN 为代表的信息不对称负相关。此外，与 AIMR 披露评级、标准普尔透明度和披露评级、一些专有的手动披露评分以及调查显示的公司自身对其披露水平的评估相比，AIMD 具有结构有效性。我还使用 SEC 监管的公司的 127,895 个公司年度观察样本来证明 AIMD 作为衡量披露情况的一种经济有效的技术的适用性。
Empirical research on voluntary disclosure lacks an appropriate measurement technique for quantifying the intensity of a firm's disclosure. In this paper, I introduce artificial intelligence measurement of disclosure (AIMD), a computerised technique for measuring disclosure using artificial intelligence, which derives disclosure proxies from English-language annual reports for 10 different information dimensions without human involvement. Criterion validity tests indicate that, controlling for a robust set of covariates and multiple statistical techniques, AIMD is negatively associated with information asymmetry as proxied by spreads and PIN. Furthermore, AIMD has construct validity when compared to the AIMR disclosure rating, Standard & Poor's Transparency and Disclosure Rating, several proprietary manual disclosure scorings and companies’ own assessment of their level of disclosure as indicated by a survey. I also demonstrate the applicability of AIMD as a cost-effective technique for measuring disclosure using a sample of 127,895 firm-year observations of companies regulated by the SEC.
DOI：
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发表时间：
1997
期刊：
Accounting review: A quarterly journal of the American Accounting Association
影响因子：
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作者：
Christine Botosan
通讯作者：
Christine Botosan