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.
We propose a simple discrete time semi-supervised graph embedding approach to link prediction in dynamic networks. The learned embedding reflects information from both the temporal and cross-sectional network structures, which is performed by defining the loss function as a weighted sum of the supervised loss from past dynamics and the unsupervised loss of predicting the neighborhood context in the current network. Our model is also capable of learning different embeddings for both formation and dissolution dynamics. These key aspects contributes to the predictive performance of our model and we provide experiments with three realworld dynamic networks showing that our method is comparable to state of the art methods in link formation prediction and outperforms state of the art baseline methods in link dissolution prediction.
a b s t r a c tUsing the uniform most powerful unbiased test, we observed the sales distribution of consumer electronics in Japan on a daily basis and report that it follows both a lognormal distribution and a power-law distribution and depends on the state of the market. We show that these switches occur quite often. The underlying sales dynamics found between both periods nicely matched a multiplicative process. However, even though the multiplicative term in the process displays a size-dependent relationship when a steady lognormal distribution holds, it shows a size-independent relationship when the powerlaw distribution holds. This difference in the underlying dynamics is responsible for the difference in the two observed distributions.
Blacklists are widely used in society to avoid interactions with inappropriate entities. For example, international organizations issue sanctions lists that are used to prohibit trade with entities that are involved in illegal activities. Financial institutions keep blacklists of inappropriate firms that have financial problems or environmental issues. They create their blacklists by gathering information from various news sources to keep their portfolios profitable as well as green. In the present paper, we focus on the prediction of blacklists in the finance domain. We construct a vast heterogeneous information network that covers the necessary information surrounding each firm, which is assembled using seven professionally curated datasets and two open datasets, which results in approximately 50 million nodes and 400 million edges in total. Exploiting this vast heterogeneous information network, we propose a model that can learn to predict firms that are more likely to be added to a blacklist in the near future. Our approach is tested using the negative news blacklist data of more than 35,000 firms worldwide from January 2012 to May 2018. Comparing with the state-of-the-art methods with and without the network, we show that the predictive accuracy is substantially improved when using the heterogeneous information network. This work suggests new ways to consolidate the diffuse information contained in big data to monitor dominant firms on a global scale for better risk management, more socially responsible investment, and the surveillance of dominant firms. A B C D E P Q (b) Simplified network that illustrates the investigation.that can adjust its prediction strategy to each blacklist category accordingly. Thus, we aim to build a model that can adaptively adjust to each category. However, it is not sufficient to develop an adaptable prediction strategy for each blacklist category by using only basic information that one data vendor provides (i.e., date of addition, industry classification, and headquarters location). Thus, we construct a vast heterogeneous information network that covers the necessary information surrounding each firm by gathering information from several sources. The network is assembled using seven professionally curated datasets and two open datasets, which results in approximately 50 million nodes and 400 million edges in total. Exploiting this vast heterogeneous information network, we propose a model that can navigate through the network to predict firms that are more likely to be added to each blacklist in the near future.To motivate the heterogeneous information network approach in our setting further, we provide a specific example of how real investigators and journalists solve the problem of determining possible entities to add to the blacklist. This example is from a book written by a former member of the United Nations Panel of Experts on Sanctions against North Korea [10]. The Panel of Experts is in charge of the investigation to determine possible candidates to include in the Unit...
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