What it means (plain English)
When you evaluate a PMS scheme, you’re trying to answer three questions:how much it returns,how much risk it takes, andhow predictable the journey is. Drawdown Clustering helps you quantify one part of that story.
Why it matters for PMS scheme selection
See the complete PMS evaluation framework
- Improves fairness in comparisons by adding context to headline returns.
- Helps identify trade-offs (return vs risk, upside capture vs downside protection, consistency vs bursts).
- Reduces the risk of “chasing” the last best period by encouraging multi-metric evaluation.
How to interpret it (practical checklist)
Try the relevant calculator/tool
Common pitfalls (how this gets misused)
Read our methodologyfor assumptions and limitations.
- Judging a scheme based on one metric alone.
- Comparing metrics calculated with different assumptions or data frequencies.
- Ignoring benchmark choice and peer-group context.
- Overweighting short histories or cherry-picked periods.
Related metrics to review together
Use Drawdown Clustering alongside these metrics to avoid one-number decision-making:
Related guides
- Max Drawdown Explained
- Drawdown Recovery Time
- PMS Sharpe Vs Sortino
- Rolling Returns Guide
- Volatility Explained
FAQs
Drawdown Clustering is a concept used to evaluate PMS scheme performance, risk, or portfolio behavior. It helps you compare schemes more fairly than headline returns alone.
Use Drawdown Clustering alongside related metrics (drawdowns, volatility, and benchmark-relative measures) and review it across multiple horizons to reduce cherry-picking.
Common mistakes include focusing on one metric in isolation, comparing across different strategies/benchmarks, and relying on short track records.
What is Drawdown Clustering in PMS evaluation?
Drawdown Clustering is a concept used to evaluate PMS scheme performance, risk, or portfolio behavior. It helps you compare schemes more fairly than headline returns alone.
How should I use Drawdown Clustering when comparing schemes?
Use Drawdown Clustering alongside related metrics (drawdowns, volatility, and benchmark-relative measures) and review it across multiple horizons to reduce cherry-picking.
What are the common mistakes investors make with Drawdown Clustering?
Common mistakes include focusing on one metric in isolation, comparing across different strategies/benchmarks, and relying on short track records.