{"title":"量化财经新闻与股票的相关性","authors":"Haizhou Qu, D. Kazakov","doi":"10.1109/SSCI.2016.7850021","DOIUrl":null,"url":null,"abstract":"Financial news and stocks appear linked to the point where the use of online news to forecast the markets has become a major selling point for some traders. The correlation between news content and stock returns is clearly of interest, but has been mostly centred on news meta-data, such as volume and popularity. We address this question here by measuring the correlation between the returns of 27 publicly traded companies and news about them as collected from Yahoo Financial News for the period 1 Oct 2014 to 30 Apr 2015. In all reported experiments, two metrics are defined, one to measure the distance between two time series, the other to quantify the difference between two collections of news items. Two 27 × 27 distance matrices are thus produced, and their correlation measured with the Mantel test. This allows us to estimate the correlation of stock market data (returns, change, volume and close price) with the content of published news in a given period of time. A number of representations for the news are tested, as well as different distance metrics between time series. Clear, statistically significant, moderate level correlations are detected in most cases. Lastly, the impact of the length of the period studied on the observed correlation is also investigated.","PeriodicalId":120288,"journal":{"name":"2016 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Quantifying correlation between Financial News and stocks\",\"authors\":\"Haizhou Qu, D. Kazakov\",\"doi\":\"10.1109/SSCI.2016.7850021\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Financial news and stocks appear linked to the point where the use of online news to forecast the markets has become a major selling point for some traders. The correlation between news content and stock returns is clearly of interest, but has been mostly centred on news meta-data, such as volume and popularity. We address this question here by measuring the correlation between the returns of 27 publicly traded companies and news about them as collected from Yahoo Financial News for the period 1 Oct 2014 to 30 Apr 2015. In all reported experiments, two metrics are defined, one to measure the distance between two time series, the other to quantify the difference between two collections of news items. Two 27 × 27 distance matrices are thus produced, and their correlation measured with the Mantel test. This allows us to estimate the correlation of stock market data (returns, change, volume and close price) with the content of published news in a given period of time. A number of representations for the news are tested, as well as different distance metrics between time series. Clear, statistically significant, moderate level correlations are detected in most cases. Lastly, the impact of the length of the period studied on the observed correlation is also investigated.\",\"PeriodicalId\":120288,\"journal\":{\"name\":\"2016 IEEE Symposium Series on Computational Intelligence (SSCI)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 IEEE Symposium Series on Computational Intelligence (SSCI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SSCI.2016.7850021\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE Symposium Series on Computational Intelligence (SSCI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SSCI.2016.7850021","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Quantifying correlation between Financial News and stocks
Financial news and stocks appear linked to the point where the use of online news to forecast the markets has become a major selling point for some traders. The correlation between news content and stock returns is clearly of interest, but has been mostly centred on news meta-data, such as volume and popularity. We address this question here by measuring the correlation between the returns of 27 publicly traded companies and news about them as collected from Yahoo Financial News for the period 1 Oct 2014 to 30 Apr 2015. In all reported experiments, two metrics are defined, one to measure the distance between two time series, the other to quantify the difference between two collections of news items. Two 27 × 27 distance matrices are thus produced, and their correlation measured with the Mantel test. This allows us to estimate the correlation of stock market data (returns, change, volume and close price) with the content of published news in a given period of time. A number of representations for the news are tested, as well as different distance metrics between time series. Clear, statistically significant, moderate level correlations are detected in most cases. Lastly, the impact of the length of the period studied on the observed correlation is also investigated.
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