Demystifying Hedge Funds: An Overview of Trades and Alpha

December 2010

This overview paper examines the factors that drive hedge fund returns and alpha across five major strategies: convertible arbitrage, merger arbitrage, equity market neutral, distressed debt, and fixed income relative value. It sets out the questions institutional allocators ask about hedge funds, whether they add sustainable alpha, whether the average manager earns fees, and whether common return drivers can be accessed more transparently, and then makes the case that many managers within a strategy share a common set of trades. Simulating those trades at the security level reproduces much of a strategy’s return character, which helps separate genuine manager alpha from returns that are simply the strategy at work. Because the approach is rules-based, it is transparent by construction. The paper is written for allocators, OCIOs, and consultants, and it frames its conclusions as analytical perspective grounded in long historical simulations. It also serves as the summary companion to the firm’s detailed strategy-by-strategy studies.

What This Paper Examines

• Whether hedge funds add alpha, how they generate it, and whether that alpha tends to be sustainable.
• How much of a strategy’s return comes from components shared across managers, rather than any single manager’s skill.
• Whether the core return of a strategy can be captured with transparent, rules-based trades.
• How individual strategy portfolios combine into diversified multi-strategy portfolios.
• Whether multi-manager indexes and index-based replication can reproduce a strategy’s returns, or only part of them.

Key Findings

• Within a strategy, a large share of return variation comes from components common to many managers. A principal-components analysis suggests that a handful of shared factors explain much of the variation in returns within a strategy. Identifying them helps distinguish manager alpha from the strategy itself.
• The core return of each strategy can often be captured with transparent, rules-based trades. Simulating a strategy’s main trades reproduces much of its return character. This demystifies the strategy and supports transparency, rather than relying on statistical curve-fitting.
• A high correlation to an index is not the same as capturing its alpha. A portfolio can track a benchmark’s risk closely while still leaving much of its average return behind. Both risk and return matter when evaluating any replication claim.
• Index-based replication and multi-manager indexes tend to capture risk more readily than alpha. Because these strategies hedge macro risks, approaches built from broad index exposures often reproduce the risk profile but not the trade-level return, and multi-manager indexes can carry reporting and selection biases.
• A core-satellite approach can pair transparency with selective manager skill. The evidence points to a transparent, rules-based core, run with controlled leverage and diversification, augmented by managers who have demonstrated alpha within their strategy.

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.

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 the firm’s strategy-by-strategy research across five hedge fund strategies, each simulated at the security or trade level rather than through index-level regressions alone. The universes span global equities across developed Europe, Japan, the UK, and the US; announced mergers across North America, Europe, and Asia; large convertible bonds in North America and Europe; defaulted corporate bonds and loans; and government bonds, swaps, and related derivatives across four regions.

The methods are systematic and hypothesis-driven. They include a principal-components decomposition of hedge fund returns into common and idiosyncratic parts, factor-based and rules-based portfolio construction, and returns-based comparisons to hedge fund and investable indexes, using the Dimson and Newey-West adjustments for stale prices and smoothing. Third-party data referenced across the underlying studies include Bloomberg, Interactive Data Corporation, Worldscope, Thomson Reuters I/B/E/S, MSCI Barra, Moody’s, Markit, LoanX, and Monis. Sample periods run from the early 1990s through 2009.