High Quality Topic Extraction from Business News Explains Abnormal Financial Market Volatility

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.) affects 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 affects 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 facts 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 is genuinely novel and provides relevant financial information.

New approaches in agent-based modeling of complex financial systems

“…6(b), the probability distribution ofF k (t) is obviously asymmetric with a heavier positive tail. This result indicates that the external information usually drives the market to be more active, which is consistent with the previous empirical findings for the internet query data or news [35,32].…”

“…In this section, we define an information driving force and analyze how it drives the complex financial system. The states of the external information, i.e., the Google search volume G k (t) for the k-th stock, may be complicated [35,39]. As a first approach, we simplify the information states to two states, i.e.,…”

“…In recent years, exploring the scientific impact of online big-data has attracted much attention of researchers from different fields. The massive new data sources resulting from human interactions with the internet offer a better understanding for the profound influence of external information on complex financial systems [31,32,33,34,35,36,37,38,39].…”

A Survey on Journey of Topic Modeling Techniques from SVD to Deep Learning

“…In [63], topic modeling technique was applied to uncover the functional groups in each microbiome sample and in [64], latent topics has been identified in wearable wireless sensor devices to track the high-level from low-level physical activities. In [65], topic modeling technique was applied to stock market activity to extract the information that influences the stock market. In [66], topic modeling technique was applied to predicting the popular twitter messages to classifying the users and messages into topical categories.…”

Trading strategy of a stock index based on the frequency of news releases for listed companies

“…This multiplicative process generates a fat tail of price changes (Newman 2005). Exogenous mechanisms have also been investigated using news archives and historical tick-by-tick price data (Mizuno et al 2015;Hisano et al 2013;Alanyali et al 2013;Mizuno et al 2012;Petersen et al 2010;Rangel 2011). Natural language processing for news articles has identified a relationship between price changes and text words in news articles (Bollen et al 2011;Schumaker and Chen 2009;Thelwall et al 2010).…”