Mean Absolute Deviation (MAD) is a statistical measure of the average distance between each data point in a dataset and the mean of that dataset — expressed as an absolute value rather than a squared difference (as used in variance and standard deviation calculations). In financial analysis, MAD measures the average volatility or dispersion of returns around the mean return of an asset or portfolio. It is calculated as: MAD = Σ |Xᵢ – Mean| ÷ n, where Xᵢ represents each individual return and n is the number of observations. MAD is considered a more intuitive and robust measure of risk than standard deviation because it treats positive and negative deviations symmetrically without squaring them — making it less sensitive to extreme outliers or fat-tail events. In Indian mutual fund analysis, MAD alongside standard deviation provides a more complete picture of return consistency. Fund managers and portfolio analysts use MAD to assess how consistently a strategy delivers returns near its average — a lower MAD indicates more predictable, consistent performance.
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