Survivorship Bias and Mutual Fund Performance

What is Survivorship Bias?

Survivorship Bias, which is also known as survivor bias, is a tendency of viewing how existing stocks or funds are performing in a market based on historical data without considering the stocks that no longer exist. Survivorship bias occurs when reports on mutual fund performance portray the use of data about mutual funds that currently exist; however, they actually do not include data about certain funds (such as merged or defunct funds or failed funds).

Due to Survivorship bias, an investor may overestimate the performance of the stock or index due to the inflated historical data or other attributes of a fund or index. Such published data misguides the investor and increases the chances of them making the wrong investment decision, thus increasing the Survivorship Bias Risk.

Understanding Survivorship Bias

To understand survivorship bias, let us assume that a trader’s portfolio consists of mutual funds, bonds, and stocks in the year 2019. Next year due to the pandemic’s effect, the price of the stock drastically fell. Instead of including this observation, in 2020, the stocks were directly removed from the portfolio.

This information is then published, showing that the portfolio contains only mutual funds and bonds.

Suppose the performance of this portfolio for the year 2020 is calculated without considering the poor performance of the stocks in 2020, while typically calculating the performance in 2019, including all 3. In that case, it won’t give the correct view of the portfolio. Also, there is also a possibility that the mutual funds and bonds may or may not perform as well in the future.

Here the survivorship bias impacted the results of the portfolio in 2020. An investor that follows this information, without knowing the unrecorded data, will take a misguided investment decision that might lead him to losses in the future.

It is critical to determine if the potential risk and loss would be more than the potential gain or if one may suffer from survivorship bias.

Example of Survivorship Bias

Assume these figures for Mutual Fund returns and that all funds meet the researcher’s criteria

Fund Historical Return Status
A 10% Fund Still Active
B -6% Fund closed due to acquisition
C -3% Fund closed due to poor performance
D 9% Fund Still Active
E 5% Fund Still Active

If we calculate the return considering all the funds in the portfolio since they meet the possible criteria, the average return would be 3%. However, due to survivorship bias, if we calculate only the active funds, the average return would be 8%

This makes it very important for the researcher to do a careful, in-depth study of the data. However, it is difficult to notice omissions, and hence they may fall victim to survivorship bias.

The actual database contains thousands of data observations. It is very difficult to track the omissions. Implementing set rules and procedures, accurate data-keeping and auditing, educating staff about good practices are necessary for data managers. Responsible data managers will automatically reduce the risk of survivorship bias.

Impact of Survivorship Bias

It is essential to understand how survivorship bias impacts an investor. Survivorship bias tends to present a conclusion to the investor that might look overly optimistic or overly pessimistic.

The bias occurs when the investment manager closes the funds in the market for various reasons. This leads existing funds to survive very well in the forefront in the market, getting the most exposure. At the same time, it leaves out observations that stopped existing due to these market conditions.

In the case of mutual funds, survivorship bias skews the returns to seem optimistic due to consideration of only mutual funds currently in existence. Due to suitable investment strategies or timely reactions from the management, these mutual funds have survived difficult situations such as economic recessions and pandemic scenarios.

The mutual funds that fell due to the recession or pandemic and were forced to shut because of poor performance are not included while calculating returns.

Since all mutual funds- surviving or not surviving- are not considered, the positively skewed net return won’t depict the actual returns.

To understand the actual returns of the mutual fund scenario, it is necessary to evaluate the returns regardless of the period in the study.

Avoiding Survivorship Bias

To avoid survivorship bias, one can do certain simple things before researching any database. Investors should become aware that survivorship bias could be a factor influencing the performance of their investments. It is necessary to pick data from very selective data sources to reduce the risk of survivorship bias. If the data is picked from a biased source, the overall result of the research would be biased as well. Make sure to check that while doing the valuation of a portfolio or database, the observations are intact and not removed if no longer in existence. Regardless of the performance status, the portfolio or database should contain all variables.

By doing this ensures that the decision is based on accurate and correct observations and valuation.

On a more sophisticated level, the market researchers examine fund survivorship bias and how the funds close to gauge omitted observations and historical trends and add the analyzed data to fund performance monitoring. Including quantitative fund research is also helpful at times to mitigate survivorship bias.


We have observed how survivorship bias can lead to unreliable information if any observation is omitted and its impact on traders, managers, and mutual funds. Researchers should use the correct database consisting of both best performing and worst performing variables to determine a correct approach towards decision-making while investing in mutual funds. Though survivorship bias is rampant in the market, investors blindly follow idealistic portfolios and fund managers, so the risk of survivorship bias can be mitigated by conducting good research using correct databases.