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

TL;DR

This study investigates the relationship between news sentiment from The New York Times and global stock indices, revealing that news significantly influences market movements and shares common dynamics with indices.

Contribution

It applies Random Matrix Theory and sentiment analysis to quantify and analyze the flow of information between news and stock markets, demonstrating a deep connection and directional influence.

Findings

A common factor influences both news and indices.

Correlations persist despite added noise, indicating robustness.

Information flows from news to markets with specific delays.

Abstract

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.