Causality in Quantiles and Dynamic Stock Return-Volume Relations

This paper investigates the causal relations between stock return and volume based on quantile regressions. We first define Granger non-causality in all quantiles and propose testing non-causality by a sup-Wald test. Such a test is consistent against any deviation from non-causality in distribution, as opposed to the existing tests that check only noncausality in certain moment. This test is readily extended to test non-causality in different quantile ranges, and the testing results enable us to identify the quantile range for which causality is relevant. In the empirical studies of 3 major stock market indices, we find that, while the conventional test suggests no causality in mean, there are strong evidences that lagged volume Granger causes return in all but some middle quantiles. In particular, the causal effects have opposite signs at lower and upper quantiles and are stronger at more extreme quantiles. These relations form (symmetric) V shapes across quantiles. They also show that the dispersion of the return distribution increases with volume so that volume has a positive effect on return volatility. It is also shown that the quantile causal effects of lagged return on volume are mainly negative.Granger non-causality in quantiles, quantile causal effect, quantile regression, return-volume relation, sup-Wald test

Predictors of psychiatric readmissions in the short- and long-term: a population-based study in taiwan

OBJECTIVES: To explore the risks and rates of readmission and their predictors 14 days, one year, and five years after discharge for the psychiatric population in Taiwan. METHODS: This was a prospective study based on claims from 44,237 first-time hospitalized psychiatric patients discharged in 2000, who were followed for up to five years after discharge. The cumulative incidence and incidence density of readmission were calculated for various follow-up periods after discharge, and Cox proportional hazard models were generated to identify the significant predictors for psychiatric readmission. RESULTS: The less than 14-day, one-year, and five-year cumulative incidences were estimated at 6.1%, 22.3%, and 37.8%, respectively. The corresponding figures for incidence density were 4.58, 1.04, and 0.69 per 1,000 person-days, respectively. Certain factors were significantly associated with increased risk of readmission irrespective of the length of follow-up, including male gender, length of hospital stay >15 days, economic poverty, a leading discharge diagnosis of schizophrenia/affective disorders, and residence in less-urbanized regions. Compared to children/adolescents, young adults (20-39 years) were significantly associated with increased risks of <one-year and <five-year readmissions, but not <14-day readmission. Additionally, hospital characteristics were significantly associated with increased risk of <14-day and <one-year readmission, but not with risk of <five-year readmission. CONCLUSIONS: This study found that the significant predictors for psychiatric readmission 14 days to five years after discharge were essentially the same except for patient's age and hospital accreditation level. This study also highlighted the importance of socioeconomic factors in the prediction of readmission