Survivorship Bias: Why Investment Results May Not Be What They Seem

Survivorship bias is a common pitfall in finance where the failure process is ignored, leading to an overly optimistic view of past performance. It arises when we only consider investments, companies, or funds that have “survived” to the present day, while omitting those that have ceased to exist, often due to poor performance or bankruptcy. This skewed sample creates a distorted picture of success rates and expected returns.

Imagine evaluating the performance of hedge funds. A database showing only the funds that are currently active and reporting returns might suggest that hedge funds, on average, generate superior returns compared to the broader market. However, this ignores the “graveyard” of hedge funds that have closed down due to losses or underperformance. These defunct funds are often excluded from performance analyses, artificially inflating the apparent success rate of the surviving funds. The funds that disappeared were likely the worst performers, meaning the reported average is higher than the true average if all funds were included.

The implications of survivorship bias can be significant for investors. It can lead to:

• Overestimation of Returns: Believing that certain investment strategies are more profitable than they actually are, based on incomplete historical data.
• Underestimation of Risk: Failing to fully appreciate the likelihood of failure or losses associated with a particular investment strategy, as failed examples are not visible.
• Poor Investment Decisions: Allocating capital to investments that appear attractive based on biased performance data, potentially leading to disappointing results.
• Inaccurate Benchmarking: Comparing investment performance against biased benchmarks that only include surviving entities, leading to an inaccurate assessment of true performance.

Detecting and mitigating survivorship bias is crucial for informed investment decisions. Here are some strategies:

• Seek Comprehensive Data: Look for datasets that include both active and inactive entities. Check for data providers that specifically track and account for fund closures or bankruptcies.
• Consider Longer Time Horizons: Analysis over longer periods increases the likelihood that failed entities will be included in the dataset.
• Be Skeptical of Top-Performer Lists: Understand that top-performer lists often exclude underperforming entities that have ceased to exist. Investigate the selection criteria and potential biases.
• Focus on Risk-Adjusted Returns: Instead of solely focusing on raw returns, consider risk-adjusted performance measures that account for the volatility and potential downsides of an investment.
• Question the Narrative: Be wary of narratives that overemphasize success and downplay the possibility of failure. Always consider the full spectrum of possible outcomes.

By understanding the dangers of survivorship bias and taking steps to mitigate its impact, investors can make more informed and realistic investment decisions, leading to better long-term outcomes.