Hedge funds not reporting to commercial databases have a significant impact on the perceived performance and size of the hedge fund industry. Here are key points from the research:

• Size and Performance Discrepancies: The total returns earned by hedge funds that do not report to any public database are significantly higher than those reported to public databases. Over the sample period, non-publicly reporting funds achieved a total return of 29%, while publicly reporting funds returned only 10%3.
• Alpha vs. Systematic Risk: The outperformance of non-publicly reporting funds is attributed entirely to alpha (risk-adjusted returns) rather than differences in systematic risk factor exposures. This suggests that these funds are generating higher returns through skill or unique strategies not captured by public databases3.
• Investor Flows: Despite their better performance, non-publicly reporting funds receive considerably lower net investor flows compared to publicly reporting funds. This indicates that investor capital allocation is influenced by the availability of performance data, potentially biasing the flow-performance relationship3.
• Selection Bias: The decision to report or not to public databases introduces a negative selection bias in public data. High-performing funds are less likely to report publicly, challenging the assumption that self-reporting biases performance upwards3.
• Regulatory Data: Using regulatory filings like Form PF, researchers have been able to capture data on funds that do not report to public databases, revealing the true size and performance of the industry is much larger and better than previously understood3.
• Flow-Performance Relationship: Previous estimates of the flow-performance relationship in hedge funds are likely biased due to the exclusion of non-publicly reporting funds. Including these funds shows a different dynamic in how performance influences investor flows3.
• Implications for Research: The findings suggest a need to revisit and potentially revise much of the academic research on hedge funds that has relied on incomplete and biased public data3.

In summary, the decision by many hedge funds, especially larger and better-performing ones, to not report to commercial databases has led to a significant underestimation of the hedge fund industry's size and performance. This has broad implications for investors, researchers, and regulators, highlighting the need for more comprehensive data collection methods to accurately assess the industry.

Other studies have also explored the issue of hedge funds not reporting to commercial databases and the biases this introduces in understanding the hedge fund industry:

• Agarwal, Fos, and Jiang (2013) conducted a formal analysis of self-reporting bias in hedge fund databases by examining the quarterly equity holdings of hedge funds that file Form 13F with the SEC between 1980 and 2008. They found that funds initiate self-reporting after positive abnormal returns, which do not persist into the reporting period. Funds that cease reporting often experience return deterioration and outflows. The authors noted that funds facing higher costs of disclosure (e.g., those with strategies likely to be revealed through disclosure) are less likely to report publicly, while those seeking funding (young and medium-sized funds) are more likely to initiate disclosure. Their research highlighted the significant self-selection bias in commercial databases, suggesting that reported performance might not reflect the broader universe of hedge funds1.
• NilssonHedge examined the potential for selection bias in voluntarily reported hedge fund performance data. They constructed a set of returns from hedge funds that do not report to commercial databases and found that funds reporting to these databases significantly outperform non-reporting funds. This suggests a selection bias that might exaggerate the average skill of hedge fund managers. They also noted issues like backfill bias, survivor bias, and differences in reporting managers between databases, which can skew performance estimates4.
• Kenan-Flagler Business School researchers, including Greg Brown, Christian Lundblad, and William Volckmann, published a study in February 2024 exploring the impact of missing data from large institutional hedge funds. They found that including these funds, which do not report to commercial databases, significantly increases average hedge fund performance by more than two percentage points annually. The study highlighted that these hidden hedge funds had higher alphas, lower market risk factor exposure, and less flow sensitivity to performance5.
• Alpha Architect summarized a study by Barth, Joenväärä, Kauppila, and Wermers, which used regulatory filings (Form PF) to capture data on funds not reporting to vendor databases. They found that the hedge fund industry was managing $6.0 trillion in worldwide net assets by 2019, significantly larger than prior estimates. Funds not reporting to databases constituted a substantial portion of the industry's AUM. Their findings indicated that non-vendor-listed funds had higher alphas and less fragile capital, suggesting that the decision to list with commercial databases biases performance estimates downward6.

These studies collectively emphasize the importance of considering the unreported segment of the hedge fund industry to gain a more accurate understanding of its size, performance, and risk characteristics. They challenge the conventional wisdom derived from publicly reported data and highlight the need for comprehensive data collection methods to assess the true nature of hedge fund performance and investor flows.

The research paper titled "The Hedge Fund Industry Is Bigger (and Has Performed Better) Than You Think" by Daniel Barth, Juha Joenvaara, Mikko Kauppila, and Russell R. Wermers, published in February 2020, addresses several key points about the hedge fund industry:

• Industry Size: The paper suggests that the hedge fund industry is significantly larger than commonly perceived. Traditional estimates might undervalue the industry size due to various factors, including the inclusion of only some of the assets under management (AUM) in databases.
• Performance: The authors argue that hedge funds have performed better than many investors believe. Their analysis shows that when accounting for biases in traditional data sources, the returns of hedge funds appear more favorable, especially when considering the risk-adjusted performance metrics.
• Data Bias: They highlight several biases in hedge fund data collection:
• • Survivorship Bias: Funds that fail or cease operations are often removed from databases, skewing performance upwards.
  • Backfill Bias: New funds might report historical performance only when it's positive, leading to an overestimation of performance.
  • Self-selection Bias: Only funds willing to report their data are included, potentially excluding those with poor performance.
• Adjusted Performance Metrics: Using more comprehensive data sets, the researchers attempt to correct for these biases, revealing that hedge funds have provided superior risk-adjusted returns compared to what traditional benchmarks suggest.
• Implications for Investors: The study implies that investors should reconsider the perceived underperformance of hedge funds. It suggests a need for better data and methodologies to evaluate hedge fund performance accurately, which could lead to more informed investment decisions.
• Methodology: The authors likely use an extensive database or proprietary data sets to account for the biases mentioned above, offering a more nuanced view of the industry's performance.

This paper challenges the conventional wisdom regarding hedge fund performance and size, providing a deeper understanding of the industry by addressing data quality issues and adjusting for biases in existing datasets1.

Instant history bias, a significant issue in hedge fund performance reporting, can substantially distort perceived returns by backfilling historical data when funds are added to databases, with studies revealing bias estimates ranging from 14 to 88 months.

Instant History Bias Defined
Instant history bias occurs when hedge funds backfill their historical performance data upon joining a database, potentially inflating their reported returns. This practice can lead to a significant overestimation of fund performance, as funds are more likely to report and backfill favorable historical results. The bias is particularly problematic because it can create an illusion of consistent outperformance, misleading investors and skewing overall industry performance metrics. The extent of this bias varies across studies, with estimates ranging from 14 to 88 months, highlighting the substantial impact it can have on the accuracy of hedge fund performance evaluations.

Fung and Hsieh Findings
Fung and Hsieh's research in 2000 provided valuable insights into the extent of instant history bias in hedge fund performance reporting. By employing a novel approach of deleting the first 12 months of all funds' reported returns in the TASS database, they measured an instant history bias of 14 months. This methodology helped quantify the impact of backfilled data on perceived fund performance, highlighting the need for caution when interpreting historical hedge fund returns. Their findings laid the groundwork for subsequent studies and contributed to a growing awareness of the potential distortions in hedge fund performance metrics.

Malkiel and Saha Analysis
Malkiel and Saha's 2005 study revealed a substantial backfill difference of approximately 88 months from 1994 to 2003, highlighting the significant impact of instant history bias on hedge fund performance reporting. This finding underscores the potential for considerable overestimation of returns, as funds may selectively report favorable historical data when joining databases. The extended backfill period identified by Malkiel and Saha suggests that investors should exercise caution when evaluating hedge fund track records, particularly those spanning nearly a decade prior to a fund's inclusion in a database.

Posthuma and van der Sluis Study
The research conducted by Posthuma and van der Sluis in 2003 shed light on the pervasive nature of backfilled returns in hedge fund databases. Their analysis of the TASS database revealed that over half of all reported returns were backfilled, with an estimated instant history bias of approximately 36 months during the 1996-2001 period. This finding underscores the significant impact of backfilling on hedge fund performance metrics, suggesting that a substantial portion of reported returns may not accurately reflect real-time performance.