Predicting trend reversals using market instantaneous state

Collective behaviours taking place in financial markets reveal strongly correlated states especially during a crisis period. A natural hypothesis is that trend reversals are also driven by mutual influences between the different stock exchanges. Using a maximum entropy approach, we find coordinated behaviour during trend reversals dominated by the pairwise component. In particular, these events are predicted with high significant accuracy by the ensemble's instantaneous state.Comment: 18 pages, 15 figure

A statistical physics perspective on criticality in financial markets

Stock markets are complex systems exhibiting collective phenomena and particular features such as synchronization, fluctuations distributed as power-laws, non-random structures and similarity to neural networks. Such specific properties suggest that markets operate at a very special point. Financial markets are believed to be critical by analogy to physical systems but few statistically founded evidence have been given. Through a data-based methodology and comparison to simulations inspired by statistical physics of complex systems, we show that the Dow Jones and indices sets are not rigorously critical. However, financial systems are closer to the criticality in the crash neighborhood.Comment: 23 pages, 19 figure

Collective behaviours in the stock market -- A maximum entropy approach

Scale invariance, collective behaviours and structural reorganization are crucial for portfolio management (portfolio composition, hedging, alternative definition of risk, etc.). This lack of any characteristic scale and such elaborated behaviours find their origin in the theory of complex systems. There are several mechanisms which generate scale invariance but maximum entropy models are able to explain both scale invariance and collective behaviours. The study of the structure and collective modes of financial markets attracts more and more attention. It has been shown that some agent based models are able to reproduce some stylized facts. Despite their partial success, there is still the problem of rules design. In this work, we used a statistical inverse approach to model the structure and co-movements in financial markets. Inverse models restrict the number of assumptions. We found that a pairwise maximum entropy model is consistent with the data and is able to describe the complex structure of financial systems. We considered the existence of a critical state which is linked to how the market processes information, how it responds to exogenous inputs and how its structure changes. The considered data sets did not reveal a persistent critical state but rather oscillations between order and disorder. In this framework, we also showed that the collective modes are mostly dominated by pairwise co-movements and that univariate models are not good candidates to model crashes. The analysis also suggests a genuine adaptive process since both the maximum variance of the log-likelihood and the accuracy of the predictive scheme vary through time. This approach may provide some clue to crash precursors and may provide highlights on how a shock spreads in a financial network and if it will lead to a crash. The natural continuation of the present work could be the study of such a mechanism.Comment: 146 pages, PhD Thesi

The Effect of Reaction Control System Thruster Plume Impingement on Orion Service Module Solar Array Power Production

NASA's new Orion Crew Exploration Vehicle has geometry that orients the reaction control system (RCS) thrusters such that they can impinge upon the surface of Orion's solar array wings (SAW). Plume impingement can cause Paschen discharge, chemical contamination, thermal loading, erosion, and force loading on the SAW surface, especially when the SAWs are in a worst-case orientation (pointed 45 towards the aft end of the vehicle). Preliminary plume impingement assessment methods were needed to determine whether in-depth, timeconsuming calculations were required to assess power loss. Simple methods for assessing power loss as a result of these anomalies were developed to determine whether plume impingement induced power losses were below the assumed contamination loss budget of 2 percent. This paper details the methods that were developed and applies them to Orion's worst-case orientation

Detecting and distinguishing transitions in ecological systems: model and data-driven approaches

There exists a plethora of systems that have the capacity to undergo sudden transitions that result in a significantly different state or dynamic. Consider the collapse of fisheries, outbreak of disease or transition to a 'Hothouse Earth' to name a few. The common factor among these transitions is mathematical - they are the result of crossing a bifurcation point. This thesis is concerned with the detection and description of these bifurcations from time series data, and the mechanisms that lead to these transitions. We begin in the domain of climate change, where models of the climate system are extremely sophisticated, but those that incorporate social dynamics and its two-way coupling with climate dynamics are lacking. In developing a simple socio-climate model, we show how mechanisms such as social learning, social norms, and perceived mitigation costs play a major role in climate change trajectories. These social effects can strongly determine the predicted peak global temperature anomaly, how quickly human populations respond to a changing climate, and how we can chart optimal pathways to climate change mitigation. However, we also show that if the climate model is subject to a tipping point, the climate can transition to a new state before mitigating behaviour becomes sufficiently widespread to prevent the transition. This motivates a need for early warning signals (EWS) of tipping points. Hence, in the next chapter we focus on the development of EWS in time series data that can be used to detect an upcoming bifurcation. This thesis develops two 'spectral EWS', which are derived from the power spectrum. We show that the peak in the power spectrum provides a more sensitive and conservative EWS when compared to conventional metrics, and the shape of the power spectrum, quantified using AIC weights, provides clues as to the type of approaching bifurcation. We validate these spectral EWS with empirical data from a predator-prey system. Finally we focus on EWS for population extinction, where we study the efficacy of EWS in seasonal environments. We find that conventional EWS prevail under seasonal environments, however asymmetries exist in higher-order metrics such as skewness and kurtosis that could be used to distinguish the driver of extinction. To conclude, nonlinear behaviour arising from social learning and social norms yield bifurcations that have profound impacts on future trajectories of climate change, and bifurcations can be anticipated across a wide range of systems using spectral EWS, that also provide information on the type of bifurcation. The further development of generic and system-specific EWS will play an important role in preserving healthy ecosystem functioning in the Anthropocene

Systematic prey preference by introduced mice exhausts the ecosystem on Antipodes Island

For assistance collecting samples in the field the authors thank David Thompson, Erica Sommer, David Boyle and Mark Fraser in summer 2011, Helen Nathan, Terry Greene and Graeme Elliott in winter 2013, Fin Cox in autumn 2016 and Jose Luis Herrera in winter 2016. Thanks to the Department of Conservation, Murihiku, for logistical support, and Hank Haazen and crew of the Tiama for transport. Funding was provided for the summer 2011 expedition by NIWA and winter 2013 expedition by the National Geographic Society (Grant No. 9322-13). Thanks to Stephen Thorpe, Robert Hoare, and John Marris for taxonomic identification of invertebrate samples. Thanks to Surrya Khanam for laboratory sorting, Julie Brown and Anna Kilimnik for stable isotope laboratory analyses and Wendy Nelson for macroalgae identification. JCR is currently funded by the Royal Society of New Zealand Rutherford Discovery Fellowship (Grant No. RDF-UOA1404). TWB is currently funded by the European Union's Horizon 2020 research and innovation programme Marie Skłodowska-Curie Fellowship (Grant No. 747120). Thanks to Katherine Russell and two anonymous reviewers for feedback on the manuscript. This research was conducted under DOC entry (SO-29716-LND 1011/35) and research (SO-29140-FAU 1011/20) permits, and University of Auckland Animal Ethics Committee approval (R845).Peer reviewedPublisher PD

Assessing the reliability of uptake and elimination kinetics modelling approaches for estimating bioconcentration factors in the freshwater invertebrate, Gammarus pulex

This study considers whether the current standard toxicokinetic methods are an accurate and applicable assessment of xenobiotic exposure in an aquatic freshwater invertebrate. An in vivo exposure examined the uptake and elimination kinetics for eight pharmaceutical compounds in the amphipod crustacean, Gammarus pulex by measuring their concentrations in both biological material and in the exposure medium over a 96 h period. Selected pharmaceuticals included two anti-inflammatories (diclofenac and ibuprofen), two beta-blockers (propranolol and metoprolol), an anti-depressant (imipramine), an anti-histamine (ranitidine) and two beta-agonists (formoterol and terbutaline). Kinetic bioconcentration factors (BCFs) for the selected pharmaceuticals were derived from a first-order one-compartment model using either the simultaneous or sequential modelling methods. Using the simultaneous method for parameter estimation, BCF values ranged from 12 to 212. In contrast, the sequential method for parameter estimation resulted in bioconcentration factors ranging from 19 to 4533. Observed toxicokinetic plots showed statistically significant lack-of-fits and further interrogation of the models revealed a decreasing trend in the uptake rate constant over time for rantidine, diclofenac, imipramine, metoprolol, formoterol and terbutaline. Previous published toxicokinetic data for 14 organic micro-pollutants were also assessed and similar trends were identified to those observed in this study. The decreasing trend of the uptake rate constant over time highlights the need to interpret modelled data more comprehensively to ensure uncertainties associated with uptake and elimination parameters for determining bioconcentration factors are minimised

Statistical pairwise interaction model of stock market

Financial markets are a classical example of complex systems as they comprise many interacting stocks. As such, we can obtain a surprisingly good description of their structure by making the rough simplification of binary daily returns. Spin glass models have been applied and gave some valuable results but at the price of restrictive assumptions on the market dynamics or others are agent-based models with rules designed in order to recover some empirical behaviours. Here we show that the pairwise model is actually a statistically consistent model with observed first and second moments of the stocks orientation without making such restrictive assumptions. This is done with an approach based only on empirical data of price returns. Our data analysis of six major indices suggests that the actual interaction structure may be thought as an Ising model on a complex network with interaction strengths scaling as the inverse of the system size. This has potentially important implications since many properties of such a model are already known and some techniques of the spin glass theory can be straightforwardly applied. Typical behaviours, as multiple equilibria or metastable states, different characteristic time scales, spatial patterns, order-disorder, could find an explanation in this picture.Comment: 11 pages, 8 figure

Long-term psychosocial impact reported by childhood critical illness survivors: a systematic review

Aim: To undertake a qualitative systematic review that explores psychological and social impact, reported directly from children and adolescents at least 6 months after their critical illness. Background: Significant advances in critical care have reduced mortality from childhood critical illness, with the majority of patients being discharged alive. However, it is widely reported that surviving critical illness can be traumatic for both children and their family. Despite a growing body of literature in this field, the psychological and social impact of life threatening critical illness on child and adolescent survivors, more than 6 months post event, remains under-reported. Data sources: Searches of six online databases were conducted up to February 2012. Review methods: Predetermined criteria were used to select studies. Methodological quality was assessed using a standardized checklist. An adapted version of the thematic synthesis approach was applied to extract, code and synthesize data. Findings: Three studies met the inclusion criteria, which were all of moderate methodological quality. Initial coding and synthesis of data resulted in five descriptive themes: confusion and uncertainty, other people’s narratives, focus on former self and normality, social isolation and loss of identity, and transition and transformation. Further synthesis culminated in three analytical themes that conceptualize the childhood survivors’ psychological and social journey following critical illness. Conclusions: Critical illness in childhood can expose survivors to a complex trajectory of recovery, with enduring psychosocial adversity manifesting in the long term. Nurses and other health professionals must be aware and support the potential multifaceted psychosocial needs that may arise. Parents and families are identified as fundamental in shaping psychological and social well-being of survivors. Therefore intensive care nurses must take opportunities to raise parents’ awareness of the journey of survival and provide appropriate support. Further empirical research is warranted to explore the deficits identified with the existing literature