  We quantify the propagation and absorption of large-scale publicly available
news articles from the World Wide Web to financial markets. To extract publicly
available information, we use the news archives from the Common Crawl, a
nonprofit organization that crawls a large part of the web. We develop a
processing pipeline to identify news articles associated with the constituent
companies in the S\&P 500 index, an equity market index that measures the stock
performance of U.S. companies. Using machine learning techniques, we extract
sentiment scores from the Common Crawl News data and employ tools from
information theory to quantify the information transfer from public news
articles to the U.S. stock market. Furthermore, we analyze and quantify the
economic significance of the news-based information with a simple
sentiment-based portfolio trading strategy. Our findings provides support for
that information in publicly available news on the World Wide Web has a
statistically and economically significant impact on events in financial
markets.

Statistical Finance;Machine Learning;Physics and Society;Trading and Market Microstructure