{"title":"新消息就是坏消息","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":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"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\":null,\"pages\":null},\"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}","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}
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.
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