When assessing an investment, most people instinctively gravitate toward past performance, top-ranked funds, and widely cited market data, which might only include the winners. Investments that failed to perform, like shutdown funds, delisted stocks, etc., are often ignored, not due to malice but due to the intrinsic nature of certain metrics and a bias of investors that prevent holistic analysis. This selective view of investing stems from survivorship bias, which inflates the picture of what investing looks like. Such a bias can lead to unprecedented loss and unfair expectations.
Therefore, this blog decodes what survivorship bias is, why it matters in the world of investing, and how it can be identified and mitigated.
What is survivorship bias?
A type of cognitive error, survivorship bias occurs when an analysis focuses exclusively on existing entities that have survived a particular process, while completely overlooking those that did not make it through. For example, if a study is done on the durability of aeroplanes, and only those planes that land safely are considered, whilst planes that crash are not, it would be called a survivorship bias.
Survivorship bias exists in different fields where decision-making is involved, including medicine, business, finance, etc.
In finance, survivorship bias occurs when performance studies, fund databases, and market assessments derive conclusions based only on existing assets. These statistics do not include mutual funds that were merged or shut down owing to poor performance, equities that were delisted from indexes, or corporations that declared bankruptcy. This results in figures that look more promising to an investor than they actually are, causing poor decision-making. For instance, A study of active mutual funds’ 10-year returns may provide an average return of 9%, but if the 30% of funds that closed over that period due to underperformance are included, the average might fall to as low as 3%.
Therefore, survivorship bias matters because it can impact investment decisions intensely.
Why survivorship bias matters
Survivorship bias gives a distorted idea of investment performance, directly creating an adverse impact on investor decision-making. Discussed below are different factors that make survivorship bias material for investment consideration.
- Inflated Idea of Investment: Survivorship bias makes investments appear better than they actually are. For instance, when analysing an index performance, an investor can note consistent double-digit returns. However, what an investor misses are the stocks that are no longer part of the index because they delisted due to poor performance.
- Wrongful Perception of Risk: Survivorship bias not only curates a wrong return expectation but also results in a poor perception of risk. If the failures of a category are not adequately represented, investors fail to get a true and nuanced idea of investment in the sector.
- Dynamic Index: Investors commonly use indices and benchmarks to make investment decisions. However, what beginners or other stock market investors often miss is the fact that these indices are dynamic. This means that new companies are added and underperforming companies are dropped. Therefore, without a periodic review of index composition, investors can miss out on facts.
Let us take some examples to explore how survivorship bias actually works.
Survivorship bias examples
Illustrations given below can help explain survivorship bias optimally.
Example 1: Mutual fund performance data
Imagine Fund A is an underperforming fund, while Fund B is a top performer in this category. The table shows their individual performance and what happens if they were merged.
| Fund A (Individual) | -3% |
| Fund B (Individual) | +12% |
| Fund AB (After the merger) | Net performance becomes +9% |
If an investor does not undertake qualitative research and revisit their history, they might solely rely on the positive performance of the merged fund. The poor historical performance of Fund A, which indicates its inefficient investment strategy as well, is ignored.
Example 2: Analysing top-performing funds only
Mr A decides to invest in mutual funds. However, he analyses the risk-return metrics of top performers only and ignores the average or bottom performance recorded in the category. This makes Mr A make a decision based on the best-case scenario alone. Without an analysis of average or bottom performance, Mr A cannot decode the extent of loss he can suffer, resulting in an inappropriate hedge.
Example 3: Stock backtesting
The process of applying an investment strategy to historical market data to gauge potential profitability and risk before investing is called stock backtesting.
Imagine if K applies a strategy to historical stock data that excludes delisted or bankrupt companies. In such a scenario, K analyses potential return and risk of stocks that exist despite market challenges, due to a certain level of optimal management. The strategy ignores the actual extent of potential loss, as experienced by stocks that had to exit the market.
However, there are different ways by which investors can avoid survivorship bias.
How to prevent survivorship bias
Even though survivorship bias is an ongoing problem, investors may lessen its impact in different ways, as illustrated below.
- Holistic Consideration of Existing and Non-Existing Entities: Investment decisions should ideally be backed by comprehensive datasets that include both surviving and non-surviving assets. Historical analysis of liquidated or delisted assets and bankruptcy of issuers gives investors an idea of the extent of risk they might have to bear.
- Quantitative and Qualitative Research: Rather than relying only on qualitative data, investors should also consider qualitative measures to gain a nuanced understanding of performance. For instance, investors should analyse fund managers and their history while choosing mutual funds. Such an analysis helps decode the efficiency of the investment strategy adopted.
- Diversification: Allocation of investible funds in a range of assets, varying in their risk profile, return, and other characteristics, helps mitigate potential loss. In a well-diversified portfolio, if one asset category records a loss, the other can earn sufficient gains to compensate.
- Independent Third-Party Analysis: The impacts of survivorship bias can be mitigated by consulting the independent analysis from third-party sources that are not financially motivated to portray a selective picture. Particularly in peer-reviewed academic research, survivorship-bias-free datasets are often used.
Besides survivorship bias, several other barriers can hinder an effective investment strategy.
Other types of research bias
Several biases that stem from individual investors or general market tendencies can distort the quality of investment analysis and financial research. Investors should be aware of the following biases that can adversely impact investment decisions.
| Bias | Meaning | Example |
| Confirmation Bias | Investors make a selective search for evidence that supports their pre-conceived notions, while ignoring contradictory data that is material. | Mr A has held stock K for over 10 years, and he consults research material that quantifies the stock as good while ignoring those that raise concerns |
| Selection Bias | The sample set used for analysing a particular occurrence or thesis is not representative of the whole population | A study on investment habits in India has 90% respondents earning over INR 1 crore, while a large section of Indian society earns below it |
| Recency Bias | Giving too much weightage to recent events to gauge future performance, while giving less than required consideration to past data | Mr K invests in Fund A because it became the top performer in the category in the recent quarter, while ignoring the negative 3-year performance |
Different biases, whether individually or in combination, can restrict nuanced and comprehensive analysis. This creates a rosier picture of the investment landscape than what is true. Therefore, a comprehensive qualitative and quantitative analysis of historical and present performance is pertinent.
Conclusion
Survivorship bias makes investors consider the performance of assets that exist, while ignoring those that could not survive due to market headwinds or poor strategy. It minimises downside risk, causing a distorted image of potential risk and return. This bias exists in several decision-making quarters and impacts various assets like mutual funds, stocks, and so on.
Besides survivorship bias, various other biases like confirmation bias and recency bias can impact investing. Undertaking both quantitative and qualitative research is key to avoiding such biases. Furthermore, referring to historical data, decoding companies that no longer exist, along with the reasons for their liquidation, is key.
FAQ‘s
In finance, survivorship bias occurs when performance studies, fund databases, and market evaluations draw conclusions only on existing assets. These figures exclude mutual funds that were merged or closed due to poor performance, stocks that were delisted from indexes, and businesses that declared bankruptcy.
A classic example of survivorship bias involves mutual fund performance reporting. When a fund house reports category-wise returns, it often includes only currently active funds, omitting those that were merged or liquidated due to poor performance. This makes the average return appear significantly higher than what investors actually experienced across all funds in that period.
The four types of biases include survivorship bias, recency bias, confirmation bias, and selection bias. Different biases, whether individual or combined, might limit a nuanced and complete analysis. This paints a rosier image of the investing environment than is accurate. As a result, a detailed qualitative and quantitative examination of past and present performance is necessary.
Survivorship bias is closely related to the logical fallacy, which involves drawing inferences from an incomplete collection of evidence. Treating the surviving group as typical of the entire leads to incorrect generalisations. While not necessarily intentional, the reasoning that follows is deemed logically flawed.
