| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w26186 |
| 来源ID | Working Paper 26186 |
| Predicting Returns With Text Data | |
| Zheng Tracy Ke; Bryan T. Kelly; Dacheng Xiu | |
| 发表日期 | 2019-09-02 |
| 出版年 | 2019 |
| 语种 | 英语 |
| 摘要 | We introduce a new text-mining methodology that extracts sentiment information from news articles to predict asset returns. Unlike more common sentiment scores used for stock return prediction (e.g., those sold by commercial vendors or built with dictionary-based methods), our supervised learning framework constructs a sentiment score that is specifically adapted to the problem of return prediction. Our method proceeds in three steps: 1) isolating a list of sentiment terms via predictive screening, 2) assigning sentiment weights to these words via topic modeling, and 3) aggregating terms into an article-level sentiment score via penalized likelihood. We derive theoretical guarantees on the accuracy of estimates from our model with minimal assumptions. In our empirical analysis, we text-mine one of the most actively monitored streams of news articles in the financial system|the Dow Jones Newswires|and show that our supervised sentiment model excels at extracting return-predictive signals in this context. |
| 主题 | Econometrics ; Estimation Methods ; Financial Economics ; Financial Markets ; Portfolio Selection and Asset Pricing ; Behavioral Finance |
| URL | https://www.nber.org/papers/w26186 |
| 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/583858 |
| 推荐引用方式 GB/T 7714 | Zheng Tracy Ke,Bryan T. Kelly,Dacheng Xiu. Predicting Returns With Text Data. 2019. |
| 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 | ||
| w26186.pdf（1445KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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