Twitter作为预测股市走势的工具:一项短窗口事件研究

Q1 Mathematics
Tahir M. Nisar, Man Yeung
{"title":"Twitter作为预测股市走势的工具:一项短窗口事件研究","authors":"Tahir M. Nisar,&nbsp;Man Yeung","doi":"10.1016/j.jfds.2017.11.002","DOIUrl":null,"url":null,"abstract":"<div><p>In order to explore the relationship between politics-related sentiment and FTSE 100 movements, we conducted a short-window event study of a UK based political event. We collected a sample of over 60,000 tweets using 3 key hashtags during the period of 6 days including before, during and after the 2016 local elections. The study involved performing a collection of correlation and regression analyses to compare daily mood with daily changes in the price of the FTSE 100 at the market level. The findings suggest that there is evidence of correlation between the general mood of the public and investment behavior in the short term; however, the relationship is not yet determined as statistically significant. There is also evidence of causation between public sentiment and the stock market movements, in terms of the relationship between MOOD and the daily closing price, and the time lag findings of MOOD and PRICE. Overall, these results show promise for using sentiment analytics on Twitter data for forecasting market movements.</p></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.jfds.2017.11.002","citationCount":"82","resultStr":"{\"title\":\"Twitter as a tool for forecasting stock market movements: A short-window event study\",\"authors\":\"Tahir M. Nisar,&nbsp;Man Yeung\",\"doi\":\"10.1016/j.jfds.2017.11.002\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>In order to explore the relationship between politics-related sentiment and FTSE 100 movements, we conducted a short-window event study of a UK based political event. We collected a sample of over 60,000 tweets using 3 key hashtags during the period of 6 days including before, during and after the 2016 local elections. The study involved performing a collection of correlation and regression analyses to compare daily mood with daily changes in the price of the FTSE 100 at the market level. The findings suggest that there is evidence of correlation between the general mood of the public and investment behavior in the short term; however, the relationship is not yet determined as statistically significant. There is also evidence of causation between public sentiment and the stock market movements, in terms of the relationship between MOOD and the daily closing price, and the time lag findings of MOOD and PRICE. Overall, these results show promise for using sentiment analytics on Twitter data for forecasting market movements.</p></div>\",\"PeriodicalId\":36340,\"journal\":{\"name\":\"Journal of Finance and Data Science\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1016/j.jfds.2017.11.002\",\"citationCount\":\"82\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Finance and Data Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2405918817300247\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"Mathematics\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Finance and Data Science","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2405918817300247","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Mathematics","Score":null,"Total":0}
引用次数: 82

摘要

为了探索政治相关情绪与富时100指数走势之间的关系,我们对英国的一个政治事件进行了短窗口事件研究。我们在2016年地方选举之前、期间和之后的6天时间里,使用3个关键标签收集了6万多条推文样本。这项研究包括进行一系列相关和回归分析,以比较每日情绪与市场层面上富时100指数(FTSE 100)价格的每日变化。研究结果表明,短期内公众情绪与投资行为之间存在相关性;然而,这种关系尚未被确定为具有统计学意义。在MOOD与每日收盘价之间的关系以及MOOD与price的时滞发现方面,也有证据表明公众情绪与股市走势之间存在因果关系。总的来说,这些结果显示了在Twitter数据上使用情绪分析来预测市场走势的前景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Twitter as a tool for forecasting stock market movements: A short-window event study

In order to explore the relationship between politics-related sentiment and FTSE 100 movements, we conducted a short-window event study of a UK based political event. We collected a sample of over 60,000 tweets using 3 key hashtags during the period of 6 days including before, during and after the 2016 local elections. The study involved performing a collection of correlation and regression analyses to compare daily mood with daily changes in the price of the FTSE 100 at the market level. The findings suggest that there is evidence of correlation between the general mood of the public and investment behavior in the short term; however, the relationship is not yet determined as statistically significant. There is also evidence of causation between public sentiment and the stock market movements, in terms of the relationship between MOOD and the daily closing price, and the time lag findings of MOOD and PRICE. Overall, these results show promise for using sentiment analytics on Twitter data for forecasting market movements.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Journal of Finance and Data Science
Journal of Finance and Data Science Mathematics-Statistics and Probability
CiteScore
3.90
自引率
0.00%
发文量
15
审稿时长
30 days
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信