What is Seasonality
Seasonality is a characteristic of a time series in which the data experiences regular and predictable changes that recur every calendar year. Any predictable change or pattern in a time series that recurs or repeats over a one-year period can be said to be seasonal. Seasonal effects are different from cyclical effects, as seasonal cycles are observed within one calendar year, while cyclical effects, such as boosted sales due to low unemployment rates, can span time periods shorter or longer than one calendar year.
BREAKING DOWN Seasonality
Seasonality refers to periodic fluctuations in certain business areas that occur regularly based on a particular season. A season may refer to a time period as denoted by the calendar seasons, such as summer or winter, as well as commercial seasons, such as the holiday season. Companies that understand the seasonality of their business can time inventories, staffing, and other decisions to coincide with the expected seasonality of the associated activities.
It is important to consider the effects of seasonality when analyzing stocks from a fundamental point of view. A business that experiences higher sales in certain seasons appears to be making significant gains during peak seasons and significant losses during off-peak seasons. If this is not taken into consideration, an investor may choose to buy or sell securities based on the activity at hand without accounting for the seasonal change that subsequently occurs as part of the company’s seasonal business cycle.
Examples of Seasonality
Seasonality can be observed in a variety of predictable changes in costs or sales as it relates to the regular transition through the times of year. For example, if you live in a climate with cold winters and warm summers, your home's heating costs probably rise in the winter and fall in the summer. You reasonably expect the seasonality of your heating costs to recur every year. Similarly, a company that sells sunscreen and tanning products within the United States sees sales jump up in the summer but drop in the winter.
Large retailers, such as Wal-Mart, may hire temporary workers in response to the higher demands associated with the holiday season. In 2014, Wal-Mart anticipated hiring approximately 60,000 employees to help offset the increased activity expected in stores. This determination was made by examining traffic patterns from previous holiday seasons and using that information to extrapolate what may be expected in the coming season. Once the season is over, a number of the temporary employees will be released as they are no longer needed based on the post-season traffic expectations.
Adjusting Data for Seasonality
A lot of data is affected by the time of the year, and adjusting for the seasonality means that more accurate relative comparisons can be drawn between different time periods. Adjusting data for seasonality evens out periodic swings in statistics or movements in supply and demand related to changing seasons. By using a tool known as Seasonally Adjusted Annual Rate (SAAR), seasonal variations in the data can be removed. For example, homes tend to sell more quickly and at higher prices in the summer than in the winter. As a result, if a person compares summer real estate sales prices to median prices from the previous year, he may get a false impression that prices are rising. However, if he adjusts the initial data based on the season, he can see whether values are truly rising or just being momentarily increased by the warm weather.