Demystifying Hedge Funds: An Analysis of Fixed Income Relative Value Trades and Alpha

December 2010

This paper examines the fixed income relative value strategy and presents a systematic, trade-based way to capture its core alpha. Fixed income relative value portfolios hold long and short positions in government bonds, interest rate swaps, and related derivatives, sized so that interest rate risk is largely immunized and returns depend on targeted relative value opportunities. Because those returns do not rest mainly on the direction of interest rates, they tend to have low correlation with bonds and equities, which can offer meaningful diversification benefits. The analysis shows that much of the strategy return can be described by a set of common trades, including curve steepener, butterfly, asset swap, futures basis, inflation breakeven, on/off the run, mortgage, emerging markets, and volatility, each oriented toward positive carry. Because the approach is founded on explicit decision rules, it is transparent by construction, which speaks directly to the process and portfolio transparency that institutional investors increasingly expect. The paper is written for institutional allocators, OCIOs, and consultants evaluating relative value fixed income strategies, and it frames its conclusions as analytical perspective grounded in a long historical simulation.

What This Paper Examines

• How the fixed income relative value strategy generates returns, and why immunizing interest rate risk tends to leave returns with low correlation to bonds and equities.
• Whether the core return can be captured with a transparent, rules-based set of carry-oriented trades.
• Which trades, including curve steepener, butterfly, asset swap, futures basis, inflation breakeven, on/off the run, mortgage, emerging markets, and volatility, make up the strategy.
• The role of positive carry, relative to yield curve forecasting and the reversal of pricing errors, as a source of return.
• Whether index-based replication can reproduce the strategy’s alpha, or only its risk.

Key Findings

• A substantial portion of the strategy return is attributable to positive carry rather than forecasting skill. Trades oriented toward positive carry earn income when yield curves stay constant, drawing on risk, liquidity, and option premia. This suggests that much of the return does not depend on a manager’s ability to predict yield curve moves.
• Immunizing interest rate risk leaves returns with low correlation to traditional assets. Sizing long and short positions to hedge out interest rate exposure removes the dominant source of risk in bond markets. As a result, returns tend to have low correlation with bonds and equities, which is where the strategy’s diversification benefit comes from.
• Much of the core return can be captured with a transparent, rules-based set of trades. A systematic implementation built on explicit decision rules helps demystify the strategy. It also supports the process and portfolio transparency that institutional investors expect, in contrast to more opaque, discretionary approaches.
• Index-based replication tends to capture the strategy’s risk but not its alpha. Because the strategy deliberately hedges interest rate risk, linear replication built from broad index exposures struggles to identify the true return drivers. Capturing the alpha generally requires implementing the underlying trades directly.
• Disciplined leverage and broad diversification are structural features, not afterthoughts. Controlling leverage and diversifying across many trades and multiple regions can soften drawdowns during stress episodes. Because the trades are weakly correlated, even sub-strategies that are unprofitable on their own can improve the overall portfolio through diversification.

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.

Versor’s founders co-authored this research with Leonid Keyser and Edward Nakon, colleagues at the firm where this work was originally conducted.

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 simulates a global trade-based portfolio composed of nine fixed income relative value trades, implemented across developed Europe, Japan, the UK, and the US, with an emerging markets sleeve added as a fifth regional component. Each trade is oriented in the direction of theoretically or historically positive carry, and the approach uses no signals, forecasts, or attempts to time or exploit security-specific mispricing.

The construction proceeds in stages. Individual sub-trades across a range of maturities are combined into a sub-strategy portfolio for each trade, using volatility targeting and equal weights, then sub-strategy portfolios are combined into regional portfolios, and regional portfolios are combined into a single global portfolio. Leverage is controlled as an integral part of the process. Raw market data for swaps, government bonds, swaptions, futures, mortgage-backed securities, inflation-indexed bonds, an emerging market bond index, and repurchase agreements are sourced from established third-party providers, including Bloomberg, Datastream, and JP Morgan. The historical simulation runs from January 1996 to December 2009, rebalanced weekly, and is reported net of transaction costs and financing costs.