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