Seasonal Index Adjustment

The seasonal adjustment index (SAI) is a commonly used technique to reduce the volatility of financial data and make comparisons between different series from different months or years. The adjustement is based on the assumption that a pattern exists in the data over time, which can be mathematicaly modelled to remove the fluctuations or seasonality from the figures.

By using the SAI, investors, analysts and companies can get a better understanding of the underlying trend of financial information and make informed decisions based on the steady and non-volatile figures.

The basic calculation of the SAI is relatively simple. It is simply the ratio of the current value (C) over the average of the value at the same period of other years (B1,...Bn). In other words, it is the ratio between C and the average of the past n observations.

For Example:

The average sales in the first quarter of the last 3 years was 100, 120 and 140.

Assuming that the sales for the first quarter of the current year is 125, then the SAI for this quarter = 125/120 = 1.04

Therefore the figure for the current period is 4% above the average of the past three years.

The above calculation demonstrated how the SAI of a series in a particular period can be calculated for comparison. This method can then be extended to the entire series. It simply allows comparing any value of the series with the average of the same period in all previous years.

The application of the SAI goes beyond just making comparison between different periods. It allows removing the periodic fluctuation from any regular time series, such as income statement or balance sheet items. This is done by multiplying each data point of the time series by its corresponding SAI figure. This should remove the seasonal variation and the resulting series should allow an easier and more accurate understanding of the underlying trend.

Using the same example as before, if the SAI for the first quarter is 1.04, then the sales figure for the first quarter of this year should be multiplied by this number to get a more accurate representation of the underlying trend.

In conclusion, the Seasonal Adjustment Index is a useful tool for investors and financial analysts because it allows a better understanding of the underlying trend of a series. It is also useful for companies to compare performance by removing seasonal variations. The calculation of the SAI is relatively simple and the application of it is even simpler. Therefore, it should definitely be part of any financial analysis.

Seasonal Adjustment Method

Seasonal adjustment is a statistical technique used to analyze economic and business data that are affected by seasonal patterns. Seasonal adjustment applies mathematical methods to account for seasonal fluctuations in the data, separating out any seasonal variation from the underlying base level. Seasonal adjustment allows for more meaningful comparisons of the data and more accurate predictions of future trends.

The most common type of seasonal adjustment is based on the X-11-ARIMA method, which involves the use of arithmetic, autoregressive and moving average models to determine the effects of seasonal patterns on the data. The X-11-ARIMA method, is based on the work of Box and Jenkins and consists of three steps:

1. Autoregressive Modeling: A statistical model is developed to estimate the underlying movements in the data. This model is used to separate out any seasonal movement from the underlying trend in the data.

2. Regression Analysis: The next step involves the use of regression analysis to analyze the residuals from the seasonal adjustment. This enables analysts to spot any irregularities or unexpected patterns.

3. Moving Average Modeling: Finally, a moving average model is used to adjust the seasonal patterns in the data. This smoothens out any remaining seasonal fluctuations and amplifies any underlying trends.

The primary advantage of the seasonal adjustment method is that it permits meaningful comparisons of data, allowing analysts to compare like-for-like variations in the data. For example, if data exhibits large fluctuations due to seasonal effects, seasonal adjustment can strip out the seasonality and provide a more accurate comparison.

In conclusion, seasonal adjustment is a powerful statistical technique used to analyze economic and business data. It enables analysts to strip out the effects of seasonality from the data and make meaningful comparisons of like data.