ORCID Profile
0000-0001-9844-7617
Current Organisation
City University of Hong Kong
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Publisher: American Accounting Association
Date: 09-2018
DOI: 10.2308/ACCR-52280
Abstract: Using brokerage mergers and closures as two sources of exogenous shock to analyst coverage, this study explores the causal effect of analyst coverage on ex ante expected crash risk as captured by the options implied volatility smirk. We find a significant increase in a firm's ex ante expected crash risk subsequent to an exogenous drop in analyst coverage this positive effect is stronger for firms initially receiving less coverage. Further, we find analysts' ability matters to investors' assessment of future crash risk. Specifically, we find the impact is more pronounced for the coverage terminations of analysts with more firm-specific or general experience, with greater access to resources, or whose prior forecasts are more accurate than those of their peers. Overall, our results suggest that investors in the options market do recognize analysts as important information intermediaries and monitors and, thus, that analyst coverage influences the underlying stock's expected crash risk. JEL Classifications: G12 M41.
Publisher: Cold Spring Harbor Laboratory
Date: 04-05-2021
DOI: 10.1101/2021.05.03.21256436
Abstract: While the effects of prolonged sleep deprivation (≥24 hours) on seizure occurrence has been thoroughly explored, little is known about the effects of day-to-day variations in the duration and quality of sleep on seizure probability. A better understanding of the interaction between sleep and seizures may help to improve seizure management. To explore how sleep and epileptic seizures are associated, we analysed continuous intracranial EEG recordings collected from 10 patients with refractory focal epilepsy undergoing ordinary life activities. A total of 4340 days of sleep-wake data were analysed (average 434 days per patient). EEG data were sleep scored using a semi-automated machine learning approach into wake, stages one, two, and three non-rapid eye movement sleep, and rapid eye movement sleep categories. Seizure probability changes with day-to-day variations in sleep duration. Logistic regression models revealed that an increase in sleep duration, by 1·66 ± 0·52 hours, lowered the odds of seizure by 27% in the following 48 hours. Following a seizure, patients slept for longer durations and if a seizure occurred during sleep, then sleep quality was also reduced with increased time spent aroused from sleep and reduced REM sleep. Our results demonstrate that day-to-day deviations from regular sleep duration correlates with changes in seizure probability. Sleeping longer, by 1·66 ± 0·52 hours, may offer protective effects for patients with refractory focal epilepsy, reducing seizure risk. Furthermore, the occurrence of a seizure may disrupt sleep patterns by elongating sleep and, if the seizure occurs during sleep, reducing its quality. Australian National Health and Medical Research Council, US National Institutes of Health and Czech Technical University in Prague and Epilepsy Foundation of America Innovation Institute
Publisher: Elsevier BV
Date: 07-2021
Publisher: Wiley
Date: 30-12-2020
DOI: 10.1111/EPI.16785
Abstract: Most seizure forecasting algorithms have relied on features specific to electroencephalographic recordings. Environmental and physiological factors, such as weather and sleep, have long been suspected to affect brain activity and seizure occurrence but have not been fully explored as prior information for seizure forecasts in a patient‐specific analysis. The study aimed to quantify whether sleep, weather, and temporal factors (time of day, day of week, and lunar phase) can provide predictive prior probabilities that may be used to improve seizure forecasts. This study performed post hoc analysis on data from eight patients with a total of 12.2 years of continuous intracranial electroencephalographic recordings (average = 1.5 years, range = 1.0–2.1 years), originally collected in a prospective trial. Patients also had sleep scoring and location‐specific weather data. Histograms of future seizure likelihood were generated for each feature. The predictive utility of in idual features was measured using a Bayesian approach to combine different features into an overall forecast of seizure likelihood. Performance of different feature combinations was compared using the area under the receiver operating curve. Performance evaluation was pseudoprospective. For the eight patients studied, seizures could be predicted above chance accuracy using sleep (five patients), weather (two patients), and temporal features (six patients). Forecasts using combined features performed significantly better than chance in six patients. For four of these patients, combined forecasts outperformed any in idual feature. Environmental and physiological data, including sleep, weather, and temporal features, provide significant predictive information on upcoming seizures. Although forecasts did not perform as well as algorithms that use invasive intracranial electroencephalography, the results were significantly above chance. Complementary signal features derived from an in idual's historic seizure records may provide useful prior information to augment traditional seizure detection or forecasting algorithms. Importantly, many predictive features used in this study can be measured noninvasively.
Publisher: Wiley
Date: 15-04-2020
DOI: 10.1111/ACFI.12642
Publisher: Elsevier BV
Date: 04-2016
Location: United States of America
No related grants have been discovered for Vaclav Kremen.