New News is Bad News

Paul Glasserman, Harry Mamaysky, Jimmy Qin
{"title":"New News is Bad News","authors":"Paul Glasserman, Harry Mamaysky, Jimmy Qin","doi":"arxiv-2309.05560","DOIUrl":null,"url":null,"abstract":"An increase in the novelty of news predicts negative stock market returns and\nnegative macroeconomic outcomes over the next year. We quantify news novelty -\nchanges in the distribution of news text - through an entropy measure,\ncalculated using a recurrent neural network applied to a large news corpus.\nEntropy is a better out-of-sample predictor of market returns than a collection\nof standard measures. Cross-sectional entropy exposure carries a negative risk\npremium, suggesting that assets that positively covary with entropy hedge the\naggregate risk associated with shifting news language. Entropy risk cannot be\nexplained by existing long-short factors.","PeriodicalId":501355,"journal":{"name":"arXiv - QuantFin - Pricing of Securities","volume":"52 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - QuantFin - Pricing of Securities","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2309.05560","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

An increase in the novelty of news predicts negative stock market returns and negative macroeconomic outcomes over the next year. We quantify news novelty - changes in the distribution of news text - through an entropy measure, calculated using a recurrent neural network applied to a large news corpus. Entropy is a better out-of-sample predictor of market returns than a collection of standard measures. Cross-sectional entropy exposure carries a negative risk premium, suggesting that assets that positively covary with entropy hedge the aggregate risk associated with shifting news language. Entropy risk cannot be explained by existing long-short factors.
新消息就是坏消息
新闻新颖性的增加预示着明年股市的负回报和负面的宏观经济结果。我们量化新闻新颖性-新闻文本分布的变化-通过熵度量,使用应用于大型新闻语料库的递归神经网络计算。熵比一组标准指标更能预测市场回报。横截面熵暴露具有负风险溢价,表明与熵正协变的资产对冲了与新闻语言变化相关的总风险。熵风险不能用现有的多空因素来解释。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
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