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About

Standard deviation alone implies magnitude but lacks context. The Coefficient of Variation (CV) normalizes variability relative to the mean, allowing for the direct comparison of datasets with vastly different units or scales. For investors, a lower CV indicates a better risk-adjusted return (less volatility per unit of return). For quality control engineers, it quantifies process stability regardless of the product size.

This tool applies Interquartile Range (IQR) logic to filter outliers before calculation, ensuring that extreme anomalies do not skew the volatility assessment. This is critical when analyzing financial tickers subject to flash crashes or sensor data with transient errors.

statistics volatility risk analysis variance finance

Formulas

The Coefficient of Variation is the ratio of the standard deviation to the mean:

CV = σμ

Where standard deviation σ for a sample is:

σ = ni=1(xi x)2n 1

Reference Data

CV RangeInterpretation (Finance)Interpretation (Manufacturing)Risk Level
< 0.05High CertaintySix Sigma potentialNegligible
0.05 - 0.15Stable Blue ChipStandard ToleranceLow
0.15 - 0.30Growth StockProcess DriftModerate
0.30 - 0.50SpeculativeRetooling RequiredHigh
> 1.00Distressed AssetSystem FailureExtreme

Frequently Asked Questions

Use CV when comparing the spread of two datasets with different means or different units (e.g., comparing the volatility of a stock priced at $10 vs. one priced at $2000). Standard deviation is absolute; CV is relative.
We calculate the 25th (Q1) and 75th (Q3) percentiles. The Interquartile Range (IQR) is Q3 minus Q1. Any data point falling below Q1 - 1.5*IQR or above Q3 + 1.5*IQR is flagged as an outlier and optionally excluded from the calculation to prevent skew.
Mathematically yes, if the mean is negative. However, CV is typically used for data with a rational zero (ratio scale) where values are positive (like height, weight, or price). A negative CV is often difficult to interpret meaningfully in finance.