Emerging Hedge Funds: A Source of Alpha

October 2010

This paper examines emerging hedge fund alpha, the excess return that young, newly launched managers tend to earn relative to peers in the same strategy. Most net capital flows into hedge funds have gone to the largest, most established firms, yet those firms make up only a small share of the active universe. The analysis compares the performance of emerging funds, defined here as managers in roughly their first three years, with that of the very largest funds, measured against appropriate strategy benchmarks over a multi-year sample. It suggests that emerging funds tend to add alpha relative to their strategy peers, while the largest funds tend to perform close to their peer group. The evidence also indicates that this pattern is not explained by emerging managers taking on more risk. Because emerging funds are numerous, an allocator can diversify across many of them rather than relying on a narrow set of names. The paper is written for institutional allocators, OCIOs, and consultants evaluating hedge fund manager selection, and it frames its conclusions as analytical perspective grounded in a bias-adjusted historical study.

What This Paper Examines

• How the performance of emerging hedge funds compares with that of the largest, most established funds, measured against the same strategy benchmarks.
• Whether emerging funds achieve their results by taking more risk, or with lower volatility and shallower drawdowns during periods of stress.
• Why reporting biases, in particular instant-history bias and liquidation bias, can distort emerging fund returns, and how they can be controlled.
• How the breadth of the emerging fund universe supports diversification across managers rather than reliance on a narrow selection.
• Which investor concerns weigh against emerging managers, and how partnership and seeding arrangements may help address them.

Key Findings

• Emerging funds have tended to add alpha relative to strategy peers, while the largest funds have tended to track their peer group. Younger managers, measured against appropriate strategy benchmarks, are associated with positive excess return. The very largest funds are associated with performance close to their strategy peers, which suggests investors are drawn to size for reasons other than superior returns.
• The higher alpha of emerging funds is not explained by higher risk. Contrary to the natural assumption that stronger returns require more risk, emerging funds are associated with lower return volatility than their largest peers. In part because of that lower risk, they also tended to experience shallower drawdowns during stress episodes such as 2008.
• Correcting for reporting biases is essential to a credible estimate. Instant-history bias, when successful funds add their earlier record on first reporting, and liquidation bias, when failing funds stop reporting, can both inflate apparent returns. Excluding returns before a fund first reports and retaining recently closed funds in the sample produces more realistic, investable estimates.
• Emerging funds are numerous, which supports diversification rather than a narrow bet. A large share of active funds are in their first three years, so emerging managers are not scarce and need not be drawn from a limited pool. That breadth lets an allocator spread idiosyncratic manager risk across many funds.
• Investor concerns about smaller managers can be addressed through structure. Higher return dispersion, along with infrastructure and operational risk, can discourage allocations to emerging funds. Separate accounts, independent risk oversight, and partnership or seeding arrangements with established institutions may help mitigate these concerns while preserving the return characteristics of emerging managers.

The Authors

This paper is part of the long lineage of quantitative research that Versor’s founders began earlier in their careers and continue to build on at the firm today.

Deepak Gurnani, Founder and Chief Investment Officer

Deepak Gurnani is the Founder and Chief Investment Officer of Versor Investments. Deepak has three decades of experience in applying quantitative methods to uncover alpha across global equity markets. Over the past decade, he has focused on pioneering the use of AI and alternative data in equity investing.

Nirav Shah, Founding Partner, Investments

Nirav Shah has over 20 years of experience in quantitative research, asset allocation, and developing scalable systems. He has been involved in the design, development, and management of Event Driven strategies at Versor since the firm’s inception, and specializes in integrating advanced AI and ML-based models for Equity Events.

Ludger Hentschel, Founding Partner, Investment Advisor

Ludger Hentschel joined Versor Investments as a Founding Partner and is based in New York. Ludger has over 20 years of experience in quantitative research and investing.

Disclaimer: Past performance is not necessarily indicative of future results. Not an offer to sell or a solicitation of any type with respect to any securities or financial products.

Methodology: The analysis draws on a database of corporate bond and loan defaults covering US and international companies, using Moody’s Default Risk Service for default and resolution events, LoanX and LPC for loans, and multiple bond price sources, including IDC, Markit, Moody’s, and Altman/NYU, combined into a single price history for each security. The default sample spans the late 1980s onward, and the portfolio performance analysis covers the period from 1990 to 2009.

The method is systematic and rules-based. The study forms a long-only simulated portfolio of defaulted bonds and loans, rebalanced monthly, that adds companies after a default event and removes them at resolution. Securities are weighted by market value subject to concentration limits on any single issuer and any single sector, with the portfolio allowed to hold cash when defaults are scarce. The universe filters out very small issues, non-corporate defaults, debtor-in-possession loans, and similar exclusions, and the return calculation generally excludes debt-to-equity conversions, which makes the estimates conservative. Results are compared against high yield indices, a reported hedge fund distressed index, and other defaulted-debt indices, with a serial-correlation adjustment applied to account for stale pricing.