  We use Random Matrix Theory (RMT) and information theory to analyze the
correlations and flow of information between 64,939 news from The New York
Times and 40 world financial indices during 10 months along the period
2015-2016. The set of news was quantified and transformed into daily polarity
time series using tools from sentiment analysis. Results from RMT shows that a
common factor lead the world indices and news, and even share the same
dynamics. Furthermore, the global correlation structure has found preserved
when adding white noise, which indicate that correlations are not due to sample
size effects. Likewise, we found a lot of information flowing from news to
world indices for specific delay, being of practical interest for trading
purpose. Our results suggest a deep relationship between news and world
indices, and show a situation where news drive world market movements, giving a
new evidence to support behavioral finance as the current economic paradigm.
