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

Hisano R, Sornette D, Mizuno T, Ohnishi T, Watanabe T - PLoS ONE (2013)

Bottom Line: Understanding the mutual relationships between information flows and social activity in society today is one of the cornerstones of the social sciences.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.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.

View Article: PubMed Central - PubMed

Affiliation: ETH Zurich, Department of Management, Technology and Economics, Zurich, Switzerland. em072010@yahoo.co.jp

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.

Show MeSH

Related in: MedlinePlus

Magnifications of Figure 9.Six magnifications of the “islands” indicated by the arrows in the network of topics shown in Fig. 10, with links between two topics quantifying the degree of similarity associated with their word distributions. Each node is accompanied by the name of the company and its top three most frequent words, as quantified by the topic distribution. The size of a node is set to be proportional to the “fraction of volume explained” (FVE) by that topic and the thickness of a link is equal to 1 minus the JSD metric for the two linked topics. Panel (a) shows the network associated with retail sales of clothing companies; panel (b) that associated with drug and patents; panel (c) that associated with products in telecommunication business; panel (d) that associated with tobacco law suit; panel (e) that associated with national defense budget; panel (f) that associated with the potential Comcast Disney merger in 2004.
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC3675119&req=5

pone-0064846-g010: Magnifications of Figure 9.Six magnifications of the “islands” indicated by the arrows in the network of topics shown in Fig. 10, with links between two topics quantifying the degree of similarity associated with their word distributions. Each node is accompanied by the name of the company and its top three most frequent words, as quantified by the topic distribution. The size of a node is set to be proportional to the “fraction of volume explained” (FVE) by that topic and the thickness of a link is equal to 1 minus the JSD metric for the two linked topics. Panel (a) shows the network associated with retail sales of clothing companies; panel (b) that associated with drug and patents; panel (c) that associated with products in telecommunication business; panel (d) that associated with tobacco law suit; panel (e) that associated with national defense budget; panel (f) that associated with the potential Comcast Disney merger in 2004.

Mentions: Fig. 9 shows the whole network of all the topics extracted by our method for the 206 stocks we focus on. The network can be viewed as consisting of the “mainland” and more isolated “islands.” The mainland is made up of all the connections between topics produced by words reflecting earnings reports (“profit,” “earning,” “share,” “pct (short for percent)”), credit ratings (“rating,” “debt,” “credit”), merger deal (“merger,” “deal”), and the financial crisis (“crisis,” “financial”). In order to better discern some of the major “islands,” Fig. 10 presents six zooms on the domains indicated by the arrows in Fig. 9. The observed clusters of company names and words representing the topic distributions confirm that our method successfully extracted the correct information. Note that all the word contents of the constructed topic distributions have financial and/or economic meaning that carry useful information from the point of view of an investor and can be surmised to indeed have an impact on the future earning of the firms. We refer in particular to the following word contents: “earning reports,” “retailers profits,” “drug patents,” “national defense budget,” “new products,” “merger deal,” “global recession,” “natural disasters,” and so on.


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

Hisano R, Sornette D, Mizuno T, Ohnishi T, Watanabe T - PLoS ONE (2013)

Magnifications of Figure 9.Six magnifications of the “islands” indicated by the arrows in the network of topics shown in Fig. 10, with links between two topics quantifying the degree of similarity associated with their word distributions. Each node is accompanied by the name of the company and its top three most frequent words, as quantified by the topic distribution. The size of a node is set to be proportional to the “fraction of volume explained” (FVE) by that topic and the thickness of a link is equal to 1 minus the JSD metric for the two linked topics. Panel (a) shows the network associated with retail sales of clothing companies; panel (b) that associated with drug and patents; panel (c) that associated with products in telecommunication business; panel (d) that associated with tobacco law suit; panel (e) that associated with national defense budget; panel (f) that associated with the potential Comcast Disney merger in 2004.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3675119&req=5

pone-0064846-g010: Magnifications of Figure 9.Six magnifications of the “islands” indicated by the arrows in the network of topics shown in Fig. 10, with links between two topics quantifying the degree of similarity associated with their word distributions. Each node is accompanied by the name of the company and its top three most frequent words, as quantified by the topic distribution. The size of a node is set to be proportional to the “fraction of volume explained” (FVE) by that topic and the thickness of a link is equal to 1 minus the JSD metric for the two linked topics. Panel (a) shows the network associated with retail sales of clothing companies; panel (b) that associated with drug and patents; panel (c) that associated with products in telecommunication business; panel (d) that associated with tobacco law suit; panel (e) that associated with national defense budget; panel (f) that associated with the potential Comcast Disney merger in 2004.
Mentions: Fig. 9 shows the whole network of all the topics extracted by our method for the 206 stocks we focus on. The network can be viewed as consisting of the “mainland” and more isolated “islands.” The mainland is made up of all the connections between topics produced by words reflecting earnings reports (“profit,” “earning,” “share,” “pct (short for percent)”), credit ratings (“rating,” “debt,” “credit”), merger deal (“merger,” “deal”), and the financial crisis (“crisis,” “financial”). In order to better discern some of the major “islands,” Fig. 10 presents six zooms on the domains indicated by the arrows in Fig. 9. The observed clusters of company names and words representing the topic distributions confirm that our method successfully extracted the correct information. Note that all the word contents of the constructed topic distributions have financial and/or economic meaning that carry useful information from the point of view of an investor and can be surmised to indeed have an impact on the future earning of the firms. We refer in particular to the following word contents: “earning reports,” “retailers profits,” “drug patents,” “national defense budget,” “new products,” “merger deal,” “global recession,” “natural disasters,” and so on.

Bottom Line: Understanding the mutual relationships between information flows and social activity in society today is one of the cornerstones of the social sciences.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.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.

View Article: PubMed Central - PubMed

Affiliation: ETH Zurich, Department of Management, Technology and Economics, Zurich, Switzerland. em072010@yahoo.co.jp

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.

Show MeSH
Related in: MedlinePlus