Seasonal Adjustment

Seasonal adjustment is used to streamline data. It is also referred to as seasonality. Businesses use seasonality to filter out false positives from sales figures. In data analytics, it is used as a tool to comprehend demand-supply fluctuations.

Company sales vary between seasons; every business encounters a peak season and an off-season. Thus, to study business growth or performance, firms make adjustments in data—based on seasonal variations during a specific period. The seasonal adjustment formula eliminates the factors influencing the study and paints a clear picture. If the inaccuracies are not removed data can be misleading.

• Seasonal adjustment removes seasonal patterns from the data. This way, the revised data accounts for supply, demand, and sales fluctuations. Irrelevant data tamper with accuracy.
• The seasonally adjusted annual rate (SAAR) considers the previous year's data to analyze the current period.
• The consumer price index applies seasonality to study the fluctuation in prices of goods and services in a market basket.
• Businesses regulate supply chains, inventories, and prices based on peak season and off-season. If businesses fail to predict seasonal changes, they can incur huge losses.

Seasonal adjustment is a filter in business data analytics. It is also called seasonality. It removes irrelevant factors that result in false positives. Irrelevant data tamper with accuracy. Seasonality is used in various fields to filter data and to get realistic results.

Businesses track the supply, demand, and market sales to ascertain revenue, profit, and growth. But, in most cases, the sale is not even. Every business has a peak season and an off-season.

For example, a TV manufacturer may encounter an off-season for a few months and record fewer sales. This trend could even last a quarter. When the business analyzed its financial reports, it saw a steep decline in sales in the months when it encountered an off-season. This is where seasonality comes into play. To deliver valuable data, a seasonality filter is introduced.

But not just off-season; a TV manufacturer also encounters a peak season.TV manufacturers record higher sales figures during holidays, super bowls, important sports events, world cups, etc. These fluctuations tamper with annual sales data, and thus seasonality needs to be introduced. The same goes for gift shops and bakeries during Christmas and holidays. Likewise tourist attractions and hotels peak during the summer.

Based on peak season and off-season timing, businesses regulate supply chains, inventories, and prices based on peak season and off-season. If businesses fail to predict seasonal changes, they can incur huge losses.

The use of seasonal adjustment is not limited to businesses alone. Many governmental authorities track seasonal fluctuations; the consumer price index applies seasonal adjustments to study the fluctuation in prices of goods and services in a market basket. A market basket comprises a varied set of goods and services found in a typical household. Consumer price index in return affects inflation.

CPI assesses consumers’ purchasing power. The increase in prices reduces the purchasing power of the customers and vice-versa. Further authorities use CPI to predict seasonality in wages. Based on this they set a minimum wage level (for the upcoming period).

In the U.S., the Bureau of Labor Statistics (BLS) employs seasonality to derive the unemployment levels of every state. In addition, in the 2008 financial crisis, seasonality was used to study the recession's impact on global fuel prices.

The total seasonal adjustment is made through a tool called the seasonally adjusted annual rate (SAAR)—this is entirely based on the previous year's data. Authorities rely on seasonal adjustments for forecasting and decision-making. The formula considers four quarters or twelve months to determine the annual rate.

SAAR determines the seasonal component by eliminating time series. Its objective is to create a series with consecutive time intervals and to collate with other series. Analysts look for similarities between series.

Now, let us look at the seasonal adjustment factor (also known as the seasonality factor).

Let us assume that a company earned $720,000 in 2021. In September alone, the company recorded a sales figure of $90,000.

Now, let us determine the average sale for 2021:

• Average Sales = 720,000/12
• Average Sales = $60,000

In order to calculate the September seasonality factor adjustment, we divide September sales by average total sales:

• Seasonality Factor = 90000/60000
• Seasonality Factor = 1.5

Let us look at a seasonal adjustment example to understand its practical implications.

Robin owns a small business in his backyard—he manufactures umbrellas. Robin started this endeavor in January 2021. Robin manufacturers throughout the year—for all twelve months. At times, the demand was high, and Robin ran out of stock. On the other side of the spectrum, there were months when Robin did not make a single sale.

When Robin ponders over sales data, he observes that sales rose during the rainy season. Similarly, many also purchased summer umbrellas to protect themselves from the sun and heat. However, it was during winter that Robin struggled with sales.

Predicting seasonality can help Robin regulate inventory in a better manner for 2022. For example, he can create more umbrellas during lean periods so he does not run out of stock during peak season.

Also, based on the seasonality filter, a seasonal business might choose the fiscal year for accounting instead of a calendar year. 

Firms with high seasonality in sales have an uneven revenue distribution across consecutive accounting periods. Therefore, they might miss tax benefits if they follow a calendar year for accounting.

This article has been a guide to what is Seasonal Adjustment and its meaning. Here, we explain it with its formula, Factor, and example. You can learn more about it from the following articles -

• Bankruptcy
• Limitations of Financial Accounting
• Leading Indicators