| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w27950 |
| 来源ID | Working Paper 27950 |
| How to Talk When a Machine is Listening?: Corporate Disclosure in the Age of AI | |
| Sean Cao; Wei Jiang; Baozhong Yang; Alan L. Zhang | |
| 发表日期 | 2020-10-19 |
| 出版年 | 2020 |
| 语种 | 英语 |
| 摘要 | Growing AI readership, proxied by expected machine downloads, motivates firms to prepare filings that are friendlier to machine parsing and processing. Firms avoid words that are perceived as negative by computational algorithms, as compared to those deemed negative only by dictionaries meant for human readers. The publication of Loughran and McDonald (2011) serves as an instrumental event attributing the difference-in-differences in the measured sentiment to machine readership. High machine-readership firms also exhibit speech emotion assessed as embodying more positivity and excitement by audio processors. This is the first study exploring the feedback effect on corporate disclosure in response to technology. |
| 主题 | Financial Economics ; Financial Markets ; Corporate Finance |
| URL | https://www.nber.org/papers/w27950 |
| 来源智库 | National Bureau of Economic Research (United States) |
| 引用统计 | |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/585624 |
| 推荐引用方式 GB/T 7714 | Sean Cao,Wei Jiang,Baozhong Yang,et al. How to Talk When a Machine is Listening?: Corporate Disclosure in the Age of AI. 2020. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 资源类型 | 版本类型 | 开放类型 | 使用许可 | ||
| w27950.pdf（756KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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