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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

Selected topic learned by LDA for Toyota.Selected topics learned by LDA and the associated news volume estimated using equation (1) for the term “Toyota.” The top three words for these topics were: (A) Toyota, recall, safety; (B) financial, crisis, economy; (C) Japan, production, earthquake; (D) team, F1, race.
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pone-0064846-g003: Selected topic learned by LDA for Toyota.Selected topics learned by LDA and the associated news volume estimated using equation (1) for the term “Toyota.” The top three words for these topics were: (A) Toyota, recall, safety; (B) financial, crisis, economy; (C) Japan, production, earthquake; (D) team, F1, race.

Mentions: where is the number of times a word tagged with topic appeared in document and is the indicator function of the set of documents on day . Fig. 3 presents some examples of the time evolution of the news volume for four topics for the term “Toyota.” It also lists the top three words of the corresponding topic distributions. A full description is provided in the supporting information as long as their time series plot (i.e. Data. S1 and Fig. S1).


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)

Selected topic learned by LDA for Toyota.Selected topics learned by LDA and the associated news volume estimated using equation (1) for the term “Toyota.” The top three words for these topics were: (A) Toyota, recall, safety; (B) financial, crisis, economy; (C) Japan, production, earthquake; (D) team, F1, race.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0064846-g003: Selected topic learned by LDA for Toyota.Selected topics learned by LDA and the associated news volume estimated using equation (1) for the term “Toyota.” The top three words for these topics were: (A) Toyota, recall, safety; (B) financial, crisis, economy; (C) Japan, production, earthquake; (D) team, F1, race.
Mentions: where is the number of times a word tagged with topic appeared in document and is the indicator function of the set of documents on day . Fig. 3 presents some examples of the time evolution of the news volume for four topics for the term “Toyota.” It also lists the top three words of the corresponding topic distributions. A full description is provided in the supporting information as long as their time series plot (i.e. Data. S1 and Fig. S1).

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