1,720,981 research outputs found
A Dynamic Latent-Space Model for Asset Clustering
Full text linkPeriods of financial turmoil are not only characterized by higher correlation across assets but also by modifications in their overall clustering structure. In this work, we develop a dynamic Latent-Space mixture model for capturing changes in the clustering structure of financial assets at a fine scale. Through this model, we are able to project stocks onto a lower dimensional manifold and detect the presence of clusters. The infinite-mixture assumption ensures tractability in inference and accommodates cases in which the number of clusters is large. The Bayesian framework we rely on accounts for uncertainty in the parameters’ space and allows for the inclusion of prior knowledge. After having tested our model’s effectiveness and inference on a suitable synthetic dataset, we apply the model to the cross-correlation series of two reference stock indices. Our model correctly captures the presence of time-varying asset clustering. Moreover, we notice how assets’ latent coordinates may be related to relevant financial factors such as market capitalization and volatility. Finally, we find further evidence that the number of clusters seems to soar in periods of financial distress
Time-Varying Assets Clustering via Identity-Link Latent-Space Infinite Mixture: An Application on DAX Components
No full textFinance literature suggests that cross-correlations among assets increase during periods of financial distress, and that cross-correlation’s very own clustering structure varies over time. This work proposes an Identity-Link Latent-Space Infinite-Mixture model to analyze the clustering structure of cross-correlation over time. The model allows for the representation of stocks on a d-dimensional Euclidean space and the clustering of assets into groups. Model estimation is carried out within a Bayesian framework, which allows including prior extra-sample information in the inference and accounting for parameter uncertainty. We apply the model to time-varying correlations among the DAX components. We find evidence of clustering effects and positive dependence between the number of clusters and both annualized volatility and average cross-correlation
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
From confirmation bias to echo-chambers: a data-driven approach
No full textPer gettare una luce sul fenomeno della disinformazione, in questo rapporto presenteremo e discuteremo alcune prove scientifiche provenienti da un approccio basato sui dati per comprendere l’interazione tra utenti, i social media e il consumo di notizie. Internet e i social media hanno cambiato l’interazione tra utenti e informazioni. I nostri risultati hanno mostrato che gli utenti online tendono a selezionare le informazioni che confermano la loro visione del mondo nonostante tutte le informazioni in contraddizione con questa visione, e che tale tendenza favorisca l’emergere di gruppi polarizzati attorno a narrative condivise.To cast a light on the phenomenon of misinformation, in this report we will present and discuss scientific evidence coming from a data-driven approach to understand the interplay between users, social media and news consumption. Internet and social media changed the interaction between users and information. Our results showed that online users tend to select information confirming their worldview in spite of dissenting information and that such a trend fosters the emergence of polarized groups around shared narratives
Media bias and polarization through the lens of a Markov switching latent space network model
Full text linkNews outlets are now more than ever incentivized to provide their audience with slanted news, while the intrinsic homophilic nature of online social media may exacerbate polarized opinions. Here we propose a new dynamic latent space model for time-varying online audience-duplication networks, which exploits social media content to conduct inference on media bias and polarization of news outlets. We contribute to the literature in several directions: (1) Our model provides a novel measure of media bias that combines information from both network data and text-based indicators; (2) we endow our model with Markov-switching dynamics to capture polarization regimes while maintaining a parsimonious specification; (3) we contribute to the literature on the statistical properties of latent space network models. The proposed model is applied to a set of data on the online activity of national and local news outlets from four European countries in the years 2015 and 2016. We find evidence of a strong positive correlation between our media slant measure and a well-grounded external source of media bias. In addition, we provide insight into the polarization regimes across the four countries considered
Multiple Equilibria and the Phillips Curve: Do Agents Always Underreact?
Full text linkWe study a New Keynesian Phillips curve in which agents deviate from the rational expectation paradigm and forecast inflation using a simple, potentially misspecified autoregressive rule. Consistency criteria à la Hommes and Zhu (2014) between perceived and actual laws of motion of inflation might allow for multiple expectational equilibria. Unfortunately, multiple equilibria models pose challenges for empirical validation. This paper proposes a latent Markov chain process to dynamically separate such equilibria. Moreover, an original Bayesian inference approach based on hierarchical priors is introduced, which naturally offers the possibility of incorporating equilibrium-identifying constraints with various degrees of prior beliefs. Finally, an inference procedure is proposed to assess a posteriori the probability that the theoretical constraints are satisfied and to estimate the equilibrium changes over time. We show that common prior assumptions regarding structural parameters favor the separation of equilibria, thereby making the Bayesian inference a natural framework for Markov-switching Phillips curve models. Empirical evidence obtained from observed inflation, output gap, and the consensus expectations from the Survey of Professional Forecasters supports multiple equilibria, and we find evidence of temporal variation in over-and under-reaction patterns, which, to the best of our knowledge, have not been previously documented. Specifically, we observe that agents tend to underreact to shocks when inflation is high and persistent, whereas they behave substantially as fully informed forecasters when the inflation level is low and stable, i.e., after the mid-nineties. We also find that the model does not suffer from the missing disinflation puzzle during the Great Recession
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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