Exogenous and endogenous factors affecting stock market transactions: A Hawkes process analysis of the Tokyo Stock Exchange during the COVID-19 pandemic.

Transactions in financial markets are not evenly spaced but can be concentrated within a short period of time. In this study, we investigated the factors that determine the transaction frequency in financial markets. Specifically, we employed the Hawkes process model to identify exogenous and endogenous forces governing transactions of individual stocks in the Tokyo Stock Exchange during the COVID-19 pandemic. To enhance the accuracy of our analysis, we introduced a novel EM algorithm for the estimation of exogenous and endogenous factors that specifically addresses the interdependence of the values of these factors over time. We detected a substantial change in the transaction frequency in response to policy change announcements. Moreover, there is significant heterogeneity in the transaction frequency among individual stocks. We also found a tendency where stocks with high market capitalization tend to significantly respond to external news, while their excitation relationship between transactions is weak. This suggests the capability of quantifying the market state from the viewpoint of the exogenous and endogenous factors generating transactions for various stocks.

Gaussian hierarchical latent Dirichlet allocation: Bringing polysemy back.

Topic models are widely used to discover the latent representation of a set of documents. The two canonical models are latent Dirichlet allocation, and Gaussian latent Dirichlet allocation, where the former uses multinomial distributions over words, and the latter uses multivariate Gaussian distributions over pre-trained word embedding vectors as the latent topic representations, respectively. Compared with latent Dirichlet allocation, Gaussian latent Dirichlet allocation is limited in the sense that it does not capture the polysemy of a word such as "bank." In this paper, we show that Gaussian latent Dirichlet allocation could recover the ability to capture polysemy by introducing a hierarchical structure in the set of topics that the model can use to represent a given document. Our Gaussian hierarchical latent Dirichlet allocation significantly improves polysemy detection compared with Gaussian-based models and provides more parsimonious topic representations compared with hierarchical latent Dirichlet allocation. Our extensive quantitative experiments show that our model also achieves better topic coherence and held-out document predictive accuracy over a wide range of corpus and word embedding vectors which significantly improves the capture of polysemy compared with GLDA and CGTM. Our model learns the underlying topic distribution and hierarchical structure among topics simultaneously, which can be further used to understand the correlation among topics. Moreover, the added flexibility of our model does not necessarily increase the time complexity compared with GLDA and CGTM, which makes our model a good competitor to GLDA.

Fluctuations in the Number of Stores by Industry During the COVID-19 Pandemic Based on Japanese Phone Book Entries.

Currently in Japan, summaries of the number of bankruptcies due to the spread of COVID-19 can only be obtained from surveys conducted by a few research firms targeting particular companies. In this study, we used Japanese telephone directory data containing detailed information on the location and industrial category of stores/facilities nationwide in an effort to infer the influence of COVID-19 on businesses in Japan. We analyzed the temporal change in the number of stores before and after the COVID-19 outbreak. Among other findings, the analysis revealed that the number of travel agencies and facilities offering karaoke and other forms of entertainment declined significantly after the outbreak in some prefectures, with the largest declines in Ibaraki, Osaka, and Hyogo prefectures, and a relatively small decline in Tochigi prefecture. Among the stores and facilities categorized as restaurants and travel-related services, the decline was particularly significant in urban areas such as Tokyo and Osaka prefectures.

Visualizing Social and Behavior Change due to the Outbreak of COVID-19 Using Mobile Phone Location Data.

We visualize the rates of stay-home for residents by region using the difference between day-time and night-time populations to detect residential areas, and then observing the numbers of people leaving residential areas. There are issues with measuring stay-home rates by observing numbers of people visiting downtown areas, such as central urban shopping centers and major train stations. The first is that we cannot eliminate the possibility that people will avoid areas being observed and go to other areas. The second is that for people visiting downtown areas, we cannot know where they reside. These issues can be resolved if we quantify the degree of stay-home using the number of people leaving residential areas. There are significant differences in stay-home levels by region throughout Japan. By this visualization, residents of each region can see whether their level of stay-home is adequate or not, and this can provide incentive toward compliance suited to the residents of the region.

Metabolic fingerprints of fear memory consolidation during sleep.

Metabolites underlying brain function and pathology are not as well understood as genes. Here, we applied a novel metabolomics approach to further understand the mechanisms of memory processing in sleep. As hippocampal dentate gyrus neurons are known to consolidate contextual fear memory, we analyzed real-time changes in metabolites in the dentate gyrus in different sleep-wake states in mice. Throughout the study, we consistently detected more than > 200 metabolites. Metabolite profiles changed dramactically upon sleep-wake state transitions, leading to a clear separation of phenotypes between wakefulness and sleep. By contrast, contextual fear memory consolidation induced less obvious metabolite phenotypes. However, changes in purine metabolites were observed upon both sleep-wake state transitions and contextual fear memory consolidation. Dietary supplementation of certain purine metabolites impaired correlations between conditioned fear responses before and after memory consolidation. These results point toward the importance of purine metabolism in fear memory processing during sleep.

Complex Network Representation of the Structure-Mechanical Property Relationships in Elastomers with Heterogeneous Connectivity.

The complicated structure-property relationships of materials have recently been described using a methodology of data science that is recognized as the fourth paradigm in materials science. In network polymers or elastomers, the manner of connection of the polymer chains among the crosslinking points has a significant effect on the material properties. In this study, we quantitatively evaluate the structural heterogeneity of elastomers at the mesoscopic scale based on complex network, one of the methods used in data science, to describe the elastic properties. It was determined that a unified parameter with topological and spatial information universally describes some parameters related to the stresses. This approach enables us to uncover the role of individual crosslinking points for the stresses, even in complicated structures. Based on the data science, we anticipate that the structure-property relationships of heterogeneous materials can be interpretatively represented using this type of "white box" approach.

Sparse Activity of Hippocampal Adult-Born Neurons during REM Sleep Is Necessary for Memory Consolidation.

The occurrence of dreaming during rapid eye movement (REM) sleep prompts interest in the role of REM sleep in hippocampal-dependent episodic memory. Within the mammalian hippocampus, the dentate gyrus (DG) has the unique characteristic of exhibiting neurogenesis persisting into adulthood. Despite their small numbers and sparse activity, adult-born neurons (ABNs) in the DG play critical roles in memory; however, their memory function during sleep is unknown. Here, we investigate whether young ABN activity contributes to memory consolidation during sleep using Ca2+ imaging in freely moving mice. We found that contextual fear learning recruits a population of young ABNs that are reactivated during subsequent REM sleep against a backdrop of overall reduced ABN activity. Optogenetic silencing of this sparse ABN activity during REM sleep alters the structural remodeling of spines on ABN dendrites and impairs memory consolidation. These findings provide a causal link between ABN activity during REM sleep and memory consolidation.

The gradual evolution of buyer-seller networks and their role in aggregate fluctuations.

Buyer-seller relationships among firms can be regarded as a longitudinal network in which the connectivity pattern evolves as each firm receives productivity shocks. Based on a data set describing the evolution of buyer-seller links among 55,608 firms over a decade and structural equation modeling, we find some evidence that interfirm networks evolve reflecting a firm's local decisions to mitigate adverse effects from neighbor firms through interfirm linkage, while enjoying positive effects from them. As a result, link renewal tends to have a positive impact on the growth rates of firms. We also investigate the role of networks in aggregate fluctuations.

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

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.