  Understanding the mutual relationships between information flows and social
activity in society today is one of the cornerstones of the social sciences. In
financial economics, the key issue in this regard is understanding and
quantifying how news of all possible types (geopolitical, environmental,
social, financial, economic, etc.) affect trading and the pricing of firms in
organized stock markets. In this article, we seek to address this issue by
performing an analysis of more than 24 million news records provided by
Thompson Reuters and of their relationship with trading activity for 206 major
stocks in the S&P US stock index. We show that the whole landscape of news that
affect stock price movements can be automatically summarized via simple
regularized regressions between trading activity and news information pieces
decomposed, with the help of simple topic modeling techniques, into their
"thematic" features. Using these methods, we are able to estimate and quantify
the impacts of news on trading. We introduce network-based visualization
techniques to represent the whole landscape of news information associated with
a basket of stocks. The examination of the words that are representative of the
topic distributions confirms that our method is able to extract the significant
pieces of information influencing the stock market. Our results show that one
of the most puzzling stylized fact in financial economies, namely that at
certain times trading volumes appear to be "abnormally large," can be partially
explained by the flow of news. In this sense, our results prove that there is
no "excess trading," when restricting to times when news are genuinely novel
and provide relevant financial information.
