Asymptotic behaviour of two-point functions in multi-species models

We extract the long-distance asymptotic behaviour of two-point correlation functions in massless quantum integrable models containing multi-species excitations. For such a purpose, we extend to these models the method of a large-distance regime re-summation of the form factor expansion of correlation functions. The key feature of our analysis is a technical hypothesis on the large-volume behaviour of the form factors of local operators in such models. We check the validity of this hypothesis on the example of the $SU(3)$-invariant XXX magnet by means of the determinant representations for the form factors of local operators in this model. Our approach confirms the structure of the critical exponents obtained previously for numerous models solvable by the nested Bethe Ansatz.Comment: 45 pages, 1 figur

Predicting recessions using trends in the yield spread

The yield spread, measured as the difference between long- and short-term interest rates, is widely regarded as one of the strongest predictors of economic recessions. In this paper, we propose an enhanced recession prediction model that incorporates trends in the value of the yield spread. We expect our model to generate stronger recession signals because a steadily declining value of the yield spread typically indicates growing pessimism associated with the reduced future business activity. We capture trends in the yield spread by considering both the level of the yield spread at a lag of 12 months as well as its value at each of the previous two quarters leading up to the forecast origin, and we evaluate its predictive abilities using both logit and artificial neural network models. Our results indicate that models incorporating information from the time series of the yield spread correctly predict future recession periods much better than models only considering the spread value as of the forecast origin. Furthermore, the results are strongest for our artificial neural network model and logistic regression model that includes interaction terms, which we confirm using both a blocked cross-validation technique as well as an expanding estimation window approach

Financial distress, corporate takeovers and the distress anomaly

Purpose: This paper examines the relation between takeover likelihood and the documented underperformance of distressed company stocks while exploring two competing hypotheses. The failure risk explanation predicts lower returns to distressed firms with high probability of being acquired because the acquisition reduces risk and investors\u27 required return. Conversely, the agency conflicts explanation predicts lower returns when acquisition is unlikely. Design/methodology/approach: The likelihood of receiving a takeover bid is estimated, and portfolio tests explore the underperformance of distressed company stocks relative to non-distressed stocks across varying levels of takeover likelihood. Predictive regressions subsequently examine the relation between distress, takeover exposure and future firm operating performance including how the relation is affected by state anti-takeover laws. Findings: Distressed stocks underperform non-distressed company stocks by economically and statistically significant margins when takeover likelihood is low, yet there is no evidence of underperformance among distressed stocks with moderate or high takeover exposure. Consistent with agency conflicts playing a key role, distressed firms that are disciplined by takeover threats invest more, use more leverage and experience higher future profitability. State-level anti-takeover legislation limits this disciplinary effect, however. Originality/value: The results show that the well-documented distress anomaly is driven by a subset of distressed firms whose managers face limited pressure from the external takeover market. The evidence casts doubt on the failure risk explanation and suggests that agency conflicts play a key role

“Toxic” Schools? How School Exposures During Adolescence Influence Trajectories of Health Through Young Adulthood

© 2020 The Author(s) A large body of research identifies the critical role of early-life social contexts such as neighborhoods and households in shaping life course trajectories of health. Less is known about whether and how school characteristics affect individual health and contribute to population health inequality. However, recent scholarship argues that some school environments are so stressful due to high levels of violence, disorder, and poverty that they may be “toxic” to student health, but this hypothesis has not been tested using population data. Integrating insights from the life course perspective and stress process model, we use rich longitudinal data from the National Longitudinal Study of Adolescent to Adult Health (n = 11,382), diverse markers of physiological functioning and psychological well-being, and multilevel regression models to examine whether and how school characteristics shape trajectories of physiological dysregulation and depressive risk from adolescence through early adulthood. Findings reveal that, across multiple measures of physiological functioning and psychological well-being, the social and structural characteristics of schools play an essential role in shaping health risk from adolescence through young adulthood—long after students left school. In particular, indicators of school-level violence and perceptions of safety and school social disconnectedness had especially strong associations with health risk in both the short- and long-term. School socioeconomic composition was also strongly associated with physiological dysregulation in young adulthood, net of individual and neighborhood socioeconomic exposures. Together, findings from this study suggest that school environments can serve as early-life stressors in the lives of young people that unequally shape health trajectories and contribute to broader patterns of health inequality

Cryptocurrency return reversals

Analysing a set of 200 cryptocurrencies over the period from 2015 to 2019, we document a significant return reversal effect that holds at the daily, weekly, and monthly rebalancing frequencies and is robust to controls for differences in size, turnover, and illiquidity. Moreover, the reversal effect persists during both halves of our sample period and following periods of both high and low market implied volatility. Consistent with the effect being driven by a combination of market inefficiency and compensation for liquidity provision, we find reversals are most pronounced among smaller capitalization and less liquid cryptocurrencies

Sensitivity Studies for the Exercise I-1 of the OECD/UAM Benchmark

OECD/NEA has initiated an international Uncertainty Analysis in Modeling (UAM) benchmark focused on uncertainties in modeling of Light Water Reactor (LWR). The first step of uncertainty propagation is to perform sensitivity to the input data affected by the numerical errors and physical models. The objective of the present paper is to study the effect of the numerical discretization error and the manufacturing tolerances on fuel pin lattice integral parameters (multiplication factor and macroscopic cross-sections) through sensitivity calculations. The two-dimensional deterministic codes NEWT and HELIOS were selected for this work. The NEWT code was used for analysis of the TMI-1, PB-2, and Kozloduy-6 test cases; the TMI-1 test case was investigated using the HELIOS code. The work has been performed within the framework of UAM Exercise I-1 "Cell Physics.

Forest resource information system, phase 3

There are no author-identified significant results in this report