What Is Mutual Fund Survivorship?
Mutual fund survivorship refers to the phenomenon in investment performance measurement where only funds that continue to exist throughout a given study period are included in performance analysis, while those that have been liquidated, merged, or otherwise ceased operations are excluded. This creates a form of selection bias within the broader field of financial research methodology. If funds cease to exist primarily due to poor investment performance, then any analysis that only includes surviving funds will present an artificially inflated view of the average returns of the overall mutual fund industry or a specific segment within it. Understanding mutual fund survivorship is crucial for investors, researchers, and policymakers seeking an accurate assessment of fund performance and the effectiveness of different investment strategy approaches.
History and Origin
The concept of survivorship bias in financial data gained significant attention in academic research, particularly in the study of actively managed portfolios like mutual funds. Early researchers often relied on readily available datasets that predominantly included only active funds, inadvertently skewing their findings. A seminal paper titled "Mutual Fund Survivorship" by Mark M. Carhart, Jennifer N. Carpenter, Anthony W. Lynch, and David K. Musto, affiliated with institutions including NYU Stern School of Business, extensively explored this issue. Their work demonstrated how conditioning on survival can lead to an overestimation of average performance and weaken evidence of performance persistence.6 The importance of considering all funds, including those that have failed, became a critical aspect of rigorous quantitative analysis in finance. Regulatory bodies, such as the U.S. Securities and Exchange Commission (SEC), also contribute to transparency by publishing aggregated data on the registered fund industry, which includes thousands of mutual funds, exchange-traded funds, and closed-end funds.5 This helps provide a broader view of the industry, moving towards more complete data sets.
Key Takeaways
- Inflated Performance: Mutual fund survivorship bias typically results in an overestimation of average historical returns because poorly performing funds that fail are excluded from analyses.
- Misleading Persistence: It can distort conclusions about fund manager skill and performance persistence, making it seem like top performers consistently repeat their success more often than they actually do.
- Data Integrity: Addressing survivorship bias requires careful data integrity and the use of comprehensive databases that track both surviving and defunct funds.
- Longer Horizons, Greater Bias: The longer the evaluation period, the more pronounced the survivorship bias can become, as more funds will have exited the market over extended timeframes.
- Investor Impact: Investors relying on biased performance figures may make suboptimal decisions about their portfolio construction and expectations for future returns.
Interpreting Mutual Fund Survivorship
Interpreting mutual fund survivorship is about recognizing and accounting for the "missing" data of failed funds when evaluating historical performance. When a researcher or investor looks at a list of currently existing mutual funds and analyzes their past returns, they are observing a survivor-only sample. This can lead to an overly optimistic assessment of how well the average mutual fund has performed. For instance, if an analysis shows a particular fund consistently outperformed its benchmark over a decade, it is important to consider how many other similar funds failed during that same period and why.
To interpret fund performance accurately, one must consider the attrition rate of funds. Funds often merge or liquidate due to sustained underperformance, failing to attract or retain sufficient asset under management, or changes in investment objectives. Consequently, an average risk-adjusted return calculated only from surviving funds will likely appear higher than the true average return of all funds that began the period. Researchers specifically try to measure the impact of this bias on reported performance figures like alpha, which represents excess returns relative to a benchmark.
Hypothetical Example
Consider a hypothetical mutual fund category consisting of 100 funds at the beginning of a 10-year period. Over this decade, 30 of these funds perform poorly, consistently underperforming their benchmarks, and eventually close or merge out of existence due to low investor interest and a decline in assets. The remaining 70 funds survive the entire 10-year period.
If an analyst calculates the average annual return calculation for this category by only including the 70 surviving funds, they would find a higher average return. For example, the surviving funds might have an average annual return of 8%. However, if the 30 failed funds, which likely had negative or very low returns, were included in the calculation from their inception to their liquidation date, the true average annual return for all 100 funds in the category might only be 6%. The 2% difference represents the survivorship bias. An investor who looked only at the 8% figure might mistakenly believe the category as a whole was more profitable than it truly was, potentially leading to unrealistic expectations for new investments in similar funds.
Practical Applications
Mutual fund survivorship is a critical consideration across various areas of finance:
- Academic Research: Researchers rigorously account for survivorship bias when studying topics such as mutual fund performance, manager skill, and the efficiency of equity markets. Ignoring this bias can lead to faulty conclusions regarding investment strategies or market anomalies. Publications in academic journals like The Review of Financial Studies often explicitly address how they handle survivorship issues in their datasets.4
- Investment Analysis: Financial analysts and consultants who evaluate mutual fund performance for clients must use survivorship-bias-free data to provide accurate assessments. Relying solely on data from currently operating funds can lead to an overly optimistic view of historical returns.
- Regulatory Oversight: Regulatory bodies, such as the SEC, monitor the mutual fund industry and aim to provide transparent data to the public. The SEC's "Registered Fund Statistics" reports provide aggregated information on the industry, including trends in fund types and asset flows, which indirectly helps shed light on the dynamics of fund entry and exit.3 The data provided helps market participants understand the full scope of fund performance, including the rates at which funds merge or liquidate. Morningstar, a prominent investment research firm, also tracks and reports on fund lineup turnover, which captures both fund launches and discontinuations, giving investors insight into the overall stability and change within fund families.2
- Portfolio Management: Professional portfolio managers and institutional investors understand that past performance, especially if not adjusted for survivorship bias, is not necessarily indicative of future results. They incorporate this understanding into their due diligence process when selecting funds.
Limitations and Criticisms
While essential for accurate analysis, addressing mutual fund survivorship also presents certain complexities and criticisms. One challenge lies in constructing truly "survivor-bias-free" datasets, as obtaining comprehensive data for all funds that ever existed, including those that failed years ago, can be difficult. Mergers, liquidations, and changes in reporting standards can complicate the process of tracking a fund's entire lifecycle.
Some criticisms revolve around the magnitude of the bias and its practical significance. While academic studies consistently show that survivorship bias exists and inflates reported returns, the exact impact can vary depending on the asset class, the time period studied, and the specific methodology used to calculate returns and account for fund exits. For instance, the impact on hedge funds, which often have less stringent reporting requirements and higher turnover, can be particularly significant.1
Another point of discussion is the interaction of survivorship bias with other data biases. For example, if funds with certain characteristics are more prone to failure, then analyses that sort funds by those characteristics might still be subtly biased even if failed funds are included in some way. Furthermore, while the average performance might be inflated by survivorship bias, some argue that investors only care about the performance of funds they can actually invest in now, making the performance of defunct funds less relevant for forward-looking decisions. However, this perspective overlooks the value of understanding the true historical landscape and the probabilities of success and failure within the industry, which is crucial for prudent diversification.
Mutual Fund Survivorship vs. Incubation Bias
Mutual fund survivorship and incubation bias are two distinct but related forms of data bias that can affect the accurate evaluation of investment performance.
Mutual Fund Survivorship refers to the overstatement of mutual fund returns that occurs when researchers or analysts only include data from funds that have survived throughout the entire study period. This bias arises because poorly performing funds are more likely to fail (liquidate or merge), and by excluding them from the analysis, the average returns appear higher than they would if all funds, including the failed ones, were considered from their inception.
Incubation Bias, on the other hand, occurs when a fund company launches new funds as "incubator funds" and only formally introduces to the public those that demonstrate strong initial performance. The initial, poor-performing funds might be quietly closed or merged before they are widely marketed, effectively removing their early subpar returns from publicly available datasets. This creates a misleading impression of strong performance for the funds that are eventually offered to the broader investor base, as their weak early periods are hidden.
The key difference lies in when the bias occurs and what data is excluded. Survivorship bias removes data from funds that failed after being publicly active for a period, leading to an overstatement of average returns for the entire universe of funds that existed. Incubation bias removes data from funds that never made it past an initial, internal testing phase (or were quietly wound down shortly after launch), thus presenting a misleading view of the success rate of new fund launches. Both biases contribute to an overly optimistic perception of investment returns within the mutual fund industry.
FAQs
Why is mutual fund survivorship important for investors?
It is important because it can lead to an inflated perception of historical returns for mutual funds. If you only look at funds that currently exist, you miss all the funds that failed due to poor performance, which skews the average returns upwards and can lead to unrealistic expectations for future investment outcomes.
Does survivorship bias only apply to mutual funds?
No, survivorship bias can apply to any dataset where observations (e.g., companies, hedge funds, stocks) that perform poorly are more likely to disappear from the dataset. It is a common challenge in financial data analysis, affecting studies of individual stocks, hedge funds, and other investment vehicles.
How do researchers account for mutual fund survivorship?
Researchers try to account for mutual fund survivorship by using comprehensive databases that include data on both active and defunct funds. This often involves tracking funds from their inception until their liquidation or merger, ensuring that the performance of all funds that existed during a period is included in the analysis.
Can mutual fund survivorship affect my retirement planning?
Yes, it can indirectly affect retirement planning if your financial projections are based on historical mutual fund returns that have not been adjusted for survivorship bias. Overly optimistic return expectations could lead to under-saving or an inaccurate assessment of how long your investments might last, impacting your overall financial planning.
Is there a way for individual investors to avoid survivorship bias?
While individual investors may not have access to the comprehensive, survivorship-bias-free databases used by academics, they can be aware of the issue. Seeking out analysis that explicitly addresses survivorship bias, understanding that past performance is not a guarantee of future returns, and diversifying investments across many asset classes and managers can help manage the risks associated with this and other biases. diversification is a key strategy to mitigate risks inherent in single fund selection.