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If you are an investor, you may consider some assets or funds in your portfolio very strong and therefore more valuable to you. At first glance, it may seem that there should be as many robust and growing assets as possible in order for your investment strategy to be more profitable.
Before making a decision about rebalancing, think about whether you really have enough information for an objective assessment and a balanced action. You may have encountered survivorship bias. This means making a false judgment because you only focus on the assets that succeed, not those that fail.
Below we explain why many inexperienced financial analysts pay more attention to the visible winners but leave out investments that failed or disappeared and why this can make a market, fund, or strategy look stronger than it really is.
What Is Survivorship Bias?
A survivorship bias is a type of logical error where a person only pays attention to people or entities that “survived” a crisis, a market decline, or another problematic process and ignores those that didn’t.
You can fall victim to this bias when looking at role models or positive stories. For example, you follow top-performing traders or corporate leaders and explore their path to success. You might think that if you do exactly what they did, you will also succeed. In reality, this is never guaranteed.
Businesses do the same. Companies succumb to a survivorship bias when analyzing the financial achievements, advertising methods, and product development strategies of competitors. They focus on what they think worked, not on numerous unverified hypotheses, consequently repeating the mistakes of others rather than learning from them.
How Survivor Bias Enters Investment Data
Survivorship bias in investing limits the ability of holistic analysis. The main threat of prejudice is that everyone is talking about the most popular or fastest-growing assets. These simply outshine other investment tools in news and reviews. So, investors get an incomplete, one-sided view of the market trends.
As those assets that didn’t survive are often less visible compared to exceptional and successful ones, it’s hard to make correct investment performance evaluations. This commonly occurs in mutual funds and market indices.
Database Maintenance
Both commercial and free databases investors analyze often are living-only registries, because they include currently listed tickers. If some securities were delisted, they are not in the data table. For example, hedge-fund databases often keep only funds that are still reporting. Investment pools don’t prioritize underperforming share classes or omit them. That’s why you don’t see all the closed and merged firms that were kicked out, so using the bases and extracts retroactively may harm your trading research.
Companies That Left an Index or a Ranking
Index restructurings and ranking methodologies also contribute to strengthening the bias. For instance, the S&P 500 list constituents change, and 20–25 weak firms are removed yearly. Overviews that select the top 10 or top 50 companies or cryptocurrencies by market capitalization use current fundamentals and don't take into account those who have gone bankrupt or fallen in value. This affects historical returns comparisons. If you want a clearer analysis, it’s better to rely on an index return calculated with a maintained methodology.
Backtests
In backtesting, the final return at delisting or the liquidation value is often ignored, so investors fail to see the true economic outcome of a strategy. Investment performance metrics like CAGR and drawdown are unrealistic because the worst outcomes, mergers, or reverse splits are excluded or softened. Mutual fund survivorship bias inflates the average performance by about 0.5–1.5% per year.
In practice, a poorly configured backtest algorithm doesn’t enforce point-in-time audits and allows investing in stocks that were not yet in the index at that time. Proper backtests must reconstruct the lists of securities or cryptos as of each investment decision date, preserve the impact of bankruptcies, and retain payouts or near-zero values.
Marketing Pages and Whitepapers
The goal of marketers luring customers to investment firms is to present the best metrics. They strive to show you should trust them with your money. This is an intuitive approach, as investors won’t place money in unprofitable or low-yield projects. That is why marketing materials show the backtest results of large-cap firms without specifying whether they used historical returns and promo pages display the ARR of funds using only those that are active.
Survivorship Bias vs Selection Bias vs Availability Bias
There are three similar types of prejudices that occur at different stages of the evidence pipeline: survivor bias, selection bias, and availability bias.
Survivorship bias makes an investor analyze only entities that are still listed or operating, so failures are missing because they no longer exist or are no longer reported. The average returns and success rates are overstated.
Selection bias implies that the sample is chosen intentionally depending on the outcome or correlating factor. There’s a logical rule that determines who is included in the dataset. You may analyze only the 100 companies based on size in a current universe. Hence, your estimates reflect the subgroups or drivers, not the real picture.
Availability bias means that you overweight recent, vivid examples that are easier to recall. The data may be complete, but your judgment is based on what comes to mind first, such as recent headlines, iconic launches, and early unicorns. Consequently, probabilities and expected values are misestimated.
These prejudices require separate fixes:
A Simple Example With and Without the Missing Cases
Our Venga expert has prepared a nice illustrative example that shows how survivorship bias inflates average returns. These numbers are hypothetical and for demonstration only.
Assume that you have a portfolio of 10 small investments made in 2020, each with $1,000 initial capital. By the end of 2025, some have grown and some have failed.
The total return will be the sum of all returns—50%. The average return will be +5% per investment over the period. This view includes the reality with big winners, modest gainers, and failures.
Now let’s say that a database only includes investments that are still active. You only take into account the survivors and drop failed funds and stocks. The set will be like this:
The total return will rise to 500%. The average return will then become +100% per investment over the period. So, if you exclude the 5 investments, the returns seem 20 times higher, even though the strategy hasn’t changed.
In reality, investors experience total losses, slow or partial recoveries, and profits. They get the outcome of +5%. A survivor-only dataset would show +100%, giving a misleading impression of the strategy quality.
Why the Survivorship Bias in Investing Changes Decisions
If your market or fund analysis will focus purely on survivors, you may miss out on the whole picture. The survivors are not a representative sample, so investors get an incorrect idea of what actually happens on the market and can make poor choices.
If you face survivorship prejudice, you are likely to have:
- Overstated returns, as these rise when inferior equities are excluded.
- Understated risk and failure rates, higher volatility, and deeper drawdowns.
- Exaggerated probability of success and a higher chance of poor or negative returns.
- Overexposure to risky strategies and insufficient savings.
- A desire to chase recent winners and pay higher fees for fake “star” managers.
Survivorship Bias in Crypto Markets
If you trade cryptocurrencies and would like to choose a crypto project or a digital instrument to invest in, survivor bias may also affect you. If you analyze only the surviving tokens, NFTs, exchanges, or projects, the return distortion can be large.
Listings and delistings in crypto are frequent, and some trading pairs quickly become unavailable. You should consider this and choose a comprehensive asset group that you thoroughly and rationally analyze. Otherwise, you can belatedly discover misleading factors or anomalies that negate or weaken returns or poorly hedge risks.
How to Check Whether a Dataset Includes Non-Survivors
To avoid an overly optimistic picture of returns and risk, you have to verify that the dataset or list displays the original universe and includes non-survivors. Check that it has the fields that make backtest methodologies correct, such as:
- Listing date or period;
- Current asset status;
- Closures, liquidations, and delistings;
- Mergers and acquisitions;
- Historical constituents, point-in-time audits;
- Separate asset class records or event logs of changes, where applicable.
How Data Providers Try to Control the Survivorship Bias in Investing
To share trustworthy data on investment performance, some market data providers supply users with built-in tools for point-in-time audits and tips that help you make analysis relevant as well as openly share information about their point-in-time architecture and inclusion rules.
Commonly used crypto datasets tied to exchange listings or public dashboards also tend to highlight tokens that are still trading. So, for truth-seeking crypto investors, there are tools like CoinAPI’s Market Data API that provide crypto metadata that tells when the assets were listed or delisted, help you track historical returns and apply time-bounded eligibility rules.
Note that even if you see a special survivorship-bias-free label, it’s advisable to check the data provider’s methodology and actual columns and rows in your dataset.
What Correcting the Prejudice Does Not Solve
A better database reduces distortion but does not allow you to predict future results. It neither makes a forecast certain nor eliminates look-ahead bias, selection bias, or overfitting. Short samples and changing methodology may still constitute a technical issue.
Apart from that, you need to know that to copy someone and their success, you have to copy their approach, upbringing, timing, connections, and unfair advantages as well, which is almost impossible. As for companies, any high business performance does not mean that the company has become the best of all. In fact, it might just mean that you only see it because it still exists due to a number of factors, including coincidences, luck, or background. Or that these results are hyped or false.
Learn What Is Missing Before Trusting the Average
You need to be very careful and attentive when selecting a fund or product to invest in. It’s not advisable to take a high profitability percentage for granted. An average or success rate is only meaningful if you are sure that the figure came from a reliable source using a comprehensive, survivorship-bias-free methodology.
So, verify that the data provider is proactive about giving trustworthy figures and includes in the datasets it shares all the asset statuses, closures, delistings, mergers, constituents, etc. This should help you avoid investment decisions made from incomplete evidence.
Disclaimer: The content provided in this article is for educational and informational purposes only and should not be considered financial or investment advice. Interacting with blockchain, crypto assets, and Web3 applications involves risks, including the potential loss of funds. Venga encourages readers to conduct thorough research and understand the risks before engaging with any crypto assets or blockchain technologies. For more details, please refer to our terms of service.