Demand fluctuations,labour flexibility and productivity

There is a dearth of studies analysing the relationship between demand variations, productivity and flexible working in the face of variable demand challenges confronting the tourism industry. This investigation seeks to inform important firm and industry specific labour management strategies for improving productivity. Using data for 43 medium sized hotels owned by two chains in the UK, this paper analyses productivity in relation to external (demand variations) and internal (labour management) conditions over an 8 year period from 2005 to 2013. The paper’s findings show that demand variation is the principal determinant of productivity. Numerical, functional and zero-contract hour flexible labour management also contributes to labour productivity. Significant differences in findings between establishments and departments indicate the importance of disaggregated analyses.     

Testing for unit and fractional orders of integration in the trend and seasonal components of US monetary aggregates

Monthly seasonally unadjusted data can exhibit roots with possibly fractional orders of integration, corresponding to the monthly but also to the quarterly and to the long-run or trending components of the series. In this paper we use a procedure which is suitable to test simultaneously for the order of integration of each of these components and apply it to several US monetary aggregates.

Forecasting monthly riverflow time series

Mean monthly flows from thirty rivers in North and South America are used to test the short-term forecasting ability of seasonal ARIMA, deseasonalized ARMA, and periodic autoregressive models. The series were split into two sections and models were calibrated to the first portion of the data. The models were then used to generate one-step-ahead forecasts for the second portion of the data. The forecast performance is compared using various measures of accuracy. The results suggest that a periodic autoregressive model, identified by using the partial autocorrelation function, provided the most accurate forecasts     

The Optimism Cycle: Sell in May

Summary  On average, stocks deliver close to zero returns from May through October. We hypothesize that this seasonal pattern is caused by an optimism cycle. With year end approaching, investors start to look towards next year, often with overly optimistic expectations. Several months into the year, the initial optimism becomes hard to maintain and the stock market experiences a summer lull. A global sector-rotation strategy based on this theory appears to be highly profitable. Global earnings growth revisions follow a seasonal pattern parallel to that of the stock market. Investors’ optimism as measured by the initial returns on IPOs almost completely captures the results of the sector-rotation strategy in a separate analysis for the US stock market. All these findings support the optimism-cycle hypothesis. The author is grateful to Steef Bergakker for all the useful discussions about market dynamics through the years, which have contributed to the theory that an optimism cycle might exist. Special thanks go to Marno Verbeek for his very helpful suggestions. This paper also benefited from the remarks and suggestions from two anonymous referees.     

Seasonality and unit roots: the demand for fruits

We apply seasonal unit root tests to apple and pear price and quality data. We then develop a method for testing shifts in amplitude and/or phase of the seasonal cycles. The results have implications to speifications of models which use short-run data (quarterly, monthly).     

SARIMA damp trend grey forecasting model for airline industry

The aim of this paper is to propose a new model that improves the Damp Trend Grey Model (DTGM) with a dynamic seasonal damping factor to forecast routes passengers demand (pax) in the air transportation industry. The model is called the SARIMA Damp Trend Grey Forecasting Model (SDTGM). In the DTGM, the damp trend factor is a static smoothing factor because it does not change over time, and therefore, it cannot capture the dynamic behavior of time series data. For this reason, the modification consists in using the trend and seasonality effects of time series data to calculate a dynamic damp trend factor as time grows. The DTGM damping factor is based on the forecasted data obtained by the GM(1,1) model; otherwise, the SDTGM calculates a seasonal damping factor based on historical data using a large amount of data points for short lead-times. The SDTGM has less uncertainty than the DTGM. The simulation results show that the SDTGM captures the seasonality effect and does not allow the forecast to exponentially grow. The SDTGM forecasts more reasonable routes pax for short lead-times when having a large amount of data points than the DTGM. The United States domestic air transport market data are used to compare the performance of the DTGM against the proposed SDTGM.     

Weekend gold returns in bull and bear markets

This study examines the weekend effect in gold returns during bull and bear markets over the period 1975 through 2011. It shows that gold returns from close on Friday to close on Monday are significantly lower than returns during the rest of the week. This result is due largely to gold returns during bear markets. During gold bull markets, gold weekend returns are not significantly different from weekday returns. The study shows that the effect has substantial economic implications for gold investors. The effect is shown to be related to a significantly negative skewness in the weekend returns.     

Forecasting using a periodic transfer function: with an application to the UK price of ferrous scrap

The familiar concept of cointegration enables us to determine whether or not there is a long-run relationship between two integrated time series. However, this may not capture short-run effects such as seasonality. Two series which display different seasonal effects can still be cointegrated. Seasonality may arise independently of the long-run relationship between two time series or, indeed, the long-run relationship may itself be seasonal. The market for recycled ferrous scrap displays these features: the US and UK scrap prices are cointegrated, yet the local markets exhibit different forms of seasonality. The paper addresses the problem of using both cointegrating and seasonal relationships in forecasting time series through the use of periodic transfer function models. We consider the problems of testing for cointegration between series with differing seasonal patterns and develop a periodic transfer function model for the US and UK scrap markets. Forecast comparisons with other time series models suggest that forecasting efficiency may be improved by allowing for periodicity but that such improvement is by no means guaranteed. The correct specification of the periodic component of the model is critical for forecast accuracy.     

Seasonality and stock returns: some evidence from Japan

This paper examines the seasonal properties of Japanese stock prices using time series data from 1971 through 1997. Of interest are the influences of particular months of the year, which this study measures for the Tokyo stock price index (TOPIX), and indices that represent companies with large, medium, and small numbers of listed shares. The monthly effects in the various stock indices are confirmed for the total sample period. In contrast, such effects are not found for the latter half of the sample, and seasonal unit roots are rejected for all indices. That is, the seasonality of Japanese stock price indices is found to be deterministic but not stochastic.