Private credit and public credit compared: returns, risks and what the research shows

In depth12 min read · 14 sources

Private credit (also called private debt) consists of loans that funds and other non-banks make directly to companies, without an exchange and usually without a bank. The market has grown strongly since the financial crisis and is now also offered to retail investors, for example through ELTIFs. This article compares private credit with exchange-traded corporate bonds and loans (public credit), shows with worked numbers how smoothed valuations make risk look smaller, and summarises what research knows about the returns.

What private credit is

According to Cai and Haque (2024), private credit or private debt is debt-like, non-publicly traded financing that non-banks such as private credit funds or business development companies (BDCs) provide to private companies. Because there is no liquid secondary market, lenders usually hold the loans until maturity or refinancing. Borrowers are typically middle-market companies with annual revenues between $10 million and $1 billion; almost all loans are floating-rate. The main strategies:

• Direct lending: senior secured loans, often for buyouts by private equity firms (sponsors), frequently as a unitranche, i.e. a single loan instead of a senior and a junior tranche. According to Cai and Haque (2024), about half of the market.
• Mezzanine: subordinated loans between the senior loan and equity, with higher interest and often a share in the company's value (equity kicker).
• Distressed and special situations: loans to companies in difficulty or in special situations such as restructurings; the return depends heavily on the recovery.
• Asset-based finance: financing that depends not on a company's cash flow but on assets such as receivables, leases, inventories or real estate.

Typical building blocks are a floating rate made up of a reference rate (e.g. Euribor or SOFR) plus a margin, upfront fees and covenants, i.e. conditions that give the lender an early say if the company's situation deteriorates. From their survey, Block et al. (2024) report that direct lending funds mainly provide cash-flow-based loans, negotiate financial and negative covenants, and that a private equity sponsor helps them lend more and design more effective covenants.

Why the market has grown since 2008

After the financial crisis, capital requirements for banks rose and the rules became more risk-sensitive; banks became more reluctant to lend to highly indebted middle-market companies. According to IMF (2024), private credit stepped into this gap and grew to around $2.1 trillion worldwide in 2023, including committed capital not yet called; in the US, the market is about as large as the markets for leveraged loans and high-yield bonds. Cai and Haque (2024) give a figure of just under $1.7 trillion of private credit for June 2023, comparable to leveraged loans (around 1.4 trillion) and high-yield bonds (around 1.3 trillion).

Research supports the bank regulation explanation. Chernenko et al. (2022) analysed loan agreements of US middle-market firms from 2010 to 2015: a third of the loans came directly from non-banks, and two thirds of these can be traced back to rules that make it harder for banks to lend to unprofitable and highly indebted firms. Firms with negative EBITDA or with debt above six times EBITDA were 32% and 15% more likely, respectively, to borrow from non-banks and paid considerably higher interest rates, especially after the revision of the US leveraged lending guidance in 2013.

Public credit and private credit compared

The terms investment grade and high yield refer to the credit rating. The floating rate makes leveraged loans and private credit almost insensitive to rising policy rates, but the credit risk remains; the spread duration is therefore considerably larger than the interest rate duration. For defaults and recovery rates in private credit, there are hardly any public, consistently defined statistics. Our assessment: default rates from managers' documents can only be compared to a limited extent with the rating agencies' statistics for bonds and leveraged loans, because restructurings and interest deferrals may be counted differently.

Returns and fees: what is left net

The return on a direct loan is made up of the reference rate, the margin and fees; expected credit losses and the fund's costs are deducted from this. A worked example with round, made-up figures, without leverage at fund level:

A net return of around 5% is not automatically better than a high-yield ETF with a similar return: the fund is less liquid, the loans are smaller and less transparent, and many funds borrow themselves. Block et al. (2024) report that the funds they surveyed use leverage, but considerably less than banks and CLOs. The floating rate protects against price losses from rising policy rates, but increases the borrowers' interest burden. According to Cai and Haque (2024), this is a risk in a downturn.

Smoothing: why private credit looks calmer than it is

Private loans have no exchange price. The fund's value comes from model valuations that reflect market movements only with a delay and in dampened form; according to IMF (2024), the values of private loans are adjusted less and more slowly than prices in public markets. A simple model of return smoothing and of unsmoothing following Geltner (1993):

Using hedge funds, Getmansky et al. (2004) showed that the strong autocorrelation of many returns most likely stems from illiquid assets: reported returns are smoother than the economic ones, volatility is underestimated, and risk-adjusted metrics such as the Sharpe ratio look too good. Asness et al. (2001) found that hedge funds carry considerably more market risk than simple regressions show once stale or estimated prices are taken into account. For correlation and diversification, this means: feeding smoothed returns into an optimisation overstates the benefit and understates volatility.

Access for retail investors: BDCs, ELTIFs and evergreen funds

• Listed BDCs: US companies that make middle-market loans and whose shares trade on the stock exchange. Their price fluctuates with the stock market and can deviate considerably from the book value of the loans. That is the opposite of smoothing.
• ELTIFs: European long-term investment funds, more easily accessible to retail investors since the reform of the ELTIF Regulation (Regulation (EU) 2023/606 2023). More in the article ELTIF.
• Evergreen and semi-liquid funds: funds without a fixed term that regularly issue and redeem units, but only to a limited extent. According to IMF (2024), redemptions are often restricted by gates, fixed redemption dates and suspension clauses; the growth of such funds could increase the advantage of those who exit first, and with it the risk of a run.
• Leveraged loan and CLO funds: not private credit in the narrower sense, but floating-rate corporate loans or their securitisations, with daily trading.

Pros and cons

What the research shows

• Munday et al. (2018) analysed 476 private credit funds, including 155 direct lending funds. Internal rates of return were positive in the top three quartiles of all strategies; in a comparison using public market equivalents, the funds performed about as well as or better than indices for leveraged loans, high yield and BDCs. Direct lending funds had a low beta, a positive alpha and a low correlation with these indices; the leveraged loan index was the best-fitting benchmark.
• Böni and Manigart (2022) found that private debt funds outperformed bond and equity benchmarks after fees in the cross-section, with wide dispersion between strategies and quartiles. A manager's earlier results predicted later ones, and conditions in the credit market strongly shaped returns. Managers were only partly able to time them.
• Erel et al. (2024) construct, for each fund, a comparison portfolio of equity and bond benchmarks from its cash flows. On this basis, a typical private debt fund earns no significant excess return after fees, but a positive one before fees. Anyone who compares only with bond indices also finds positive excess returns, because the funds also contain equity risk. So the loan interest is enough to cover fees and risk, but no more.
• Block et al. (2024) surveyed managers with private debt assets of over $390 billion. In their own assessment, they finance companies and leverage levels that banks would not finance, and they aim for unlevered returns that appear high relative to the risk.
• Performance is usually measured with the internal rate of return or via public market equivalents following Kaplan and Schoar (2005), not as a monthly time-weighted return. Volatility and correlation can only be derived from this with assumptions about smoothing.

Risks from the supervisors' perspective

In its April 2024 report, IMF (2024) highlights mainly these vulnerabilities:

• Valuation: without a secondary market, valuations are often stale; doubts about them could shake confidence.
• Hidden leverage: several layers of leverage (at companies, funds and their investors) could amplify losses, especially as reporting gaps conceal them.
• Interconnectedness and concentration: data gaps conceal how closely banks, insurers and pension funds are linked and how concentrated the investors are.
• Liquidity: as funds for retail investors grow, liquidity risk could increase.

The IMF recommends closer supervision of private credit funds, more cooperation between authorities across borders and sectors, closing the data gaps and close monitoring of liquidity and conduct risks in the funds. At the US Federal Reserve, Cai and Haque (2024) judge the risk of fire sales to be low because of long capital lock-ups and low leverage, but point to risks to lending standards from competition for borrowers and from links to banks. They also stress that the industry has not yet been through a long recession.

For advanced readers: estimating risk correctly

• Smoothing in general: Getmansky et al. (2004) let the reported return be a weighted average of the true returns of several periods, with weights θ_j that add up to 1; the measured variance then shrinks by the factor Σ θ_j². The simple model above corresponds to the weights θ_j = (1 − θ) × θ^j, and Σ θ_j² = (1 − θ) / (1 + θ).
• Beta with lags: instead of only the contemporaneous market return, you also include earlier market returns in the regression and add up the betas (Asness et al. 2001). In the model above, this gives the true beta again: 0.5 × 0.4 × (1 + 0.6 + 0.6² + …) = 0.5.
• Annualising: with autocorrelated returns, the usual scaling by √12 or √4 is biased (Lo 2002); positive autocorrelation makes the annual volatility look too small.
• Limits of unsmoothing: the correction amplifies measurement errors, and θ is itself estimated. Couts et al. (2024) show that funds with similar assets share a common source of smoothing that the usual methods do not fully remove: systematic risk remains underestimated, and the average alpha of illiquid funds is smaller than thought.

Limitations and common mistakes

• Mixing return measures: internal rates of return of funds and time-weighted returns of ETFs are not directly comparable, because the manager controls the timing of the cash flows (see time-weighted return).
• Sample: databases only contain funds that report (survivorship bias); the history since 2008 is short and largely fell into a period of low interest rates.
• Liquidity in a crisis: if you have to sell when many others want to sell, semi-liquid funds often return only part of your money, or you have to wait.
• Optimising smoothed figures: without unsmoothing, private credit receives too much weight in a mean-variance optimisation.
• Proxies are not the investment: listed BDCs fluctuate with the stock market and use leverage; they show the credit risk, but not the valuation approach of a closed-end fund.

How Portfolio Lab calculates it

Portfolio Lab has no time series for private credit itself, because illiquid segments are not available as a freely accessible monthly series. Instead, Asset classes offers exchange-traded proxies: Listed private credit (BDCs) via an ETF on US BDCs from 2013, Leveraged loans (senior loans) via funds from 2000, CLO AAA from 2020, as well as corporate and high-yield bonds in US dollars and euros, each converted into euros. Because these series come from exchange prices, they are not smoothed: volatility and correlation in the Backtest and in Optimisation tend to be higher than what private funds' reports show. The app does not offer unsmoothing of your own return series. Sources and chaining are described in the asset classes methodology.

This article explains a method; it is not investment or tax advice. Worked examples are simplified and rounded. All terms in the glossary.