GOLDMAN SACHS Downside Variance
| GLCGX Fund | | | USD 33.39 0.09 0.27% |
Downside Variance (or DV) is measured by target semi-variance and is termed downside volatility. It is expressed in percentages and therefore allows for rankings in the same way as variance. One way to view downside volatility is the annualized variance of returns below the target. Below is GOLDMAN SACHS's current Downside Variance with peer comparisons and related risk metrics.
Current Downside Variance Value
The current Downside Variance of 1.43 places GOLDMAN SACHS at moderate price variability. This places GOLDMAN SACHS within the typical volatility range for Mutual Fund Funds.
Downside Variance | = | SUM(RET DEV)2N(ER) |
| = | 1.43 | |
| SUM | = | Summation notation |
| RET DEV | = | Actual returns deviation over selected period |
| N(ER) | = | Number of points with returns less than expected return for the period |
Downside Variance Peers Comparison
Among sector peers, GOLDMAN SACHS's Downside Variance of 1.43 is above the 1.42 group average. The range runs from 0.4679 (Goldman Sachs Mlp) to 2.72 (Allianzgi Technology Fund). GOLDMAN SACHS has exhibited greater price dispersion than the peer average over the measured period.
Downside Variance Relative To Other Indicators
The chart below plots Downside Variance against Maximum Drawdown for Goldman Sachs and its peers. Each point represents one equity — position along the horizontal axis shows Downside Variance while the vertical axis shows Maximum Drawdown. Equities that cluster in different quadrants carry distinct risk-return profiles. Use the dropdowns to swap in other indicators for either axis.
GOLDMAN SACHS's Maximum Drawdown of
5.11 runs about
3.57 times its Downside Variance of
1.43 . This indicates Maximum Drawdown is significantly higher than Downside Variance for GOLDMAN SACHS.
Methodology, Assumptions & Data Sources
The current Downside Variance for GOLDMAN SACHS is 1.43. GOLDMAN SACHS's Downside Variance is computed from historical closing prices over the selected time horizon, applying the indicator's defined mathematical transformation to raw price data. The underlying data comes from exchange-reported daily closes with corporate action adjustments applied where relevant. Indicator accuracy depends on data continuity across the calculation period. Gaps in trading history may affect the output.
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