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We quantify the propagation and absorption of large-scale publicly availablenews articles from the World Wide Web to financial markets. To extract publiclyavailable information, we use the news archives from the Common Crawl, anonprofit organization that crawls a large part of the web. We develop aprocessing pipeline to identify news articles associated with the constituentcompanies in the S\&P 500 index, an equity market index that measures the stockperformance of U.S. companies. Using machine learning techniques, we extractsentiment scores from the Common Crawl News data and employ tools frominformation theory to quantify the information transfer from public newsarticles to the U.S. stock market. Furthermore, we analyze and quantify theeconomic significance of the news-based information with a simplesentiment-based portfolio trading strategy. Our findings provides support forthat information in publicly available news on the World Wide Web has astatistically and economically significant impact on events in financialmarkets.

On the impact of publicly available news and information transfer to financial markets