1,222 research outputs found
Long-range memory model of trading activity and volatility
Earlier we proposed the stochastic point process model, which reproduces a
variety of self-affine time series exhibiting power spectral density S(f)
scaling as power of the frequency f and derived a stochastic differential
equation with the same long range memory properties. Here we present a
stochastic differential equation as a dynamical model of the observed memory in
the financial time series. The continuous stochastic process reproduces the
statistical properties of the trading activity and serves as a background model
for the modeling waiting time, return and volatility. Empirically observed
statistical properties: exponents of the power-law probability distributions
and power spectral density of the long-range memory financial variables are
reproduced with the same values of few model parameters.Comment: 12 pages, 5 figure
Searching Data: A Review of Observational Data Retrieval Practices in Selected Disciplines
A cross-disciplinary examination of the user behaviours involved in seeking
and evaluating data is surprisingly absent from the research data discussion.
This review explores the data retrieval literature to identify commonalities in
how users search for and evaluate observational research data. Two analytical
frameworks rooted in information retrieval and science technology studies are
used to identify key similarities in practices as a first step toward
developing a model describing data retrieval
Generic Multifractality in Exponentials of Long Memory Processes
We find that multifractal scaling is a robust property of a large class of
continuous stochastic processes, constructed as exponentials of long-memory
processes. The long memory is characterized by a power law kernel with tail
exponent , where . This generalizes previous studies
performed only with (with a truncation at an integral scale), by
showing that multifractality holds over a remarkably large range of
dimensionless scales for . The intermittency multifractal coefficient
can be tuned continuously as a function of the deviation from 1/2 and of
another parameter embodying information on the short-range amplitude
of the memory kernel, the ultra-violet cut-off (``viscous'') scale and the
variance of the white-noise innovations. In these processes, both a viscous
scale and an integral scale naturally appear, bracketing the ``inertial''
scaling regime. We exhibit a surprisingly good collapse of the multifractal
spectra on a universal scaling function, which enables us to derive
high-order multifractal exponents from the small-order values and also obtain a
given multifractal spectrum by different combinations of and
.Comment: 10 pages + 9 figure
Anyone Can Become a Troll: Causes of Trolling Behavior in Online Discussions
In online communities, antisocial behavior such as trolling disrupts
constructive discussion. While prior work suggests that trolling behavior is
confined to a vocal and antisocial minority, we demonstrate that ordinary
people can engage in such behavior as well. We propose two primary trigger
mechanisms: the individual's mood, and the surrounding context of a discussion
(e.g., exposure to prior trolling behavior). Through an experiment simulating
an online discussion, we find that both negative mood and seeing troll posts by
others significantly increases the probability of a user trolling, and together
double this probability. To support and extend these results, we study how
these same mechanisms play out in the wild via a data-driven, longitudinal
analysis of a large online news discussion community. This analysis reveals
temporal mood effects, and explores long range patterns of repeated exposure to
trolling. A predictive model of trolling behavior shows that mood and
discussion context together can explain trolling behavior better than an
individual's history of trolling. These results combine to suggest that
ordinary people can, under the right circumstances, behave like trolls.Comment: Best Paper Award at CSCW 201
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