Backfill bias

What Is Backfill Bias?

Backfill bias, also known as instant history bias or inclusion bias, is a form of investment performance measurement bias that occurs when a new investment fund, typically a hedge fund, is added to a database or financial indexes and its past investment returns are retrospectively included. This practice often leads to an upward distortion of historical aggregate returns for the index or database, as funds with strong past performance are more likely to elect to report their results and be added. This phenomenon falls under the broader category of financial data biases.³⁷, ³⁸

History and Origin

The concept of backfill bias emerged prominently with the growth of the hedge fund industry. Unlike traditional mutual funds, hedge funds are not always required to report their performance publicly, and their inclusion in databases is often voluntary.³⁴, ³⁵, ³⁶ Managers typically choose to submit their fund's data to these databases when they have achieved a period of particularly strong performance, aiming to attract new investors and capital.³³ When these funds are added, database providers may "backfill" the fund's historical performance, incorporating its past returns into the aggregate index data as if it had been part of the index all along.³¹, ³² This creates an "instant history" that overstates the actual performance of the universe of funds, as poorly performing or fledgling funds are less likely to join or be included.²⁹, ³⁰ Studies have suggested that backfill bias can inflate reported hedge fund returns by a significant margin.²⁷, ²⁸ This practice highlights challenges in accurate historical data collection within less regulated asset classes.

Key Takeaways

• Backfill bias occurs when a new fund's past, typically strong, performance is included in a database or index.
• It primarily affects indices and databases for hedge funds and alternative investments due to voluntary reporting.
• This bias tends to inflate historical investment returns and can mislead investors regarding true aggregate performance.
• It is closely related to other data biases, such as selection bias and survivorship bias.
• Investors conducting due diligence should be aware of backfill bias when evaluating historical performance figures.

Interpreting the Backfill Bias

Interpreting the effects of backfill bias involves understanding that reported aggregate investment returns for a category, such as hedge funds, might appear higher than what an investor actually experienced if they had invested consistently in the entire universe of funds. When a fund is added to a database, its historical record, often cherry-picked for its strong performance, is retroactively included. This means the database effectively gains an "instant history" of success.²⁵, ²⁶ This upward distortion can lead investors to overestimate the true average returns of an investment style or market segment. Analysts evaluating portfolio management strategies that rely on historical data must account for this bias to avoid making erroneous assumptions about potential future outcomes.

Hypothetical Example

Consider a hypothetical database that tracks the performance of emerging market hedge funds. In January 2020, the database reports an average annual return of 8% for its existing funds over the past five years. In June 2020, a new hedge fund, "Global Alpha Opportunities," which started in January 2018, decides to join the database. Over its two years of operation (2018-2019), Global Alpha Opportunities achieved exceptional annual returns of 20% and 25%, respectively.

When Global Alpha Opportunities is added to the database, its 2018 and 2019 returns are "backfilled" into the historical data. The database then recalculates its average annual returns for 2018 and 2019, now incorporating the strong performance of Global Alpha Opportunities, even though the fund was not part of the database during those years. This inclusion causes the reported average returns for those earlier periods to increase, making the entire index look more attractive than it was before the new fund's inclusion. An investor reviewing the database in late 2020 might see an inflated historical average, leading them to believe that the emerging market hedge fund sector performed better in 2018 and 2019 than it actually did for existing funds at that time. This is a classic example of how backfill bias can distort reported investment performance.

Practical Applications

Backfill bias is particularly relevant in the realm of alternative investments, especially hedge funds, private equity, and venture capital, where data reporting is often voluntary and less standardized than for publicly traded securities.²², ²³, ²⁴ For investors and researchers, understanding this bias is critical for:

• Performance Evaluation: When assessing the historical investment returns of hedge fund indices or databases, analysts must recognize that backfill bias can lead to an overstatement of actual performance. This is because funds with strong past performance are more likely to submit their data, and this past performance is then retroactively included.²¹
• Asset Allocation: Institutional investors and financial advisors engaged in portfolio management need to adjust their expectations for future returns based on historical data that may be affected by this bias. Relying on inflated historical returns could lead to an over-allocation to asset classes impacted by backfill bias.²⁰
• Due Diligence: Prospective investors in hedge funds should perform rigorous due diligence on individual funds and verify performance claims, rather than relying solely on aggregate database statistics. This helps to separate genuine alpha from statistical artifacts.¹⁹

Research Affiliates, an investment management firm, has highlighted the importance of being aware of biases like backfill bias when evaluating peer group returns, particularly in the context of alternative risk premia strategies.¹⁸

Limitations and Criticisms

A primary limitation of backfill bias is its tendency to artificially inflate reported investment returns, particularly in areas like hedge funds. Studies have indicated that backfill bias can add several percentage points to reported hedge fund returns, potentially accounting for a significant portion of their perceived outperformance or alpha relative to traditional benchmarks.¹⁶, ¹⁷ This overstatement can mislead investors into believing that these asset classes offer higher historical returns than they actually delivered.

Critics argue that the voluntary nature of reporting in the alternative investments space, combined with the practice of backfilling, creates a system where only "winners" are ultimately showcased, while "losers" remain hidden.¹⁵ This distorts the true distribution of returns and makes it difficult for investors to conduct accurate risk management or benchmark performance effectively.

Efforts to mitigate backfill bias include database providers limiting or even prohibiting the backfilling of performance data.¹⁴ However, researchers still face challenges in fully correcting for the bias, as simply truncating a fixed number of initial returns may not be sufficient to eliminate its effects, especially for funds with longer backfill periods.¹³ The challenge in obtaining comprehensive and unbiased financial data remains a significant hurdle in accurately assessing these markets.

Backfill Bias vs. Survivorship Bias

While often discussed together and closely related, backfill bias and survivorship bias represent distinct forms of data distortion, particularly prevalent in the analysis of hedge funds and other investment vehicles with voluntary reporting.

Backfill bias occurs when a fund, upon joining a database or index, retrospectively includes its past performance, often a period of strong investment returns. This inflates the historical average returns of the database or index because only successful funds are typically motivated to join and share their past "instant history."¹¹, ¹²

Survivorship bias, on the other hand, arises when unsuccessful funds that cease to exist or are liquidated are removed from a database or index, and their past performance is also removed from the historical record. This results in the remaining (surviving) funds' performance appearing better than the actual average performance of all funds that ever existed in that universe.⁹, ¹⁰

The key difference lies in when the bias is introduced: backfill bias relates to the inclusion of past data from new, successful funds, while survivorship bias relates to the exclusion of data from failed funds. Both biases tend to exaggerate historical investment performance and lead to an overestimation of actual returns. A study published in the Financial Analysts Journal in 2005 highlighted how the voluntary reporting and backfilling of favorable results, combined with significant attrition in the hedge fund industry, lead to an upward bias in returns calculated from hedge fund databases.⁸

FAQs

Why is backfill bias more common in hedge funds?

Backfill bias is more common in hedge funds because, unlike traditional mutual funds, they often have less stringent reporting requirements. Fund managers can voluntarily choose if and when to report their financial data to databases. They typically do so after a period of strong investment performance, allowing them to retrospectively include these favorable returns.⁷

How does backfill bias affect reported returns?

Backfill bias typically inflates reported investment returns for the historical periods when the newly added funds were operating but not yet included in the database. This is because funds with poor or average performance are less likely to join, meaning the "backfilled" data primarily consists of positive outcomes.⁵, ⁶ This can lead to an overestimation of the true average returns of an asset class or strategy.

Can backfill bias be avoided?

Completely avoiding backfill bias can be challenging due to the voluntary nature of data reporting in some sectors, particularly alternative investments. However, databases and researchers attempt to mitigate it by "truncating" or removing the initial period of returns for new funds, or by simply only including data from the date a fund starts reporting.³, ⁴ Investors should also conduct thorough due diligence and be skeptical of exceptionally strong historical performance claims without understanding the data's source and methodology.

Is backfill bias the same as selection bias?

Backfill bias is a specific type of selection bias. Selection bias broadly refers to any bias introduced when the sample of data is not representative of the entire population. Backfill bias is a manifestation of selection bias where the "selection" of funds to be included in a database is influenced by their past strong performance.¹, ²