Survivorship Bias

Published 2026-02-25. Last updated 2026-04-17. Editorial review: Know Your PMS editorial standards. By Abhimanyu Kucheria for Know Your PMS.

Topic cluster: Evaluation & Due Diligence

Start here if you are building a shortlist or reading factsheets for the first time. These guides cover comparison frameworks, disclosure literacy, and the traps that make good marketing look like good performance.

Pillar guide: How To Compare Schemes

More in this cluster:

• How To Compare Schemes
• How To Read Factsheet
• Common Pms Red Flags
• Benchmark Selection
• Peer Group Comparisons

What it means (plain English)

Survivorship bias omits failed or closed products from performance databases—making surviving PMS look better on average than all products that existed. Indian PMS industry has seen closures, mergers, and quiet strategy retirements.

Marketing shows live stars; databases may drop dead records unless you hunt. Backtests add historical bias—rules fit to data that 'survived' to launch.

Due diligence: ask how many strategies firm launched vs running, performance of closed composites, and whether rankings include full firm history.

Know Your PMS aims for broad coverage—still ask managers directly about graveyard strategies when sizing trust.

Worked example (Indian PMS scenario)

A platform shows 'average mid-cap PMS returned 26% in FY24' across 28 schemes—omitting 6 schemes soft-closed or merged after poor FY22–23. True universe average might be 21%. You pick a 'winner' from a curated list that excludes corpses.

Backtests suffer worse survivorship: stock universe excludes delisted frauds and merged penny stocks. Simulated 2015–2020 returns look +4% better than point-in-time investable set.

Due diligence fix: ask for performance of all share classes and soft-closed vintages; weight live client outcomes. On ₹1 cr, 5% optimism bias from survivorship equals ₹5 lakh overestimated expected gain in year one.

Why it matters for PMS scheme selection

Survivorship bias makes picking PMS from leaderboards alone like hiring only from winners' photos, not applicants.

See the complete PMS evaluation framework

• Corrects inflated peer median returns
• Surfaces manager strategy graveyard
• Skepticism for backtest-only launches
• Improves firm-level governance view
• Reduces lottery of visible winners only

How to interpret it (practical checklist)

1. Ask list of discontinued PMS strategies
2. Request performance of closed products if any
3. Note firm age vs strategy age
4. Weight live composite over backtest
5. Question database coverage scope
6. Read pitch for only-best fund selection
7. Prefer managers with long single-strategy live records

Explore related metrics · Compare PMS schemes · Cagr

Common pitfalls (how this gets misused)

Read our methodology for assumptions and limitations.

• Leaderboard investing without graveyard ask
• Assuming all databases include dead products
• Backtest as if live without survivorship adjust
• Ignoring firm rebrands wiping history
• Star manager spin-out losing old record
• Comparing new launch to survivor median only

Related metrics to review together

Use this guide alongside these metrics to avoid one-number decision-making:

Browse all metrics

• Cagr
• Max Drawdown
• Sharpe
• Volatility

Related guides

• PMS Sharpe Vs Sortino
• Max Drawdown Explained
• Rolling Returns Guide
• Volatility Explained
• Alpha Beta Explained

See also

• Backtest vs live performance
• Performance persistence myth
• Peer group comparisons
• Common PMS red flags

FAQs

How common is PMS closure in India?

Periodic—reasons include poor performance, merger, regulatory, or capacity. Firms don't advertise closures; ask directly.

Does SEBI require closed PMS performance archive?

Disclosure norms exist but investor access varies. Due diligence request is your tool.

Can survivorship bias affect Know Your PMS rankings?

Any platform reflects coverage universe—understand what's included and ask managers about products not listed.

Next: How to compare PMS schemes · Compare schemes · All guides