On the impact of publicly available news and information transfer to financial markets
This addresses the problem of understanding information flow in financial markets for investors and researchers, though it is incremental in applying existing methods to new data.
The study quantified how publicly available news articles from the web propagate to and impact U.S. financial markets, finding that this information has statistically and economically significant effects, as demonstrated by a sentiment-based trading strategy.
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.