rice seasonality is a phrase used to refer to the 12-month cycle apparent in P the prices of certain commodities. Due to supply and demand factors which recur at regular intervals each year, some commodity prices follow such a shortterm wave. For instance, harvest has a regular depressing effect on crop prices, as inventory levels suddenly increase. As another example, the winter months normally bring price increases in feed grains, as demand for them grows. Other regularly timed events cause price seasonality, too, such as crop reports released at the same times each year. To assess the impact of all the factors influencing a commodity's price seasonality would be a task approximating the development of an econometric model. A seasonal price pattern can, however, be considered with attention only to the pattern's shape and not its reasons for repetition. Even in this form, a knowledge of price seasonality can be a valuable trading tool.
As with any historical analysis, the value of defining price seasonality extends only as far as the chance that the pattern will be repeated. In some years, a completely contracyclical pattern will occur, baffling those who had expected otherwise at the time, and frustrating those who later look back at history in an effort to analyze. Other times, those factors which usually cause a commodity's seasonal price pattern will be overwhelmed by stronger, albeit temporary, market factors causing an opposite effect. Sometimes this abnormal impact lasts even beyond the year in which it occurred. For instance, soybean prices, normally thought of as highly seasonal, have often shown less of such a pattern in years with relatively high production levels. The pattern in the following years, then, has been distorted as prices regained their balance. Given a limited historical database, these abnormal years during which seasonal patterns run awry create great difficulty in estimating the seasonality's shape.
If the abnormal years can be identified and isolated, then the seasonal pattern can be specified much more clearly. By excluding those years from the seasonal analysis, the results can be made more informative and therefore more useful. In most cases, the odd years can be pinpointed easily and treated as outliers in the analysis. In some cases, the separation can go even as far as a hard distinction between types of years, leading to a division into two opposite sets. Studying the