Weekend Effect

This paper provides some new empirical evidence on the weekend effect, one of the most recognized anomalies in financial markets. Two different methods are used: (i) a fractional integration technique for the estimation of the degree of integration of the series (d); (ii) a trading robot approach to examine whether or not there is such an anomaly giving rise to exploitable profit opportunities by replicating the actions of traders. The lowest orders of integration are generally found on Mondays, which can be seen as evidence for a weekend effect. However, the trading robot analysis shows that this anomaly cannot be exploited to make abnormal profits, and therefore it is not inconsistent with the Efficient Market Hypothesis (EMH).

Aims: To determine if a score (PCA score derived from Principal Component Analysis), a validated score of frailty and mortality, based on 12 blood biochemistry parameters can shed light on the issue of patient acuity, end of life and weekend mortality in hospitals. Study Design: The PCA score was calculated from over 280,000 blood tests. Average PCA score was calculated for different patient groups on different days of the week. An accompanying literature review of day-of-week variation in human mental and physical performance, and of studies investigating hospital mortality. Place and Duration of Study: Retrospective analysis of 280,000 blood test results from 80,000 patients attending the Milton Keynes University Hospital in the interval January 2012 to July 2015.  Participants: Patients at outpatient clinics, the emergency department or as an inpatient who had one or more blood samples comprising the 12 biochemical tests. Methodology: Average PCA score was calculated for patients in different hospital departments, on different days of the week, in different age groups, and at different times prior to death. Results: The PCA score for individual’s ranges from -6 to +6, with scores above zero generally associated with higher morbidity and mortality. The average PCA score is lowest in outpatient and A+E settings, varies across wards dedicated to different types of inpatient care, and is highest in ICU. The average PCA score reaches a minimum around age 18, and shows a modest increase with age in those who are not an inpatient. There is a day-of-week variance in the PCA score which is higher at the weekends, and dips to a minimum around Wednesday. The strength of the day-of-week effect varies by age and condition, and occurs in locations where staffing levels remain constant throughout the week.  Conclusions: Variation in human blood biochemistry follows day-of-week patterns and responds to different conditions, age, and the acuity of the condition. These add further weight to the argument that weekend staffing levels, and proposed 7 day working patterns, do not take account of all the factors that contribute to a day-of-week variation in hospital mortality and morbidity.

Changes of schedules larger than 3 h, such as jet lag and shift work, require an adjustment period of several days to resynchronize the sleep-wake cycle and several weeks to resynchronize other circadian rhythms to the new schedule. Initial studies on adaptation to small changes of schedule (1-2 h) found that the sleep-wake cycle adapts to the new schedule in less than 48 h, and such modifications are generally not studied because they may be confounded by a potential masking effect. This article summarizes the few published studies on Daylight Saving Time (DST) and sleep during weekends, two examples of small changes in schedule. There are individual differences in adaptation to daylight saving time, while some persons adjust immediately; other persons require more than 2 weeks. During weekends, people tend to go to bed and wake up later, and to extend their sleep. Delay and extension of sleep depend on factors such as shift of work during weekdays and chronotype (morningness-eveningness). Both DST and sleep during weekends offer the opportunity to study adaptation of the sleep-wake cycle in recurrent, social conditions. Studying these phenomena is also relevant to some socioeconomic issues, like the reportedincrease of traffic accidents and complaints from the population during daylight saving time; or the possible decrease in productivity and absenteeism during the ‘Blue Monday’.

Introduction The concept of “weekend effect”, that is, substandard healthcare during weekends, has never been fully demonstrated, and the different outcomes of emergency surgical patients admitted during weekends may be due to different conditions at admission and/or different therapeutic approaches. Aim of this international audit was to identify any change of pattern of emergency surgical admissions and treatments during weekends. Furthermore, we aimed at investigating the impact of the COVID-19 pandemic on the alleged “weekend effect”. Methods The database of the CovidICE-International Study was interrogated, and 6263 patients were selected for analysis. Non-trauma, 18+ yo patients admitted to 45 emergency surgery units in Europe in the months of March–April 2019 and March–April 2020 were included. Demographic and clinical data were anonymised by the referring centre and centrally collected and analysed with a statistical package. This study was endorsed by the Association of Ita...

The presence of seasonality in stock returns violates the weak form of market efficiency because equity prices are no longer random and can be predicted based on past pattern. This facilitates market participants to devise trading strategy which could fetch abnormal returns on the basis of past pattern. Fluctuations in the stock returns of firms listed at Nairobi Securities exchange motivated this study. In the Kenyan context, studies conducted on market anomalies in different markets have continued to yield different results in the majority of the investigated markets including the Nairobi Securities Exchange. This paper examines whether there is a significant variation in the average daily stock returns at the NSE and compares the findings to the previous empirical works on the topic. The paper tested for the presence of the Monday effect, differences in mean return across the five trading days, January effect, differences in the mean return across the five trading months and also provided the day-today and year-to-year behavior of stock return at the NSE. The study employed daily data from the year 2001 to 2015 to do analysis. The method of analysis was t tests and ANOVA. Policy recommendations are afterwards presented to aide the investors in making key investment decisions.