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Understanding the mutual relationships between information flows and socialactivity in society today is one of the cornerstones of the social sciences. Infinancial economics, the key issue in this regard is understanding andquantifying how news of all possible types (geopolitical, environmental,social, financial, economic, etc.) affect trading and the pricing of firms inorganized stock markets. In this article, we seek to address this issue byperforming an analysis of more than 24 million news records provided byThompson Reuters and of their relationship with trading activity for 206 majorstocks in the S&P US stock index. We show that the whole landscape of news thataffect stock price movements can be automatically summarized via simpleregularized regressions between trading activity and news information piecesdecomposed, with the help of simple topic modeling techniques, into their"thematic" features. Using these methods, we are able to estimate and quantifythe impacts of news on trading. We introduce network-based visualizationtechniques to represent the whole landscape of news information associated witha basket of stocks. The examination of the words that are representative of thetopic distributions confirms that our method is able to extract the significantpieces of information influencing the stock market. Our results show that oneof the most puzzling stylized fact in financial economies, namely that atcertain times trading volumes appear to be "abnormally large," can be partiallyexplained by the flow of news. In this sense, our results prove that there isno "excess trading," when restricting to times when news are genuinely noveland provide relevant financial information.

High quality topic extraction from business news explains abnormal financial market volatility