97 research outputs found
Extreme Events in Hydrology: an approach using Exploratory Statistics and the Generalized Pareto Distribution. Performances and properties of the GPD estimators with outliers and rounded-off datasets
Two large databases of daily cumulated rainfall are checked with the tools of the Exploratory statistics. The analysis allows to discover not common artefacts in the first database (rounding-off of data with different rounding-off rules) and several errors in the other one. The best statistical model to fit data is selected using the L-Moments ratio diagram as a tool to explore the accommodation of each dataset to other alternative
models. This tool suggests the Generalized Pareto Distribution as the best statistical model for this data, but the application of this distribution requires an estimate of the optimal threshold for each dataset. A detailed analysis of the present techniques for the
optimal threshold selection is performed and a new approach based on quantile sums is proposed. Furthermore the performances of the GPD parameters estimators are checked
for robustness against spurious rounded-off data and severe outliers
The Accounting Network: how financial institutions react to systemic crisis
The role of Network Theory in the study of the financial crisis has been
widely spotted in the latest years. It has been shown how the network topology
and the dynamics running on top of it can trigger the outbreak of large
systemic crisis. Following this methodological perspective we introduce here
the Accounting Network, i.e. the network we can extract through vector
similarities techniques from companies' financial statements. We build the
Accounting Network on a large database of worldwide banks in the period
2001-2013, covering the onset of the global financial crisis of mid-2007. After
a careful data cleaning, we apply a quality check in the construction of the
network, introducing a parameter (the Quality Ratio) capable of trading off the
size of the sample (coverage) and the representativeness of the financial
statements (accuracy). We compute several basic network statistics and check,
with the Louvain community detection algorithm, for emerging communities of
banks. Remarkably enough sensible regional aggregations show up with the
Japanese and the US clusters dominating the community structure, although the
presence of a geographically mixed community points to a gradual convergence of
banks into similar supranational practices. Finally, a Principal Component
Analysis procedure reveals the main economic components that influence
communities' heterogeneity. Even using the most basic vector similarity
hypotheses on the composition of the financial statements, the signature of the
financial crisis clearly arises across the years around 2008. We finally
discuss how the Accounting Networks can be improved to reflect the best
practices in the financial statement analysis
Voting Behavior, Coalitions and Government Strength through a Complex Network Analysis
We analyze the network of relations between parliament members according to
their voting behavior. In particular, we examine the emergent community
structure with respect to political coalitions and government alliances. We
rely on tools developed in the Complex Network literature to explore the core
of these communities and use their topological features to develop new metrics
for party polarization, internal coalition cohesiveness and government
strength. As a case study, we focus on the Chamber of Deputies of the Italian
Parliament, for which we are able to characterize the heterogeneity of the
ruling coalition as well as parties specific contributions to the stability of
the government over time. We find sharp contrast in the political debate which
surprisingly does not imply a relevant structure based on establised parties.
We take a closer look to changes in the community structure after parties split
up and their effect on the position of single deputies within communities.
Finally, we introduce a way to track the stability of the government coalition
over time that is able to discern the contribution of each member along with
the impact of its possible defection. While our case study relies on the
Italian parliament, whose relevance has come into the international spotlight
in the present economic downturn, the methods developed here are entirely
general and can therefore be applied to a multitude of other scenarios.Comment: 6 pages, 4 figure
Analyzing the emotional impact of COVID-19 with Twitter data: Lessons from a B-VAR analysis on Italy
: The novel coronavirus 2019 revolutionized the way of living and the communication of people making social media a popular tool to express concerns and perceptions. Starting from this context we built an original database based on the Twitter users' emotions shown in the early weeks of the pandemic in Italy. Specifically, using a single index we measured the feelings of four groups of stakeholders (journalists, people, doctors, and politicians), in three groups of Italian regions (0,1,2), grouped according to the impact of the COVID-19 crises as defined by the Conte Government Ministerial Decree (8th March 2020). We then applied B-VAR techniques to analyze the sentiment relationships between the groups of stakeholders in every Region Groups. Results show a high influence of doctors at the beginning of the epidemic in the Group that includes most of Italian regions (Group 0), and in Lombardy that has been the region of Italy hit the most by the pandemic (Group 2). Our outcomes suggest that, given the role played by stakeholders and the COVID-19 magnitude, health policy interventions based on communication strategies may be used as best practices to develop regional mitigation plans for the containment and contrast of epidemiological emergencies
Tweet-tales: moods of socio-economic crisis?
The widespread adoption of highly interactive social media like Twitter, Facebook and other platforms allow users to communicate moods and opinions to their social network. Those platforms represent an unprecedented source of information about human habits and socio-economic interactions. Several new studies have started to exploit the potential of these big data as fingerprints of economic and social interactions.
The present analysis aims at exploring the informative power of indicators derived from social media activity, with the aim to trace some preliminary guidelines to investigate the eventual correspondence between social media indices and available labour market indicators at a territorial level. The study is based on a large dataset of about 4 million Italian-language tweets collected from October 2014 to December 2015, filtered by a set of specific keywords related to the labour market. With techniques from machine learning and user’s geolocalization, we were able to subset the tweets on specific topics in all Italian provinces. The corpus of tweets is then analyzed with linguistic tools and hierarchical clustering analysis. A comparison with traditional economic indicators suggests a strong need for further cleaning procedures, which are then developed in detail. As data from social networks are easy to obtain, this represents a very first attempt to evaluate their informative power in the Italian context, which is of potentially high importance in economic and social research
DebtRank: Too Central to Fail? Financial Networks, the FED and Systemic Risk
Systemic risk, here meant as the risk of default of a large portion of the financial system, depends on the network of financial exposures among institutions. However, there is no widely accepted methodology to determine the systemically important nodes in a network. To fill this gap, we introduce, DebtRank, a novel measure of systemic impact inspired by feedback-centrality. As an application, we analyse a new and unique dataset on the USD 1.2 trillion FED emergency loans program to global financial institutions during 2008–2010. We find that a group of 22 institutions, which received most of the funds, form a strongly connected graph where each of the nodes becomes systemically important at the peak of the crisis. Moreover, a systemic default could have been triggered even by small dispersed shocks. The results suggest that the debate on too-big-to-fail institutions should include the even more serious issue of too-central-to-fail
The Global Health Networks: A Comparative Analysis of Tuberculosis, Malaria and Pneumonia Using Social Media Data
Global health networks (GHNs) of organizations fighting major health threats
represent a useful strategy to respond to the challenge of mobilizing and coordinating
different types of health organizations across borders toward a common goal.
In this paper we reconstruct the GHNs of malaria, tuberculosis and pneumonia by
creating a new unique database of health organizations from the official Twitter accounts
of each organization. We use a majority voter Multi Naive Bayes classifier to
discover, among the Twitter users, the ones that represent organizations or groups active
in each disease area. We perform a social network analysis (SNA) of the global
health networks (GHNs) to evaluate the structure of the network and the role and
performance of the organizations in each network. We find evidence that the GHN
of malaria, TBC and pneumonia are different in terms of performance and leadership,
geographical coverage as well as Twitter popularity. Our analysis validate the
use of social media to analyze GHNs, their effectiveness and to mobilize the global
community toward global sustainable development
Extraction of temporal networks from term co-occurrences in online textual sources
A stream of unstructured news can be a valuable source of hidden relations between different entities, such as financial institutions, countries, or persons. We present an approach to continuously collect online news, recognize relevant entities in them, and extract time-varying networks. The nodes of the network are the entities, and the links are their co-occurrences. We present a method to estimate the significance of co-occurrences, and a benchmark model against which their robustness is evaluated. The approach is applied to a large set of financial news, collected over a period of two years. The entities we consider are 50 countries which issue sovereign bonds, and which are insured by Credit Default Swaps (CDS) in turn. We compare the country co-occurrence networks to the CDS networks constructed from the correlations between the CDS. The results show relatively small, but significant overlap between the networks extracted from the news and those from the CDS correlations
Twitter-based analysis of the dynamics of collective attention to political parties
Large-scale data from social media have a significant potential to describe
complex phenomena in real world and to anticipate collective behaviors such as
information spreading and social trends. One specific case of study is
represented by the collective attention to the action of political parties. Not
surprisingly, researchers and stakeholders tried to correlate parties' presence
on social media with their performances in elections. Despite the many efforts,
results are still inconclusive since this kind of data is often very noisy and
significant signals could be covered by (largely unknown) statistical
fluctuations. In this paper we consider the number of tweets (tweet volume) of
a party as a proxy of collective attention to the party, identify the dynamics
of the volume, and show that this quantity has some information on the
elections outcome. We find that the distribution of the tweet volume for each
party follows a log-normal distribution with a positive autocorrelation of the
volume over short terms, which indicates the volume has large fluctuations of
the log-normal distribution yet with a short-term tendency. Furthermore, by
measuring the ratio of two consecutive daily tweet volumes, we find that the
evolution of the daily volume of a party can be described by means of a
geometric Brownian motion (i.e., the logarithm of the volume moves randomly
with a trend). Finally, we determine the optimal period of averaging tweet
volume for reducing fluctuations and extracting short-term tendencies. We
conclude that the tweet volume is a good indicator of parties' success in the
elections when considered over an optimal time window. Our study identifies the
statistical nature of collective attention to political issues and sheds light
on how to model the dynamics of collective attention in social media.Comment: 16 pages, 7 figures, 3 tables. Published in PLoS ON
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