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

TL;DR

This study analyzes over 24 million news records to automatically extract and quantify how different news topics influence stock trading activity, revealing that abnormal trading volumes are driven by genuinely novel and relevant news.

Contribution

The paper introduces a method combining topic modeling and regularized regressions to automatically identify and quantify news impacts on stock trading, explaining abnormal market volatility.

Findings

News topics can be automatically summarized and linked to trading activity.

Abnormal trading volumes are associated with genuinely novel and relevant news.

The method provides a network visualization of news influence on stocks.

Abstract

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.