Calendar Effects Research Articles

The impact of the Covid-19 pandemic and government intervention on active mobility.

With data from automated counting stations and controlling for weather and calendar effects, we estimate the isolated impacts of the “first wave” of Covid-19 pandemic and subsequent government intervention (contact restrictions and closures of public spaces) on walking and cycling in 10 German cities. Pedestrian traffic in pedestrian zones decreases with higher local incidence values, and with stricter government intervention. There are ambiguous effects for cycling, which decreases in cities with a higher modal share of cycling, and increases in others. Moreover, we find impact heterogeneity with respect to different weekdays and hours of the day, both for cycling and walking. Additionally, we use data on overall mobility changes, which were derived from mobile phone data, in order to estimate the modal share changes of cycling. In almost all cities, the modal share of cycling increases during the pandemic, with higher increases in non-bicycle cities and during stronger lockdown interventions.

Examining the adaptive market hypothesis with calendar effects: International evidence and the impact of COVID-19

This study examines whether the adaptive market hypothesis (AMH) explains calendar anomalies across 16 headline stock market indices in 10 markets. We employ the rolling window analysis and estimate a T-GARCH (1,1) for a long time series that includes two years coinciding with the COVID-19 pandemic. Overall, the empirical results reveal that calendar day anomalies across our sample markets exhibit time-varying behavior, evolving through patterns that shift markets between periods of efficiency and inefficiency, thereby providing support for the AMH framework. The results also highlight the calendar anomalies that reappeared after the onset of the COVID-19 pandemic across international markets.

Mutual fund flows and seasonalities in stock returns

We propose a flow-based explanation for two long-standing anomalies in empirical finance – Sell in May and the January effect. We find that mutual fund flows exhibit similar seasonal patterns as stock returns. After controlling for fund flows both calendar effects become insignificant. We provide new evidence on what drives this correlation. We show that return seasonality is due to unanticipated fund flow driven by uninformed (flow-motivated) retail investor trading. Active funds indicate flow-induced price pressure with a corresponding reversal of the effect, while passive funds suggest feedback trading instead. These seasonalities are remarkably pervasive, exhibiting little variation across different types of stocks, and are equally strong in periods of either high or low sentiment.

Multiple seasonal STL decomposition with discrete-interval moving seasonalities

The decomposition of a time series into components is an exceptionally useful tool for understanding the behaviour of the series. The decomposition makes it possible to distinguish the long-term and the short-term behaviour through the trend component and the seasonality component. Among the decomposition methods, the STL (Seasonal Trend decomposition based on Loess) method stands out for its versatility and robustness. This method, however, has one main drawback: it works with a single seasonality, and does not deal with the calendar effect. In this article we present a new decomposition method, based on the STL, which allows the use of different seasonalities while allowing the calendar effect and special events to be introduced into the model using discrete-interval moving seasonalities (MSTL-DIMS). To show the improvements obtained, the MSTL-DIMS technique is applied to short-term load forecasting in some electricity systems, and the results are discussed.

Herd behaviour in an emerging market: an evidence of calendar and size effects

PurposeThis study aims to examine investors’ herd behaviour for various calendar events and size-based stock portfolios in Pakistan. The authors consider three calendar effects, crisis (COVID-19 and financial crisis 2018–19), announcement of political news and popular calendar anomalies (month-of-the-year and day-of-the-week), and investigate the impact of stock size on calendar effect in terms of investors’ herd behaviour.Design/methodology/approachThe study uses non-linear specification to capture herd behaviour using firm-level daily data for 496 stocks listed on Pakistan Stock Exchange over the period 2001–2020.FindingsThe results indicate herd formation during periods of COVID-19, financial crisis, political news announcements and January (month-of-the-year). The authors also observe significant herding for the biggest and smallest size stocks over complete period. However, the authors find more pronounced herding in big stocks during January as compared to the more noticeable herding in small stocks over complete period. The findings suggest that herding in small stocks is not the main cause of January herding and hint on the prevalence of significant institutional herding during January.Practical implicationsThe stock prices destabilize because of the mimicking behaviour during crisis periods, days of political announcements and month of January. Implementation of insider trading laws and transparent information environment can help in reducing these effects and increasing market efficiency.Originality/valueThe authors consider the recent COVID period in our analysis. In addition, we provide new evidence on the possible impact of stock size on calendar effect in terms of herd behaviour, which, to the best of the authors’ knowledge, has not yet been documented in literature.

LIMREF: Local Interpretable Model Agnostic Rule-Based Explanations for Forecasting, with an Application to Electricity Smart Meter Data

Accurate electricity demand forecasts play a key role in sustainable power systems. To enable better decision-making especially for demand flexibility of the end-user, it is necessary to provide not only accurate but also understandable and actionable forecasts. To provide accurate forecasts Global Forecasting Models (GFM) that are trained across time series have shown superior results in many demand forecasting competitions and real-world applications recently, compared with univariate forecasting approaches. We aim to fill the gap between the accuracy and the interpretability in global forecasting approaches. In order to explain the global model forecasts, we propose Local Interpretable Model-agnostic Rule-based Explanations for Forecasting (LIMREF), which is a local explainer framework that produces k-optimal impact rules for a particular forecast, considering the global forecasting model as a black-box model, in a model-agnostic way. It provides different types of rules which explain the forecast of the global model and the counterfactual rules, which provide actionable insights for potential changes to obtain different outputs for given instances. We conduct experiments using a large-scale electricity demand dataset with exogenous features such as temperature and calendar effects. Here, we evaluate the quality of the explanations produced by the LIMREF framework in terms of both qualitative and quantitative aspects such as accuracy, fidelity and comprehensibility, and benchmark those against other local explainers.

The wisdom of crowds in forecasting at high-frequency for multiple time horizons: A case study of the Brazilian retail sales

This case study compares the forecasting accuracy obtained for four daily Brazilian retail sales indexes at four time prediction horizons. The performance of traditional time series forecasting models, artificial neural network architectures and machine learning algorithms were compared in other to evaluate the existence of a single best performing model. Afterwards, ensemble methods were added to model comparison to verify if accuracy improvement could be obtained. Evidence found in this case study suggests that a consistent forecasting strategy exists for the Brazilian retail indexes by applying both seasonality treatment for holidays and calendar effects and by using an ensemble method which main inputs are the predictions of all models with calendar variables. This strategy was consistent across all 16 index and time horizon combinations since ensemble methods either outperformed the best single models or there were no statistical difference from them in a Diebold-Mariano's test.

Deep learning based short‐term load forecasting incorporating calendar and weather information

Short‐term load forecasting has been an important approach for economical and sustainable power systems. Various methods have been proposed for obtaining an accurate forecasting result, among which deep learning models achieve state‐of‐the‐art performance. While external factors have been considered in the modeling of the load forecasting process, there is a lack of comparison between the effect of calendar and weather information. In this letter, a TCN‐based load forecasting model incorporating calendar and weather information is proposed and outperforms three deep learning and four machine learning baselines on an open real‐world load dataset, with and without leveraging the calendar or weather information. It is found that weather information is more helpful for improving the load forecasting performance than calendar information through numerical experiments.

Irregularities in forward-looking volatility

We investigate the behavior of 30-day forward-looking volatility derived from options on market indices, commodities and individual active stocks using a wide range of seasonal and calendar anomalies documented empirically in the finance literature. Our findings indicate the consistent and systematic absence of irregularities in the majority of the examined effects because, in general, the volatility indices remain unaffected by seasonal and calendar effects. However, we detect robust and significant weekday seasonality, within-the-month, turn-of-the-month, pre-holiday, and intra-quarter effects. For investors interested in buying or selling volatility products, our findings may be useful in timing their transactions.

An incremental loss ratio method using prior information on calendar year effects.

In a run-off triangle external factors can have a similar influence on all incremental losses of the same calendar year. This can distort the triangle such that reserving methods like chain ladder or the loss ratio method do not work properly. A very recent example of such an external factor is the Covid-19 pandemic. In many countries, the insurance industry is in the process of establishing market knowledge about the impact of the pandemic on premiums and losses. We extend the additive claims reserving model to allow for calendar year effects and develop a variant of the incremental loss ratio method (also known as the additive method) that can make use of such market knowledge. We derive formulas for the mean squared error of prediction and provide a detailed numerical example.Supplementary InformationThe online version contains supplementary material available at 10.1007/s13385-022-00315-3.

Calendar effects on surface air temperature and precipitation based on model-ensemble equilibrium and transient simulations from PMIP4 and PACMEDY

Abstract. Numerical modeling enables a comprehensive understanding not only of the Earth's system today, but also of the past. To date, a significant amount of time and effort has been devoted to paleoclimate modeling and analysis, which involves the latest and most advanced Paleoclimate Modelling Intercomparison Project phase 4 (PMIP4). The definition of seasonality, which is influenced by slow variations in the Earth's orbital parameters, plays a key role in determining the calculated seasonal cycle of the climate. In contrast to the classical calendar used today, where the lengths of the months and seasons are fixed, the angular calendar calculates the lengths of the months and seasons according to a fixed number of degrees along the Earth's orbit. When comparing simulation results for different time intervals, it is essential to account for the angular calendar to ensure that the data for comparison are from the same position along the Earth's orbit. Most models use the classical calendar, which can lead to strong distortions of the monthly and seasonal values, especially for the climate of the past. Here, by analyzing daily outputs from multiple PMIP4 model simulations, we examine calendar effects on surface air temperature and precipitation under mid-Holocene, Last Interglacial, and pre-industrial climate conditions. We came to the following conclusions. (a) The largest cooling bias occurs in boreal autumn when the classical calendar is applied for the mid-Holocene and Last Interglacial, due to the fact that the vernal equinox is fixed on 21 March. (b) The sign of the temperature anomalies between the Last Interglacial and pre-industrial in boreal autumn can be reversed after the switch from the classical to angular calendar, particularly over the Northern Hemisphere continents. (c) Precipitation over West Africa is overestimated in boreal summer and underestimated in boreal autumn when the classical seasonal cycle is applied. (d) Finally, month-length adjusted values for surface air temperature and precipitation are very similar to the day-length adjusted values, and therefore correcting the calendar based on the monthly model results can largely reduce the artificial bias. In addition, we examine the calendar effects in three transient simulations for 6–0 ka by AWI-ESM, MPI-ESM, and IPSL-CM. We find significant discrepancies between adjusted and unadjusted temperature values over continents for both hemispheres in boreal autumn, while for other seasons the deviations are relatively small. A drying bias can be found in the summer monsoon precipitation in Africa (in the classical calendar), whereby the magnitude of bias becomes smaller over time. Overall, our study underlines the importance of the application of calendar transformation in the analysis of climate simulations. Neglecting the calendar effects could lead to a profound artificial distortion of the calculated seasonal cycle of surface air temperature and precipitation.

Short-Term Electricity Prices Forecasting Using Functional Time Series Analysis

In recent years, efficient modeling and forecasting of electricity prices became highly important for all the market participants for developing bidding strategies and making investment decisions. However, as electricity prices exhibit specific features, such as periods of high volatility, seasonal patterns, calendar effects, nonlinearity, etc., their accurate forecasting is challenging. This study proposes a functional forecasting method for the accurate forecasting of electricity prices. A functional autoregressive model of order P is suggested for short-term price forecasting in the electricity markets. The applicability of the model is improved with the help of functional final prediction error (FFPE), through which the model dimensionality and lag structure were selected automatically. An application of the suggested algorithm was evaluated on the Italian electricity market (IPEX). The out-of-sample forecasted results indicate that the proposed method performs relatively better than the nonfunctional forecasting techniques such as autoregressive (AR) and naïve models.

Incidence rate patterns, cumulative incidence, and time trends for moderate and severe albuminuria in individuals diagnosed with type 1 diabetes aged 0–14 years: a population-based retrospective cohort study

The incidence and temporal trends of moderate and severe albuminuria during recent decades are poorly described in type 1 diabetes. We aimed to assess diabetes duration-specific incidence rates, cumulative incidence, and secular trends of albuminuria in type 1 diabetes in Finland. We conducted a population-based, retrospective cohort study of a stratified random sample (n=1500) of all individuals diagnosed with type 1 diabetes before age 15 years during 1970-99 in Finland. The sampling frame was the database of the Finnish Institute for Health and Welfare. Individuals with an atypical clinical course, presentation of non-diabetic kidney disease, insufficient albumin excretion rate measurements, or unavailable medical records were excluded (final sample n=1430). Study participants were followed up until death, the event of interest (moderate or severe albuminuria or kidney failure), or the most recent event-free date. Medical records retrieved up to Dec 31, 2020 were systematically reviewed for albuminuria determinations. Moderate and severe albuminuria were categorised on the basis of international reference limits (two of three consecutive urine samples). Kidney failure was defined as dialysis treatment or kidney transplant. Cohorts defined by calendar year of diabetes diagnosis (1970-79, 1980-89, and 1990-99) were assessed. Patterns of duration-specific incidences were evaluated by fitting generalised additive models to the data, which were split into multiple observations of half-year duration. Cumulative incidences were calculated with Kaplan-Meier analysis. In analyses with kidney failure as the endpoint, competing risk for mortality was incorporated. In our stratified random sample, 462 individuals were diagnosed with diabetes in 1970-79, 481 were diagnosed in 1980-89, and 487 were diagnosed in 1990-99. The incidence rate pattern of severe albuminuria changed over time; a peak at 15-19 years since diabetes onset in the 1970-79 cohort was not replicated in those diagnosed later. In the combined 1980-99 diagnosis-year cohorts, the incidence rate rose during the first 14 years after diabetes onset, after which it levelled off to a plateau. Between the 1970-79 and 1980-89 diabetes diagnosis cohorts, the cumulative incidence of severe albuminuria had approximately halved (hazard ratio [HR] 0·55 [95% CI 0·42-0·72] with the 1970-79 cohort as reference, p<0·0001), whereas, between the 1980-89 and 1990-99 cohorts, no further decrease was observed (HR 0·83 [0·54-1·26] with the 1980-89 cohort as reference, p=0·38). The 25-year cumulative incidence for severe albuminuria was 26·8% (22·6-30·8) in the 1970-79 diagnosis cohort, 12·0% (9·0-15·0) in the 1980-89 cohort, and 10·8% (6·7-14·6) in the 1990-99 cohort. 15 years after onset of severe albuminuria, cumulative progression rate from severe albuminuria to kidney failure was 35·2% (27·4-43·0) in the 1970-79 cohort and 35·6% (24·3-47·0) in the 1980-99 cohorts combined (Gray's test p=0·37). In the cohorts with data on moderate albuminuria (1980-89 and 1990-99), cumulative incidence of moderate albuminuria showed no calendar effect between the earlier and later cohorts (HR 0·99 [0·78-1·28] with the 1980-89 cohort as reference, p=0·97). The incidence rate of moderate albuminuria increased until 10 years after diabetes onset, then remained mostly stable until starting to decrease at around 25 years after diabetes onset. Our analyses show that the cumulative incidence of severe albuminuria has decreased between 1970-79 and 1980-99; however, whether this decrease solely denotes a delay in albuminuria, or also a true prevention of albuminuria, needs to be investigated further. Nevertheless, diabetic kidney disease remains a significant complication of type 1 diabetes. Due to the robust association of diabetic kidney disease with premature mortality, novel therapies to improve prognosis are needed. Folkhälsan Research Foundation, Medical Society of Finland, Wilhelm and Else Stockmann Foundation, Finnish Diabetes Research Foundation, Waldemar von Frenckell Foundation, Liv och Hälsa Society, Academy of Finland, and Novo Nordisk Foundation. For the Finnish and Swedish translations of the abstract see Supplementary Materials section.

Optimal Adaptive Neuro-Fuzzy Inference System Architecture for Time Series Forecasting with Calendar Effect

This paper discusses a procedure for model selection in ANFIS for time series forecasting with a calendar effect. Calendar effect is different from the usual trend and seasonal effects. Therefore, when it occurs, it will affect economic activity during that period and create new patterns that will result in inaccurate forecasts for decision making if not considered. The focus is on the model selection strategy to find the appropriate input variable and the number of membership functions (MFs) based on the Lagrange Multiplier (LM) test. The ARIMAX stochastic model is used at the preprocessing stage to capture calendar variations in the data. The calendar effect observed is the Eid al-Fitr holiday in Indonesia, a country with the largest Muslim population in the world. The data of Tanjung Priok port passengers used as a case study. The result shows that hybrid ARIMAX-ANFIS based on the LM test can be an effective procedure for model selection in ANFIS for time series with calendar effect forecasting. Empirical results show that the use of the calendar effect variable provides more accurate predictions as indicated by smaller RMSE and MAPE values than without the calendar effect variable.

Price Pattern Prediction in the Korean Beef Market

Responding to the market has been faster than in the past as the proportion of scaled-up farms has increased. Accordingly, it became more important for the Hanwoo industry to predict the Hanwoo price level and understand the price pattern from a short-term perspective. In this paper, a price pattern analysis model of Hanwoo was constructed based on the transaction information in the auction market. As a result of the analysis, it is shown that the daily price change rate ranges from 0.87% to 1.59%, and prices decrease &#8211; relatively on Monday. Also, the price of Hanwoo was high in January and September, indicating the calendar effect, which refers to a specific price effect that occurs at a specific time. The results of the study imply that the price of Hanwoo is strongly bound by daily and monthly cycles. From a farm s point of view, shipping dates for Hanwoo can be either moved forward or postponed, allowing variable selections within a short period, and decision-making on the slaughter of Hanwoo at a short-term level can change on a daily basis. The information derived from this study can be used as a basis for decision making.

Effect of drug use calendar on adherence to iron chelation therapy in young thalassemia patients.

Background:Regular blood transfusions in thalassemia patients can lead to severe complications and iron chelation therapy is required as a treatment. Thalassemia is common in Thailand and the drugs used in iron chelation therapy are deferoxamine and deferiprone. Adherence to the therapy is a key factor for treatment success.Objective:To assess the impact of a drug use calendar on deferiprone and deferoxamine adherence in young thalassemia patients.Methods:A total of 86 young thalassemia outpatients at a Thai tertiary care hospital were recruited into the study. Patients were stratified into two groups based on self-assessment of adherence using a visual analogue scale. One group (n=41) was given a calendar with the schedule of drug use in addition to counselling as standard pharmaceutical care. The second group (n=45) only received the counselling. Adherence to iron chelation therapy was assessed by deferiprone pill or deferoxamine vial counts on six visits (V1 to V6) and results were compared between visits and groups using a multilevel linear regression model. Change in serum ferritin levels after 6 visits (n = 81) were compared using a linear regression model.Results:Adherence significantly increased in both the calendar and non-calendar groups for deferiprone mono- and combination-therapy and for deferoxamine monotherapy. In the calendar groups, average adherence increased by between 2.05 and 5.66% per visit compared to increases of 0.31 to3.92% per visit in the non-calendar groups. A significant difference in the increase in adherence per visit between the calendar and non-calendar groups was only observed for deferiprone monotherapy (3.03% SEM = 0.49vs 1.42% SEM =0.49, respectively, P-value = 0.0078). The serum ferritin level decreased in the calendar group by 20.25ng/mL (SEM = 23.80) and increased in the non-calendar group by 59.63 ng/mL (SEM = 23.01, P-value = 0.0147).Conclusion:Provision of a drug use calendar improved adherence to deferoxamine and deferiprone and decreased serum ferritin levels in young Thai thalassemia patients over the improvements obtained from standard counselling.

Acute effects of ambient non-methane hydrocarbons on cardiorespiratory hospitalizations: A multicity time-series study in Taiwan

BackgroundFew environmental epidemiological studies and no large multicity studies have evaluated the acute short-term health effects of ambient non-methane hydrocarbons (NMHC), the essential precursors of ground-level ozone and secondary organic aerosol formation. ObjectiveWe conducted this multicity time-series study in Taiwan to evaluate the association between airborne NMHC exposure and cardiorespiratory hospital admissions. MethodsWe collected the daily mean concentrations of NMHC, fine particulate matter (PM2.5), ozone (O3), weather conditions, and daily hospital admission count for cardiorespiratory diseases between 2014 and 2017 from eight major cities of Taiwan. We applied an over-dispersed generalized additive Poisson model (GAM) with adjustment for temporal trends, seasonal variations, weather conditions, and calendar effects to compute the effect estimate for each city. Then we conducted a random-effects meta-analysis to pool the eight city-specific effect estimates to obtain the overall associations of NMHC exposure on lag0 day with hospital admissions for respiratory and circulatory diseases, respectively. ResultsOn average, a 0.1-ppm increase of lag0 NMHC demonstrated an overall 0.9% (95% CI: 0.4–1.3%) and 0.8% (95% CI: 0.4–1.2%) increment of hospital admissions for respiratory and circulatory diseases, respectively. Further analyses with adjustment for PM2.5 and O3 in the multi-pollutant model or sensitivity analyses with restricting the NMHC monitoring from the general stations only confirmed the robustness of the association between ambient NMHC exposure and cardiorespiratory hospitalizations. ConclusionOur findings provide robust evidence of higher cardiorespiratory hospitalizations in association with acute exposure to ambient NMHC in eight major cities of Taiwan.

Analysis of Antideformation and Antivibration Effect of Different Track Beds under Subway Train Vibration

Running metro trains would inevitably influence the vibration deformation in the lower part of the roadbed structure. In order to study the effects of vibration, Qingdao metro line 3 tunnel was taken as the research background. The track coupling model was established using Abaqus-Simpack to analyze the train velocity, train axle load, and trackbed vibration forms for the vertical deformation of roadbed calendar effect to provide the reference in practical engineering design and deformation control. According to the analysis, influenced by the formation of Qingdao Metro Line 3, the time-history curve of the displacement excitation of the trackbed presents the distribution law of “large in the middle and small at both ends.” According to the sensitivity analysis, the deformation of the trackbed is most obviously affected by the form of trackbed; the second was the influence of the axle load of the train. The speed change of the subway train presented relatively little influence on the displacement of the trackbed. Although the deformation control ability of the integral trackbed was better, its vibration reduction performance was poor. The vibration reduction performance of the rubber cushion floating trackbed was the best, followed by the steel spring floating trackbed.

Semesters or Quarters? The Effect of the Academic Calendar on Postsecondary Student Outcomes

There exists a long-standing debate in higher education on which academic calendar is optimal. Using panel data on the near universe of four-year nonprofit institutions and leveraging quasi-experimental variation in calendars across institutions and years, we show that switching from quarters to semesters negatively impacts on-time graduation rates. Event study analyses show that the negative effects persist beyond the transition. Using transcript data, we replicate this analysis at the student level and investigate possible mechanisms. Shifting to a semester: (i) lowers first-year grades, (ii) decreases the probability of enrolling in a full course load, and (iii) delays the timing of major choice. (JEL I23, I28)

Restrictive and stimulative impacts of COVID-19 policies on activity trends: A case study of Kyoto

This paper employs regression with ARIMA errors (RegARIMA) to quantify the impacts of multiple non-pharmaceutical interventions, daily new cases, seasonal and calendar effects, and other factors on activity trends across the timeline of the ongoing COVID-19 pandemic in Japan. The discussion focuses on two controversial policy sets imposed by the Japanese government that aim to contain the pandemic and to stimulate the recovery of the economy. The containing effect was achieved by stay-at-home requests and declaring a “State of Emergency” in the combat against the first waves of infectious cases. After observing reduced cases, Go-to-travel and Go-to-eat campaigns were launched in July 2020 to encourage recreational travel and to revive the economy. To better understand the impact of the policies we utilize “Google trends” which measure how much these policies are looked up online. We suggest this reflects how much they are part of the public discussion. A case study is conducted in Kyoto, a city famous for tourism. The proposed RegARIMA model is compared with linear regression and time series models. The outperformances in measuring the magnitude of intervention impacts and forecasting the future trends are confirmed by using a total of twelve activity and mobility indices as the dependent variable. Nine indices are released by Google and Apple and three are obtained from local Wi-Fi packet sensors. The effect of the State of Emergency declaration is found to erode at the second implementation, and the second stage of the Go-to-travel campaign successfully stimulated travel demand in the autumn sighting season of 2020.