5,767 research outputs found
Diagnosis and Prediction of Market Rebounds in Financial Markets
We introduce the concept of "negative bubbles" as the mirror image of
standard financial bubbles, in which positive feedback mechanisms may lead to
transient accelerating price falls. To model these negative bubbles, we adapt
the Johansen-Ledoit-Sornette (JLS) model of rational expectation bubbles with a
hazard rate describing the collective buying pressure of noise traders. The
price fall occurring during a transient negative bubble can be interpreted as
an effective random downpayment that rational agents accept to pay in the hope
of profiting from the expected occurrence of a possible rally. We validate the
model by showing that it has significant predictive power in identifying the
times of major market rebounds. This result is obtained by using a general
pattern recognition method which combines the information obtained at multiple
times from a dynamical calibration of the JLS model. Error diagrams, Bayesian
inference and trading strategies suggest that one can extract genuine
information and obtain real skill from the calibration of negative bubbles with
the JLS model. We conclude that negative bubbles are in general predictably
associated with large rebounds or rallies, which are the mirror images of the
crashes terminating standard bubbles.Comment: 49 pages, 14 figure
Biases in metallicity measurements from global galaxy spectra: the effects of flux-weighting and diffuse ionized gas contamination
Galaxy metallicity scaling relations provide a powerful tool for
understanding galaxy evolution, but obtaining unbiased global galaxy gas-phase
oxygen abundances requires proper treatment of the various line-emitting
sources within spectroscopic apertures. We present a model framework that
treats galaxies as ensembles of HII and diffuse ionized gas (DIG) regions of
varying metallicities. These models are based upon empirical relations between
line ratios and electron temperature for HII regions, and DIG strong-line ratio
relations from SDSS-IV MaNGA IFU data. Flux-weighting effects and DIG
contamination can significantly affect properties inferred from global galaxy
spectra, biasing metallicity estimates by more than 0.3 dex in some cases. We
use observationally-motivated inputs to construct a model matched to typical
local star-forming galaxies, and quantify the biases in strong-line ratios,
electron temperatures, and direct-method metallicities as inferred from global
galaxy spectra relative to the median values of the HII region distributions in
each galaxy. We also provide a generalized set of models that can be applied to
individual galaxies or galaxy samples in atypical regions of parameter space.
We use these models to correct for the effects of flux-weighting and DIG
contamination in the local direct-method mass-metallicity and fundamental
metallicity relations, and in the mass-metallicity relation based on
strong-line metallicities. Future photoionization models of galaxy line
emission need to include DIG emission and represent galaxies as ensembles of
emitting regions with varying metallicity, instead of as single HII regions
with effective properties, in order to obtain unbiased estimates of key
underlying physical properties.Comment: 37 pages, 29 figures, 4 tables. Accepted to ApJ. See Figures 15-17
for typical global galaxy biases in strong-line ratios, electron
temperatures, and direct-method metallicitie
Species Invasion in a Network Population Model
The introduction and spread of invasive species is increasingly driven by the expansion of human-made transportation routes. We formulate a network model of biotic invasion incorporating logistic growth and dispersal along a network, and present analyses of the model. We introduce small world networks and use them to investigate the role of network properties and long-distance dispersal on spread dynamics. Lastly we present comparisons between the stochastic and deterministic models to illustrate the effects of stochasticity on invasive species spread dynamics
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