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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.

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Related in: MedlinePlus

Pictorial illustration of “peak days” of normalized trading volume.The black line shows the de-trended trading volume of Toyota stock for the period from January 2003 to June 2011. The red dots indicate the “peak days” selected by the method described in the text. There are 119 “peak days” for the entire period from January 2003 to June 2011.
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pone-0064846-g004: Pictorial illustration of “peak days” of normalized trading volume.The black line shows the de-trended trading volume of Toyota stock for the period from January 2003 to June 2011. The red dots indicate the “peak days” selected by the method described in the text. There are 119 “peak days” for the entire period from January 2003 to June 2011.

Mentions: Because researchers are generally interested in explaining large (or “abnormal”) market activity, we focus our attention on “peak days,” defined in terms of the 95th percentile of daily trading volume, so that on 95% of the days the trading volume was smaller than during the peak days. In order to pay equal attention to large market activity across the whole study period (January 2003 to June 2011), we divided the period overall into 17 six-month time windows and identified the “peak days” for each of the 17 time windows separately. The sequence of peak days is shown in Fig. 4. For each term such as “Toyota,” the fraction of the corresponding estimated news volume that can be explained by each topic via regression (2), restricting our attention to only the news volume found on “peak days,” is referred to as the “fraction of volume explained” (FVE). In this article, we only use topics that obtained FVE values larger than 0.5%. For example, this method determines out of topics as being useful for “Toyota.” Table 1 provides a list of these 9 topics and their individual FVEs for “Toyota.” Inspections of this list shows that our procedure yields sensible results, and unimportant topics such as “Formula One” shown in Fig. 3 are correctly pruned out.


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)

Pictorial illustration of “peak days” of normalized trading volume.The black line shows the de-trended trading volume of Toyota stock for the period from January 2003 to June 2011. The red dots indicate the “peak days” selected by the method described in the text. There are 119 “peak days” for the entire period from January 2003 to June 2011.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0064846-g004: Pictorial illustration of “peak days” of normalized trading volume.The black line shows the de-trended trading volume of Toyota stock for the period from January 2003 to June 2011. The red dots indicate the “peak days” selected by the method described in the text. There are 119 “peak days” for the entire period from January 2003 to June 2011.
Mentions: Because researchers are generally interested in explaining large (or “abnormal”) market activity, we focus our attention on “peak days,” defined in terms of the 95th percentile of daily trading volume, so that on 95% of the days the trading volume was smaller than during the peak days. In order to pay equal attention to large market activity across the whole study period (January 2003 to June 2011), we divided the period overall into 17 six-month time windows and identified the “peak days” for each of the 17 time windows separately. The sequence of peak days is shown in Fig. 4. For each term such as “Toyota,” the fraction of the corresponding estimated news volume that can be explained by each topic via regression (2), restricting our attention to only the news volume found on “peak days,” is referred to as the “fraction of volume explained” (FVE). In this article, we only use topics that obtained FVE values larger than 0.5%. For example, this method determines out of topics as being useful for “Toyota.” Table 1 provides a list of these 9 topics and their individual FVEs for “Toyota.” Inspections of this list shows that our procedure yields sensible results, and unimportant topics such as “Formula One” shown in Fig. 3 are correctly pruned out.

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