Demystifying Hedge Funds: An Analysis of Distressed Debt Trades and Alpha

December 2010

This paper examines distressed debt investing and presents a systematic, trade-based way to capture the return of a core distressed strategy: buying the debt of companies that have defaulted or filed for bankruptcy protection. Traditional holders are often forced to sell below-investment-grade or defaulted securities, which can push prices well below fair value and create an opportunity for investors able to hold through a restructuring. The upside comes as the debt appreciates toward or above par, or converts to equity. The analysis builds a long-only simulated portfolio of defaulted bonds and loans and studies how it behaves across market cycles. Because the return depends mainly on company-specific restructuring rather than broad market direction, performance tends to be more idiosyncratic than equities or high yield, which can offer diversification benefits. Because the portfolio is founded on explicit rules, it is transparent by construction, avoids style drift, and can be managed with controlled risk and leverage. The paper is written for institutional allocators, OCIOs, and consultants evaluating distressed and event-driven credit, and it frames its conclusions as analytical perspective grounded in a long historical simulation.

What This Paper Examines

• How distressed debt generates returns, and why performance tends to be more idiosyncratic than equities or high yield.
• The structural sources of return, including forced selling by constrained holders and the upside from a successful restructuring.
• How the opportunity set varies with the business cycle and with corporate default rates.
• Whether a transparent, rules-based portfolio of defaulted debt can capture the distressed strategy.
• How reported hedge fund performance and index proxies compare, and the biases an allocator should weigh.

Key Findings

• Distressed debt returns are driven mainly by corporate restructuring, not by broad market direction. Credit market conditions affect the prices of defaulted securities, but the primary driver of return is the company-specific restructuring process. As a result, performance tends to be more idiosyncratic than equities or high yield, which is where the diversification benefit comes from.
• The opportunity set is cyclical, and returns tend to be strongest after default rates have peaked. The supply of defaulted debt expands during recessions as more companies fall into distress. The evidence suggests investors should consider an allocation particularly when default rates have peaked and the opportunity set is large.
• Two structural forces support the return. Forced selling by constrained holders, such as insurers, pension funds, and mandate-limited vehicles, can depress prices below fair value at the time of default. Debt bought at a deep discount then behaves like a call option on the company’s recovery, offering upside when a restructuring succeeds.
• A transparent, rules-based portfolio can capture much of the strategy while avoiding style drift. Building the exposure from explicit rules helps demystify the strategy, eliminates style drift, and lets an investor control risk and leverage. It also allows independent valuation, in contrast to distressed hedge fund holdings, which tend to be opaque and harder to value.
• Reported hedge fund performance and broad credit indices are imperfect lenses on the strategy. Indices built from reported hedge fund returns can carry survivorship bias and selective reporting, for example through side pockets. High yield proxies corporate credit conditions but is not a substitute for the restructuring-driven return that defines distressed investing.

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 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 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.