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This paper sheds new light on herding of institutional investors by using a unique database that identifies every transaction made by financial institutions in the German stock market. First, the analysis reveals that herding behavior of institutions occurs daily. Second, replication of the analysis with low-frequency and anonymous transaction data indicates that previous studies overestimate herding. Third, our results suggest that herding by large financial institutions mainly results from shared preference and investment styles. Fourth, a panel analysis shows that herding on the sell side in stocks is positively related to past returns and past volatility, whereas herding on the buy side is negatively related to these variables. Hence, large financial institutions do not demonstrate positive feedback strategies. -- Investor Behavior ; Institutional Trading ; Stock Prices

In recent years, much has been written on 'big data' in both the popular and academic press. After the hubristic declaration of the 'end of theory' more nuanced arguments have emerged, suggesting that increasingly pervasive data collection and quantification may have significant implications for the social sciences, even if the social, scientific, political, and economic agendas behind big data are less new than they are often portrayed. Compared to the boosterish tone of much of its press, academic critiques of big data have been relatively muted, often focusing on the continued importance of more traditional forms of domain knowledge and expertise. Indeed, many academic responses to big data enthusiastically celebrate the availability of new data sources and the potential for new insights and perspectives they may enable. Undermining many of these critiques is a lack of attention to the role of technology in society, particularly with respect to the labor process, the continued extension of labor relations into previously private times and places, and the commoditization of more and more aspects of everyday life. In this article, we parse a variety of big data definitions to argue that it is only when individual datums by the million, billion, or more are linked together algorithmically that 'big data' emerges as a commodity. Such decisions do not occur in a vacuum but as part of an asymmetric power relationship in which individuals are dispossessed of the data they generate in their day-to-day lives. We argue that the asymmetry of this data capture process is a means of capitalist 'accumulation by dispossession' that colonizes and commodifies everyday life in ways previously impossible. Situating the promises of 'big data' within the utopian imaginaries of digital frontierism, we suggest processes of data colonialism are actually unfolding behind these utopic promises. Amid private corporate and academic excitement over new forms of data analysis and visualization, situating big data as a form of capitalist ...

In recent years, much has been written on 'big data' in both the popular and academic press. After the hubristic declaration of the 'end of theory' more nuanced arguments have emerged, suggesting that increasingly pervasive data collection and quantification may have significant implications for the social sciences, even if the social, scientific, political, and economic agendas behind big data are less new than they are often portrayed. Compared to the boosterish tone of much of its press, academic critiques of big data have been relatively muted, often focusing on the continued importance of more traditional forms of domain knowledge and expertise. Indeed, many academic responses to big data enthusiastically celebrate the availability of new data sources and the potential for new insights and perspectives they may enable. Undermining many of these critiques is a lack of attention to the role of technology in society, particularly with respect to the labor process, the continued extension of labor relations into previously private times and places, and the commoditization of more and more aspects of everyday life. In this article, we parse a variety of big data definitions to argue that it is only when individual datums by the million, billion, or more are linked together algorithmically that 'big data' emerges as a commodity. Such decisions do not occur in a vacuum but as part of an asymmetric power relationship in which individuals are dispossessed of the data they generate in their day-to-day lives. We argue that the asymmetry of this data capture process is a means of capitalist 'accumulation by dispossession' that colonizes and commodifies everyday life in ways previously impossible. Situating the promises of 'big data' within the utopian imaginaries of digital frontierism, we suggest processes of data colonialism are actually unfolding behind these utopic promises. Amid private corporate and academic excitement over new forms of data analysis and visualization, situating big data as a form of capitalist expropriation and dispossession stresses the urgent need for critical, theoretical understandings of data and society.

AbstractThis study provides one of the first investigations of deterministic chaos for Canadian securities data; specifically, value‐weighted monthly TSE‐300 index prices and daily total returns over the period January 1977 through December 1991. We applied two well‐known methods for detecting deterministic chaos, the Grassberger and Procaccia method (Grassberger & Procaccia, 1983), and the BDS statistic (Brock, Dechert, & Scheinkman, 1987). The results suggest that the monthly prices are chaotic. Little evidence of deterministic chaos, however, appears to be present in the daily returns. We offer use of inappropriate lags as a possible explanation of the conflicting results in previous studies.RésuméLes auteurs font état des résultats de l'une des premières recherches portant sur le chaos déterministe dans les données relatives aux valeurs mobilières canadiennes, et plus précisément dans la valeur mensuelle pondérée du cours des actions de l'indice TSE‐300 et les rendements totaux quotidiens observés sur une période s'échelonnant de janvier 1977 à décembre 1991. Les auteurs appliquent deux méthodes bien connues de détection du chaos déterministe: la méthode Grassberger et Procaccia (Grassberger & Procaccia, 1983) et la statistique BDS (Brock, Dechert & Scheinkman, 1987). Suivant les résultats obtenus, les cours mensuels seraient chaotiques. Ces résultats ne permettent cependant pas de conclure au chaos déterministe dans le cas des rendements quotidiens. Les auteurs suggèrent que l'utilisation d'un nombre inappropriéde retards dans les variables est une explication possible des différences de résultats des études antérieures.

This paper models the dynamics of Japanese government bond (JGB) nominal yields using daily data. Models of government bond yields based on daily data, such as those presented in this paper, can be useful not only to investors and market analysts, but also to central bankers and other policymakers for assessing financial conditions and macroeconomic developments in real time. The paper shows that long-term JGB nominal yields can be modeled using the short-term interest rate on Treasury bills, the equity index, the exchange rate, commodity price index, and other key financial variables.

Abstract. This work aims to generate and evaluate regional rainfall thresholds obtained from a combination of high-resolution gridded rainfall data, developed by the National Service of Meteorology and Hydrology of Peru, and information from observed shallow landslide events. The landslide data were associated with rainfall data, determining triggering and non-triggering rainfall events with rainfall properties from which rainfall thresholds are determined. The validation of the performance of the thresholds is carried out with events that occurred during 2020 and focuses on evaluating the operability of these thresholds in landslide warning systems in Peru. The thresholds are determined for 11 rainfall regions. The method of determining the thresholds is based on an empirical–statistical approach, and the predictive performance of the thresholds is evaluated with true skill statistics. The best predictive performance is the mean daily intensity–duration (Imean−D) threshold curve, followed by accumulated rainfall E. This work is the first estimation of regional thresholds on a country scale to better understand landslides in Peru, and the results obtained reveal the potential of using thresholds in the monitoring and forecasting of shallow landslides caused by intense rainfall and in supporting the actions of disaster risk management.