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We use Random Matrix Theory (RMT) and information theory to analyze thecorrelations and flow of information between 64,939 news from The New YorkTimes and 40 world financial indices during 10 months along the period2015-2016. The set of news was quantified and transformed into daily polaritytime series using tools from sentiment analysis. Results from RMT shows that acommon factor lead the world indices and news, and even share the samedynamics. Furthermore, the global correlation structure has found preservedwhen adding white noise, which indicate that correlations are not due to samplesize effects. Likewise, we found a lot of information flowing from news toworld indices for specific delay, being of practical interest for tradingpurpose. Our results suggest a deep relationship between news and worldindices, and show a situation where news drive world market movements, giving anew evidence to support behavioral finance as the current economic paradigm.

Correlations and Flow of Information between The New York Times and Stock Markets