Survivorship Bias

Survivorship bias is the tendency to evaluate performance or outcomes by focusing only on the entities that have survived or succeeded, while ignoring those that have failed or disappeared. In investing, this leads to an overestimation of historical returns and an overly optimistic view of strategies, funds, or indices.

Key takeaways

• Survivorship bias arises when losers (closed or delisted funds and stocks) are excluded from historical performance datasets.
• Excluding failures skews averages upward and can mislead investors about true risk and return.
• Funds typically close for low demand or poor performance; how closures are handled (liquidation vs. merger) affects reporting.
• Reverse survivorship bias can occur when winners leave a benchmark (e.g., a small-cap index) while losers remain, producing the opposite distortion.
• Investors should seek survivorship-bias-free data and consider qualitative history of a strategy or manager.

How survivorship bias appears in finance

• Mutual funds: When underperforming funds are liquidated or merged and then excluded from performance tables, the remaining sample looks better than the true historical universe.
• Market indices: Stocks that are dropped (for poor performance, size, or other reasons) are often omitted from retrospective index returns, inflating apparent past performance.
• Backtests: Strategies tested only on surviving securities or funds overstate expected performance if delisted or failed items are omitted.

Why it matters

Relying on datasets that exclude failures can:
* Overstate historical returns and understate volatility or drawdowns.
Lead investors to choose funds or strategies based on incomplete evidence.
Produce mistaken expectations about future performance and risk.

Fund closings and their impact

Funds generally close for two reasons:
1. Low demand and insufficient assets under management.
2. Poor performance prompting the manager to shutter or merge the fund.

When a fund closes:
* Liquidation: Investors’ holdings are sold; this can trigger tax consequences and the closed fund’s poor performance typically disappears from subsequent performance reports.
* Merger: Shares may be moved into another fund with fewer immediate tax implications, but the closed fund’s poor results often vanish from aggregated performance statistics.

Because closed funds are frequently excluded from later reporting, historical averages can become biased upward unless datasets explicitly include defunct funds.

Closing to new investors

A fund may close only to new investors but remain open for existing holders. This is not the same as a full closure and often reflects high demand or capacity constraints rather than failure.

Reverse survivorship bias

Reverse survivorship bias happens when underperformers remain in a sample while top performers exit. For example, a small-cap index may retain companies that stay small (or decline), while companies that grow significantly leave the index—so the index’s historical performance may understate the success potential of companies that were once included.

How to reduce survivorship bias when evaluating investments

• Use datasets that include delisted or closed funds and stocks (survivorship-bias-free data).
• Review the full historical universe for a strategy, including failures and mergers.
• Examine flows, closures, and the reasons for fund terminations.
• Look at manager and strategy histories (not just current funds) to see past attempts and failures.
• Prefer performance measures and backtests that account for delistings, corporate actions, and liquidation events.
• Combine quantitative analysis with qualitative research on strategy viability and capacity constraints.

Conclusion

Survivorship bias can substantially distort investment performance analysis if failures are omitted. Awareness of how datasets are constructed and actively seeking survivorship-bias-free information are essential steps to form realistic expectations and make better-informed investment decisions.