Forecasting the Existence of Chocolate with Variation and Seasonal Calendar Effects Using the Classic Time Series Approach

Chocolate is the raw material for making cakes, so consumption of chocolate also increases on Eid al-Fitr. However, this is different in the United States where the tradition of sharing chocolate cake is carried out on Christmas. To monitor the existence of this chocolate can be through the movement of data on Google Trends. This study aims to predict the existence of chocolate from the Google trend where the use of chocolate by the community fluctuates according to the calendar variance and seasonal rhythm. The method used is classic time series, namely nave, double exponential smoothing, multiplicative decomposition, addictive decomposition, holt winter multiplicative, holt winter addictive, time series regression, hybrid time series, ARIMA, and ARIMAX. Based on MAPE in sample, the best time series model to model the existence of chocolate in Indonesia is ARIMAX (1,0,0) while for the United States it is Hybrid Time Series Regression-ARIMA(2,1,[10]). For forecasting the existence of chocolate in Indonesia, the best models in forecasting are ARIMA (([11],[12]),1,1) and Naïve Seasonal. In contrast to the best forecasting model for the existence of chocolate in the United States, namely Hybrid Naïve Seasonal-SARIMA (2,1,0)(0,0,1)12 Hybrid Time Series Regression- ARIMA(2,1,[10]), Time Series Regression, Winter Multiplicative, ARIMAX([3],0,0).

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

Numerical modelling 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 is from the same position along the Earth's orbit. Most models use the classical "fixed-length" 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 conclude that: (a) The largest cooling bias occurs in autumn when the classical calendar is applied for the mid-Holocene and last interglacial. (b) The sign of the temperature anomalies between the Last Interglacial and pre-industrial in boreal autumn can be reversed after the switch from 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 "fixed-length" seasonal cycle is applied. (d) Finally, correcting the calendar based on the monthly model results can reduce the biases to a large extent, but not completely eliminate them. In addition, we examine the calendar effects in 3 transient simulations for 6–0 ka by AWI-ESM, MPI-ESM, and IPSL. We find significant discrepancies between adjusted and unadjusted temperature values over ice-free 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 "fixed-length" 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. One important fact to be noted here is that the discrepancy in seasonality under different calendars is an analysis bias and is highly depends on the choice of the reference position/date (usually the vernal equinox, which is set to 31th March) on the Earth's ellipse around the sun. Different model groups may apply different reference dates, so ensuring a consistent reference date and seasonal definition is key when we compare results across multiple models.

The calendar anomalies on Warsaw Stock Exchange in 2015-2020

The article is devoted to the subject of popular calendar anomalies. According to the theory of finance, if investors act rationally, the market can be considered efficient. In such a situation, achieving an above-average rate of return is impossible, as securities reflect all available information about them. However, on the basis of many studies and assumptions of behavioral economics, numerous exceptions to this rule have been discovered, which have been called market anomalies or stock anomalies. Such a deviation is the "January effect" and "January barometer" described in this work. The aim of the article is to investigate whether there is a deviation on the Warsaw Stock Exchange in 2015-2020 called the "January effect" and also whether the return rate in January can be a good prognosis for the rest of the year. In the results of the analysis, the occurrence of the title calendar effects in the studied sample was not unequivocally stated.

Sensitivity of the Art Market to Price Volatility

The purpose of the article: The art market becomes very popular among investors, when there is strong turbulence on the stock market. In times of calm, the art market is used by investors to diversify risk and build more efficient investment portfolios according to the Markovitz’s theory. The aim of this paper is to: (i) present the peculiarity of investment on the art market, represented by art market indexes in comparison to traditional investments in other financial market segments (money market, equity indexes and commodity market), (ii) to verify the hypothesis of normality of the distribution of rates of return of the analyzed art market indices as well as (iii) to analyze calendar effects occurrence on the art market. Methodology: Comparison of rates of return on the stock, bond, commodity and money markets with rates on the art market in four different time intervals. For each of the analyzed periods, an income-risk map was presented, taking into account the spectrum of financial instruments, including six art indexes: Old Masters, 19th Century, Modern art, Post War art, Contemporary art and Global art. The hypothesis of normality of the distribution of rates of return of the art market indices for four analyzed periods was verified with the use of Jarque-Bera test. Results of the research: Comparison of rates of return on the stock market and art market leads to the conclusion that their relationship depends on the period chosen. For two of the analyzed periods, the rates of return on the stock market were higher than on the art market, but for others periods, the opposite. The distribution of quarterly rates of return resulted to be a normal distribution for almost all of analyzed indices and time periods. Calendar effects were observed in the case of four analyzed indexes.

STOCK MARKET ANOMALIES: A CASE OF CALENDAR EFFECTS ON THE MALAYSIAN STOCK MARKET

The existence of market anomalies for the return reveals the inefficiency in the market that could affect investor investment strategy, portfolio selection, and profit management. It is due to the unpredictable movement of the stock market return that will affect the decision of investors later. As such, this study intends to investigate day of the week effect, a month of the year effect, and a quarter of the year effect on the Malaysian Stock Exchange, namely the Kuala Lumpur Composite Index (KLCI) on data from 2nd January of 2015 until 31st December 2018. Based on the findings from Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model analysis, it is found that the daily effect on returns was insignificant. Possible reasons for the insignificant return could be due to the lack of time-series data. However, the significant monthly effect on returns of May, November, and December while the quarterly effect on the returns is found significant in the first quarter. This study also concludes that volatility shock is persistent in the returns for all those three market anomalies.

Adaptive calendar effects and volume of extra returns in the cryptocurrency market

PurposeThis study broadly attempts to explore adaptive or dynamics patterns of calendar effects existed in the cryptocurrency market as per the adaptive market hypothesis (AMH) framework. Another agendum of this study is to investigate the quantum of extra returns which may result from the presence of calendar effects.Design/methodology/approachThe present study considers both parametric and non-parametric approaches to verify calendar effects empirically. Specifically, this study has implemented Generalised Autoregressive Conditional Heteroscedasticity (1, 1) and Kruskal–Wallis tests in the rolling window approach to reveal adaptive patterns of calendar effects. Additionally, the present study has used the implied trading strategy to evaluate the volume of excess returns resulted from calendar effects than buy-and-hold (BH) strategy.FindingsThe overall results of the current study exhibit that calendar effect in the cryptocurrency market is dynamic rather than static which indicates the calendar effect is a time-varying phenomenon. Moreover, this study also confirmed that ITS is not suitable to obtain extra returns despite the existence of calendar effects.Research limitations/implicationsThe present study has covered some broad aspects of calendar anomalies in the cryptocurrency market, keeping aside certain other limitations which need to be addressed in the following dimensions. Future studies may aim at addressing issues like, Turn-of-the-Year effect, Halloween effect, weather effect, and Month-of-the-Year effects, and try to explore the reasons of presence of dynamic patterns of calendar effects.Practical implicationsThe significant implication of this study is that it alerts investors about market return predictability due to calendar patterns or effects in different periods. It also suggests the period in which the ITS can perform better than the BH strategy.Originality/valueIt is the first study in the cryptocurrency literature which has adopted the AMH framework to verify adaptive calendar effects or anomalies. Furthermore, this study, instead of a mere examination of the presence of calendar effects, has evaluated the potential of calendar effects to produce extra returns through trading strategies.

The Calendar Effects of the Idiosyncratic Volatility Puzzle: A Tale of Two Days?

The idiosyncratic volatility (IVOL) anomaly exhibits strong calendar effects. The negative relation between IVOL and the next-month return obtains mainly in the third week of the month. The IVOL-return relation is generally negative on Mondays and positive on Fridays. However, the positive impact is absent on the third Friday because of selling pressure from stocks delivered at option expiration. This imbalance between the negative and positive returns during the third week of the month has a large impact on the IVOL-return relation. Removing the third Friday and subsequent Monday return reduces the monthly IVOL effect by at least 40%. This paper was accepted by Karl Diether, finance.

A time series approach to player churn and conversion in videogames

Players of a free-to-play game are divided into three main groups: non-paying active users, paying active users and inactive users. A State Space time series approach is then used to model the daily conversion rates between the different groups, i.e., the probability of transitioning from one group to another. This allows, not only for predictions on how these rates are to evolve, but also for a deeper understanding of the impact that in-game planning and calendar effects have. It is also used in this work for the detection of marketing and promotion campaigns about which no information is available. In particular, two different State Space formulations are considered and compared: an Autoregressive Integrated Moving Average process and an Unobserved Components approach, in both cases with a linear regression to explanatory variables. Both yield very close estimations for covariate parameters, producing forecasts with similar performances for most transition rates. While the Unobserved Components approach is more robust and needs less human intervention in regards to model definition, it produces significantly worse forecasts for non-paying user abandonment probability. More critically, it also fails to detect a plausible marketing and promotion campaign scenario.