Demystifying Hedge Funds: An Analysis of Convertible Arbitrage Trades and Alpha

December 2010

This paper examines convertible arbitrage and presents a systematic, trade-based way to capture the strategy’s core alpha. Convertible arbitrage buys convertible bonds and hedges the equity exposure by shorting the underlying stock, so that returns depend mainly on the bonds and their embedded options rather than on the direction of the equity market. Because those positions are dynamically delta-hedged, the returns tend to have low correlation with equities, which can offer meaningful diversification benefits. The analysis shows that much of the strategy return can be described, with meaningful accuracy, by a small set of economic drivers, principally positive gamma, the discount of convertible bonds to fair value, and carry. Because the approach is built on explicit decision rules, it is transparent by construction and does not suffer from style drift, 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 convertible arbitrage and other relative-value credit strategies, and it frames its conclusions as analytical perspective grounded in a long historical simulation.

What This Paper Examines

• How convertible arbitrage generates returns, and why delta-hedged positions tend to have low correlation with equities.
• Whether the strategy’s core return can be captured with a transparent, rules-based implementation built at the trade level.
• Which economic drivers, including gamma, the discount to fair value, and carry, account for most of the return.
• How the principal risks in the strategy, namely credit, volatility, and liquidity risk, interact during periods of market stress.
• Whether a transparent trade-based portfolio can represent the broader strategy without the style drift and opacity of discretionary approaches.

Key Findings

• Convertible arbitrage returns are largely hedged, security-level returns with low equity exposure. Dynamically delta-hedging convertible bonds with the underlying stock removes much of the equity direction. As a result, returns tend to have low correlation with equities, which is where the strategy’s diversification benefit comes from.
• Much of the core return can be captured with a transparent, rules-based implementation built at the trade level. Constructing the strategy from explicit position-level rules helps demystify it. It also supports the process and portfolio transparency that institutional investors expect, in contrast to more opaque, discretionary approaches.
• A small set of economic drivers accounts for most of the return. Positive gamma, the discount of convertible bonds to fair value, and carry tend to be the dominant contributors. Building the portfolio at the trade level makes it possible to decompose returns into these components rather than attributing them to security selection or market timing.
• Credit, volatility, and liquidity risk are the principal risks, and they tend to move together in stress. Widening credit spreads generally detract from returns during crises, while rising equity volatility tends to lift gamma trading gains, which can cushion some of that loss. Reduced liquidity and forced deleveraging can widen discounts to fair value and prolong drawdowns.
• Transparency and disciplined risk control are structural features of a trade-based implementation. Full visibility into positions, the absence of style drift, and controlled leverage help contain the concentration and leverage risks that the 2008 experience showed can amplify losses.

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 Edward Nakon and Dimitri Paliouras, 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 compiles a dataset of more than eight thousand global convertible bonds issued from 1990 to 2009. The investable universe is restricted to North America and Europe and filtered for size and remaining maturity, so that roughly seven hundred bonds are outstanding at a point in time. Bond, stock, credit, and volatility data are sourced from established third-party providers, including Monis, Bloomberg, Thomson Reuters QAI, IDC, Mergent, and Markit, with composite price series built to limit stale prices and outliers.

The method is systematic and rules-based. Simulated portfolios hold approximately one hundred fifty of the largest and most liquid convertible bonds, selected to represent the broader universe across premium to parity and credit spread, and weighted toward equity-sensitive bonds and the most attractively priced issues. Fair values and hedge ratios come from an option valuation model, and the equity exposure is hedged dynamically with the underlying stock, using controlled leverage consistent with industry practice. Returns are decomposed at the trade level into components including delta, discount to fair value, gamma, vega, carry, theta, credit spread, and interest rate sensitivity. The historical simulation runs from 1992 to 2009.