Key Points:
The algorithm calculates the 12-month average for each month's gross amount.
It computes the variance amount as the difference between the current month's gross amount and the higher of the 12-month average or the previous month's amount.
The average variance is calculated as a percentage of the higher value.
Calculating the variance amount as the difference between the current month's gross amount and the higher of the 12-month average or the previous month's amount is designed to provide a more meaningful and stable comparison. Here are the reasons behind this approach:
1. Stability in Comparison:
12-Month Average: Using the 12-month average helps to smooth out any short-term fluctuations and provides a more stable baseline for comparison. This is especially useful in financial analysis, where seasonal trends and irregularities can cause significant short-term variations.
Previous Month's Amount: Comparing the current amount with the previous month's ensures that any recent trends or changes are captured. This is important for identifying immediate impacts or shifts in the data.
2. Avoiding Skewed Variance:
By taking the higher of the 12-month average and the previous month's amount, the calculation avoids scenarios where a temporary dip or spike in the previous month can disproportionately affect the variance. This method ensures that the baseline for comparison is always reasonable and not unduly influenced by short-term anomalies.
3. Relevance to Current Context:
Financial data often exhibits trends or cycles. By using the higher of the two metrics, the code ensures that the variance reflects both long-term trends (through the 12-month average) and short-term changes (through the previous month's amount). This dual approach provides a more comprehensive view of the variance, making it more relevant to current financial conditions.
4. Risk Mitigation:
For financial analysis and decision-making, it is crucial to mitigate risks associated with sudden and unpredicted changes. Using the higher value helps in buffering against extreme variations, thereby providing a conservative and risk-averse measure of variance.
Example Scenario:
Consider a business that experiences seasonal sales. If sales were unusually high or low in a particular month, comparing the current month directly to this outlier can give a misleading variance. The variance calculation remains balanced and informative by using the higher of the 12-month average (which includes the seasonal trend) and the previous month (which shows recent performance).
Conclusion:
By computing the variance as the difference between the current month's gross amount and the higher of the 12-month average or the previous month's amount, the code aims to balance capturing long-term trends and short-term changes, providing a more stable, relevant, and risk-averse measure of financial performance.
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