Modelling movie attendance with seasonality: evidence from China

High-frequency data improves the timeliness of movie attendance forecasts, but also results in the issue of seasonality. The main objective of this article is to build and test a novel movie attendance model that considers seasonality. Based on the Bass model, we combine an intertemporal demand shift pattern and the binary elements of seasonality – weekends and holidays – and propose a model called DISBM. We chose a sample of 58 movies released in China in 2013 to evaluate our proposal. The empirical results suggest that DISBM has better performance than other seasonal models. We demonstrate that the intertemporal demand shift results in weekend fluctuations, while the extra demand causes the seasonal holiday effect. The intent of this study is to better understand various movie attendance diffusions given different seasonal effects, in order to develop corresponding marketing strategies.     

The impact of share price on seasonality and size anomalies in Australian equity returns

Given the high correlation between a firm's stock price and market capitalisation, it is possible that the well-documented size anomaly is masking a share-price effect. Using a seemingly unrelated regression model to accommodate contemporaneous correlation between portfolios, we estimate the separate effects of firm size and share price on returns to Australian equity portfolios. The analysis is also extended to estimate seasonal components of size and price effects. Our major findings are: (i) firm size and share price have significant and independent effects on portfolio returns averaged over all months, (ii) the familiar negative relation between size and returns is confirmed across all months, and (iii) the relation between share price and returns is negative in July and positive in all other months (with the exception of January where no price effect occurs). These findings, which are consistent across sub-periods and robust to method variations, highlight the need for future research to provide an economic foundation for the relation between average returns, size and price.     

Probabilistic forecasting of industrial electricity load with regime switching behavior

This paper suggests a novel inhomogeneous Markov switching approach for the probabilistic forecasting of industrial companies’ electricity loads, for which the load switches at random times between production and standby regimes. The model that we propose describes the transitions between the regimes using a hidden Markov chain with time-varying transition probabilities that depend on calendar variables. We model the demand during the production regime using an autoregressive moving-average (ARMA) process with seasonal patterns, whereas we use a much simpler model for the standby regime in order to reduce the complexity. The maximum likelihood estimation of the parameters is implemented using a differential evolution algorithm. Using the continuous ranked probability score (CRPS) to evaluate the goodness-of-fit of our model for probabilistic forecasting, it is shown that this model often outperforms classical additive time series models, as well as homogeneous Markov switching models. We also propose a simple procedure for classifying load profiles into those with and without regime-switching behaviors.     

Seasonality in tourist flows: Decomposing and testing changes in seasonal concentration

This paper shows that changes in timing and magnitude of seasonality in tourist flows can be measured by decomposing the change in the Gini index, a widely used index of seasonal concentration, into two components. One of them tracks the change in the pattern of seasonal fluctuations and the second captures the change in their magnitude. To assess whether changes in the seasonal pattern and magnitude are significant, a technique for testing hypotheses concerning the two components is developed. We examine changes in the seasonal concentration of tourist arrivals in six tourist destinations in the Veneto Region, one of the most tourism-oriented regions in Europe, from 2006 to 2016. The magnitude of seasonality significantly decreased in some destinations with a diversified tourism product, such as Euganean Spas and Lake Garda. The seasonal pattern remained substantially stable for all destinations except Venice, where a non-negligible shift in the seasonal pattern occurred.     

On the seasonality in the implied volatility of electricity options

Seasonality is an important topic in electricity markets, as both supply and demand are dependent on the time of the year. Clearly, the level of prices shows a seasonal behaviour, but not only this. Also, the price fluctuations are typically seasonal. In this paper, we study empirically the implied volatility of options on electricity futures, investigate whether seasonality is present and we aim at quantifying its structure. Although typically futures prices can be well described through multi-factor models including exponentially decreasing components, we do not find evidence of exponential behaviour in our data set. Generally, a simple linear shape reflects the squared volatilities very well as a curve depending on the time to maturity. Moreover, we find that the level of volatility exhibits clear seasonal patterns that depend on the delivery month of the futures. Furthermore, in an out-of-sample analysis we compare the performance of several implementations of seasonality in the one-factor framework.     

Momentum is really short-term momentum

We demonstrate the estimation biases that arise when stock returns from 12 month prior and 2 month prior are included within intermediate and recent past momentum profits. These biases lead to an overestimation of intermediate past momentum but an underestimation of recent past momentum in the US market. There is no significant difference between the predictability of stock performance in the intermediate past and the recent past once we exclude these two months from the construction of momentum strategies in the US and each of the 26 major international markets.     

Joint In-Season and Out-of-Season Promotion Demand Forecasting in a Retail Environment

Inaccurate forecasts of demand during promotions diminish the already meager profit margins of retailers. No forecasting method described in the literature can accurately account for the combination of seasonal sales variations and promotion-induced sales peaks over forecasting horizons of several weeks or months. We address this research gap by developing a forecasting method for seasonal, frequently promoted products that generates accurate predictions, can handle a large number of sales series, and requires minimal training data. In our method's first stage, we forecast the seasonal sales cycle by fitting a harmonic regression model to a decomposed training set, which excludes promotional and holiday sales, and then extrapolate that model to a testing set. In the second stage, we integrate the resulting seasonal forecast into a multiplicative demand function that accounts for consumer stockpiling and captures promotional and holiday sales uplifts. The final model is then fitted using ridge regression. We use sales data from a grocery retailing chain to compare the forecasting accuracy of our method with popular seasonal and promotion demand forecasting models at multiple aggregation levels for both short and long forecasting horizons. The significantly more accurate forecasts generated by our model attest to the merit of the approach developed here.     

A SHARP model of bid–ask spread forecasts

This paper proposes an accurate, parsimonious and fast-to-estimate forecasting model for integer-valued time series with long memory and seasonality. The modelling is achieved through an autoregressive Poisson process with a predictable stochastic intensity that is determined by two factors: a seasonal intraday pattern and a heterogeneous autoregressive component. We call the model SHARP, which is an acronym for seasonal heterogeneous autoregressive Poisson. We also present a mixed-data sampling extension of the model, which adopts the historical information flow more efficiently and provides the best (among all the models considered) forecasting performances, empirically, for the bid–ask spreads of NYSE equity stocks. We conclude by showing how bid–ask spread forecasts based on the SHARP model can be exploited in order to reduce the total cost incurred by a trader who is willing to buy or sell a given amount of an equity stock.